research article investigation of antileishmanial activities of … · 2019. 7. 30. · research...

Research ArticleInvestigation of Antileishmanial Activities ofAcridines Derivatives against Promastigotes and AmastigotesForm of Parasites Using Quantitative Structure ActivityRelationship Analysis

Samir Chtita1 Mounir Ghamali1 Rachid Hmamouchi1 Bouhya Elidrissi1

Mohamed Bourass2 Majdouline Larif3 Mohammed Bouachrine4 and Tahar Lakhlifi1

1MCNSL Faculty of Science Moulay Ismail University Meknes Morocco2Faculty of Sciences Sidi Mohamed Ben Abdellah University Fez Morocco3Separation Process Laboratory Faculty of Science Ibn Tofail University Kenitra Morocco4High School of Technology Moulay Ismail University Meknes Morocco

Correspondence should be addressed to Samir Chtita samirchtitagmailcom

Received 13 August 2016 Revised 22 September 2016 Accepted 27 September 2016

Academic Editor Michael D Sevilla

Copyright copy 2016 Samir Chtita et al This is an open access article distributed under the Creative Commons Attribution Licensewhich permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited

In a search of newer and potent antileishmanial (against promastigotes and amastigotes form of parasites) drug a series of 60variously substituted acridines derivatives were subjected to a quantitative structure activity relationship (QSAR) analysis forstudying interpreting and predicting activities and designing new compounds by using multiple linear regression and artificialneural network (ANN) methods The used descriptors were computed with Gaussian 03 ACDChemSketch Marvin Sketch andChemOffice programs The QSAR models developed were validated according to the principles set up by the Organisation forEconomic Co-operation and Development (OECD) The principal component analysis (PCA) has been used to select descriptorsthat show a high correlation with activities The univariate partitioning (UP) method was used to divide the dataset into trainingand test sets The multiple linear regression (MLR) method showed a correlation coefficient of 0850 and 0814 for antileishmanialactivities against promastigotes and amastigotes forms of parasites respectively Internal and external validations were used todetermine the statistical quality of QSAR of the two MLR models The artificial neural network (ANN) method considering therelevant descriptors obtained from the MLR showed a correlation coefficient of 0933 and 0918 with 7-3-1 and 6-3-1 ANN modelsarchitecture for antileishmanial activities against promastigotes and amastigotes forms of parasites respectively The applicabilitydomain of MLR models was investigated using simple and leverage approaches to detect outliers and outsides compounds Theeffects of different descriptors in the activities were described and used to study and design new compounds with higher activitiescompared to the existing ones

1 Introduction

For hundreds of years Leishmaniasis a disease caused bya number of species of protozoan parasites belonging tothe genus Leishmania is recognized as an important publichealth problem throughout the world [1ndash3] Currently theleishmaniases are considered to be endemic in 88 countriesand according to World Health Organization (WHO) [4]twelve million people are infected with about two to three

million new cases each year and 350million people are underrisk of infection it is a major public health problem partic-ularly in Latin America Africa and Asia [5ndash9] To date novaccine against any clinical form of Leishmaniasis has beencommercialized and treatment relies only on chemotherapywhich has been based on the use of pentavalent antimo-nial drugs Other medications such as pentamidine andamphotericin B have been used as alternative drugs forresistant parasites With the emergence of some resistant

Hindawi Publishing CorporationAdvances in Physical ChemistryVolume 2016 Article ID 5137289 16 pageshttpdxdoiorg10115520165137289

2 Advances in Physical Chemistry

strains the toxicity of current drugs severe side effects andhigh cost andor restricted therapeutic spectrum a need fordevelopment of new and safer drugs is warranted [2 3 10]A great number of natural and synthetic compounds havebeen tested in the past years in antileishmanial assays Theirstructures are diverse and often contain nitrogen heterocyclessuch as quinolines pyrimidines acridines phenothiazinesand indoles [11ndash13]

Many experiments have been performed with the com-pounds bearing the heterocyclic ring structures to exploretheir effectiveness against Leishmania These studies sug-gested their similar pharmacophoric feature of the hetero-cyclic scaffold as a potential target for drug discovery ofantileishmanial drugs [14]

Leishmania parasites exist in two forms one is promastig-otes and the other is amastigotes The promastigotes areflagellated and found in sand fly while the amastigotes areovoid and nonflagellated form of Leishmania [15] Antileish-manial activity is performed against promastigotes and thenamastigotes forms of parasites Heterocyclic system may alsobe formed by fusion with other rings either carbocyclic orheterocyclic

Since their discovery in the 1880s acridines family havedemonstrated a broad spectrum of pharmacological prop-erties [16] First employed as antibacterial agents duringthe beginning of the twentieth century [17] They havebeen rapidly revealing interesting antiproliferative activitiesagainst both protozoa and tumor cells [18 19] Consequentlythey have been extensively used in antiparasitic chemother-apy and a wide range of new acridines derivatives have beensynthesized and successfully assessed for their antileishma-nial activities [20 21]

In order to open a new way in antileishmanial drugresearch a series of sixty acridines derivatives were synthe-sized [22ndash24] and studied for their antileishmanial (againstpromastigotes and amastigotes form of parasites) activitiesThe aim of this study was to develop a QSAR model able tocorrelate the structural features of the acridines derivativeswith their biological activities

In general the QSAR methods are based on the assump-tion that the activity of a certain chemical compound relatedto its structure through a certain mathematical algorithmThis relationship can be used in the prediction interpre-tation and assessment of new compounds with desiredactivities reducing and rationalizing time efforts and cost ofsynthesis and new product development

The basic assumption to drive a QSARmodel is presenteddue to a mathematical function of the chemical propertieswhich is related to the effect (activity) Therefore the effectis like the function ldquo119891rdquo of the chemical properties ldquo119909rdquo119910 = 119891(119909) To find this algorithm we use a number ofchemical compounds with known values of the studied effect(119910) For each chemical compound we calculate a series ofparameters (called chemical descriptors) Then we find analgorithm that provides a quite accurate value similar tothe real experimental value The final step is to check if theobtained algorithm is able to predict the activity values forother chemicals not used to build up the model (externalvalidation)

Indeed it is very important to generate a model whichworked not only for the chemical substances used withinthe training set but also for other similar chemicals Con-sequently the challenge is to define the correct statisticalproperties of the model

2 Materials and Methods

The current QSAR study investigates prediction and inter-pretation of the studied compounds and was also used fordesigning new proposed compounds by using linear andnonlinear methods It consists of four stages selection ofdataset and generation of molecular descriptors descriptiveanalysis statistical analysis (prediction and evaluation) andsuggestion of novel compounds

A flow chart for the development of the QSAR modelalong with the various validation methods used in this workis demonstrated in Figure 1

21 Selection of Dataset and Generation ofMolecular Descriptors

211 Selection of Dataset In this stage the datasets ofthe antileishmanial activities (against promastigotes andamastigotes forms of parasites) of various acridine derivatives(4-monosubstituted acridines 36-disubstituted acridines45-disubstituted acridines and 7-monosubstituted 9-chloroand 9-amino-2-methoxy acridines) were collected from pre-vious works [22ndash24] The molecular structures of the studiedmolecules with their antileishmanial activities are presentedin Table 1 All experimental IC50 antileishmanial activityvalues (120583M) were converted to the negative logarithm ofIC50 (pIC50 = minuslog

10(IC50))

212 Molecular Descriptors Generation A wide variety ofmolecular descriptors was calculated using Gaussian 03ACDChemSketch Marvin Sketch and ChemOffice pro-grams [25ndash28] to predict the correlation between thesedescriptors for the studied molecules with their antileishma-nial activities and to develop linear (multiple linear regression(MLR)) and nonlinear (artificial neural network (ANN))models Tables 3 and 4 show the selected descriptors (usingthe PCA method see more in descriptive analysis results) tobe used in this study

22 Descriptive Analysis In this stage the principal compo-nent analysis (PCA) was used to determine the nonlinearityand nonmulticollinearity among variables (descriptors) andto select descriptors that correlate with the activity Afterthat the univariate partitioning (UP) method was used toform dissimilar clusters of compounds to which the querycompounds would be compared for determining the degreeof similarity and dividing the dataset into training and testsets

23 Statistical Analysis (Models Development and Evaluation)In this stage linear and nonlinear QSAR models weredeveloped and evaluated to predict the activities of the testcompoundsThe study we conducted consists of the multiple

Advances in Physical Chemistry 3

N

1

Training set (promastigotes) N NHA NHD MTI

1 14330 4752 5141 3 2 82052 13080 4353 5023 3 2 108354 10070 4124 4469 3 2 255717 8800 0805 4167 5 2 355298 7820 0141 3989 7 2 45907

62 8310 3311 4511 5 2 39911

Obs25332204063406811207

1797Molecular descriptors

minus17minus12minus07minus02

0308131823

minus2 minus1 0 1 2

TrainingValidationTest

23

4

Activity = f (descriptors)

Preparation of the dataset

Calculation of molecular descriptors

Activity

Statistical analysis (QSAR models)

Internal and external validation of QSAR

models

Novel compounds with suggested

activities

N∘ 120583 120596 middot middot middotmiddot middot middotmiddot middot middotmiddot middot middotmiddot middot middotmiddot middot middotmiddot middot middotmiddot middot middotmiddot middot middotmiddot middot middot

middot middot middotmiddot middot middotmiddot middot middotmiddot middot middot






middot middot middotmiddot middot middotmiddot middot middotmiddot middot middotmiddot middot middot

middot middot middotmiddot middot middotmiddot middot middot

middot middot middot

pIC50

R5

R6

R7

R4

R1

R2

R3

Pred (pIC50 AMA)pIC50 AMA

Pred (pIC50 AMA)

pIC

50

AM

A

Figure 1 Flow chart of the methodology used in this work

linear regression (MLR) available in the XLSTAT softwareand the artificial neural network (ANN) available in theMatlab software

In order to propose models and to evaluate quanti-tatively the physicochemical effects of the substituents onthe activities of molecules we submitted the data matrixconstituted obviously from the used variables (descriptors)corresponding to the dataset molecules to a descendant MLRanalysis and to an ANN We use the coefficients 119877 1198772 1198772adjMSE and 119875 value to select the best regression performance[29] where119877 is the correlation coefficient1198772 is the coefficientof determination 1198772adj is the coefficient adjusted for degreesof freedom Mean squared error (MSE) is the standard errorof the coefficient of each descriptor and of the global modelwhich gives an indication of the valid inclusion of a descriptorin a QSAR model 119875 value is the probability (119875) of Fisherstatistics (119865) which gives an indication of the probability thata QSAR is a chance correlation

In order to assess the significance of the models andaccurate prediction ability for new compounds

(i) we use an internal validation procedure (leave-one-out cross validation) whereby one compound isremoved and the rebuilt model with the remainingmolecules is used to predict the response of theeliminated compound This one is then returned anda second is removed and the cycle is repeated and

so on until all compounds have been removed oneby one and an overall correlation coefficient 119877cv iscomputed

(ii) after the model is built an external prediction isnecessary In this one the obtained model was usedto predict the activities of a test set comprisingcompounds that are similar to those not used in thetraining set This is usually performed by splitting adataset into a training set and a test set typically ina 15 ratio Further before performing the externalvalidation of a model it is very important to check forthe presence of systematic error that violates the basicassumptions of the least squares regression model Ifhigh systematic error (bias) is present in the modelthen suchmodel should be discarded and performingany external validation test is of no use on such biasedmodel Xternal Validation Plus is a tool that checksthe presence of systematic errors in the model andfurther computes all the required external validationparameters while judging the performance of actualprediction quality of a QSARmodel based on recentlyproposed MAE-based criteria [30]

(iii) a model is valid only within its training domainand new molecules must be considered as belongingto the domain before the model is applied (OECDPrinciple 3 [31]) Without applicability domain (AD)


Table1Ch

emicalstructurea

ndantileishmanialactivities

ofstu

died

compo

unds

(see

Figure

8)

Num

ber

R 1R 2

R 3R 4

R 5R 6

R 7pIC 50

b

PRO

cAMA

d

1H

NHCO

CH3

HH

HNHCO

CH3

Hminus2

533

minus0653

2H

NHCO

C 2H5

HH

HNHCO

C 2H5

Hminus2

204

minus0886

3H

NHCO

C 3H7

HH

HNHCO

C 3H7

Hminus1669

minus110

74

HNHCO

phH

HH

NHCO

PhH

minus0634

minus0041

5H

NHCO

-p-PhC

lH

HH

NHCO

-p-PhC

lH

mdash0699

6H

NHCO

-p-PhF

HH

HNHCO

-p-PhF

Hminus0

230

1523

7H

NHCO

-p-PhO

Me

HH

HNHCO

-p-PhO

Me

Hminus0

681

minus0041

8H

NHCO

-mp-Ph(OMe)2

HH

HNHCO

-mp-Ph(OMe)2

Hminus1207

0097

9H

NHCO

Me

HH

HNHCO

phH

minus2270

Toxa

10H

NHCO

Me

HH

HNHCO

-p-PhC

lH

minus1061

minus0462

11H

NHCO

Me

HH

HNHCO

-p-PhF

Hminus2

123

0174

12H

NHCO

Me

HH

HNHCO

-p-PhO

Me

Hminus1939

Toxa

13H

NHCO

Me

HH

HNHCO

-mp-Ph(OMe)2

Hmdash

minus0114

14H

HCH3

NH2

OMe

HH

0301

0398

15H

HCH2OH

NH2

OMe

HH

minus0732

minus0613

16H

HCH2Br

NH2

OMe

HH

minus0556

minus0663

17H

H(C

H2) 2OCO

OMe

NH2

OMe

HH

minus0380

minus0114

18H

H(C

H2) 2OCO

(CH2) 2CH3

NH2

OMe

HH

minus0491

minus039

819

HH

(CH2) 2OCO

CH2CH

(CH3) 2

NH2

OMe

HH

minus0342

Toxa

20H

H(C

H2) 2OCO

PhNH2

OMe

HH

0398

0699

21H

H(C

H2) 2OCO

PhF

NH2

OMe

HH

minus0114

0222

23H

H(C

H2) 2OCO

PhOMe

NH2

OMe

HH

minus0279

Toxa

24H

HCH3

ClOMe

HH

minus0041

minus0362

25H

HCH2OH

ClOMe

HH

minus2262

minus0959

26H

HCH2Br

ClOMe

HH

minus1703

minus117

027

HH

(CH2) 2OCO

OMe

ClOMe

HH

minus217

8minus1877

28H

H(C

H2) 2OCO

(CH2) 2CH3

ClOMe

HH

minus1707

minus1628

29H

H(C

H2) 2OCO

CH2CH

(CH3) 2

ClOMe

HH

minus1446

minus1561

30H

H(C

H2) 2OCO

PhCl

OMe

HH

minus213

5To

xa31

HH

(CH2) 2OCO

PhF

ClOMe

HH

minus2295

minus1645

32H

H(C

H2) 2OCO

PhCl

ClOMe

HH

minus219

4minus2

191


Table1Con

tinued

Num

ber

R 1R 2

R 3R 4

R 5R 6

R 7pIC 50

b

PRO

cAMA

d

33H

H(C

H2) 2OCO

PhOMe

ClOMe

HH

minus2098

minus2088

34CH2NH2

HH

HH

HH

minus0230

minus053

135

CH2OH

HH

HH

HH

Toxa

minus1515

36CH2NHCO

(CH2) 3Cl

HH

HH

HH

minus1655

minus1427

37CH2OCO

(CH2) 3Cl

HH

HH

HH

minus2200

minus218

538

CH2NHCO

CH=C

H2

HH

HH

HH

minus2245

minus0833

39CH2OCO

CH=C

H2

HH

HH

HH

minus1464

mdash40

CH2NHCO

PhH

HH

HH

Hminus1654

minus112

141

CH2OCO

PhH

HH

HH

Hminus1885

Toxa

42CH2NHCO

-p-PhF

HH

HH

HH

minus1815

mdash43

CH2OCO

-p-PhF

HH

HH

HH

minus174

1To

xa44

CH2NHCO

-p-PhC

lH

HH

HH

HTo

xaminus1004

45CH2OCO

-p-PhC

lH

HH

HH

Hminus1790

minus1433

46CH2NHCO

-p-PhO

Me

HH

HH

HH

minus1513

minus0973

47CH2OCO

-p-PhO

Me

HH

HH

HH

minus1471

minus0869

48CH2NHCO

-p-PhN

Me 2

HH

HH

HH

minus153

9minus0

672

49CH2OCO

-p-PhN

Me 2

HH

HH

HH

minus1819

Toxa

50CH2NH2

HH

HH

HCH2NH2

minus0820

minus053

151

CH2OH

HH

HH

HCH2OH

Toxa

0222

52CH2NHCO

(CH2) 3Cl

HH

HH

HCH2NHCO

(CH2) 3Cl

minus0663

minus0813

54CH2NHCO

CH=C

H2

HH

HH

HCH2NHCO

CHCH2

minus1061

Toxa

56CH2NHCO

PhH

HH

HH

CH2NHCO

Phminus0

556

minus1236

57CH2OCO

PhH

HH

HH

CH2OCO

PhTo

xaminus0

716

58CH2NHCO

-p-PhF

HH

HH

HCH2NHCO

-p-PhF

minus0756

minus0255

60CH2NHCO

-p-PhC

lH

HH

HH

CH2NHCO

-p-PhC

lmdash

minus1562

62CH2NHCO

-p-PhO

Me

HH

HH

HCH2NHCO

-p-PhO

Me

minus1797

Toxa

63CH2OCO

-p-PhO

Me

HH

HH

HCH2OCO

-p-PhO

Me

Toxa

minus1825

64CH2NHCO

-p-PhN

Me 2

HH

HH

HCH2NHCO

-p-PhN

Me 2

minus0940

minus0724

65CH2OCO

-p-PhN

Me 2

HH

HH

HCH2OCO

-p-PhN

Me 2

Toxa

minus1667

a Toxictoxicity

observed

onhu

man

macroph

agesatconcentrations

thatdidno

tdisp

layantileishmanialactivity

-p-para-m

-meta

b pIC50=minuslog(

IC50)

c pIC50AMAantileish

manialactivity

againstamastig

otes

parasiteformdpIC 50PR

Oantileish

manialactivity

againstp

romastig

otes

parasiteform


Inputw

b

w

b

+ +

Hidden layer Output layer

Output

Figure 2 The architecture used in our study of the artificial neural network

each model can predict the activity of any com-pound even with a completely different structurefrom those included in the study Therefore the ADis a tool to find out compounds that are outsidethe applicability domain of the built QSAR modeland it detects outliers present in the training setcompounds There are several methods for definingthe applicability domain (AD) of QSAR models [32]but themost common one is determining the leveragevalues ℎ119894 (ℎ119894 = 119909119879

119894(119883119879 119883)

minus1

119909119894 (119894 = 1 2 119899)) foreach compound where119909119894 is the descriptor row-vectorof query compound 119883 is 119899 119909 119896 minus 1 matrix of 119896model descriptor values for 119899 training set compoundsand the superscript 119879 refers to the transpose ofmatrixvector [32 33] In this study we use Williamsplot in this plot the applicability domain is estab-lished inside a squared area within standard deviationplusmn119909 (in this study 119909 = 3 ldquothree-sigma rulerdquo [34]) anda leverage threshold ℎlowast(ℎlowast = 3 lowast (119896 + 1)119899) [35]where 119899 is the number of training set compounds 119896is the number of model descriptors The leverage (ℎ)greater than the warning leverage (ℎlowast) suggested thatthe compoundwas very influential on themodel [36]The results of the leverage approach were comparedwith that of the simple approach introduced by Royet al [37]

231 Multiple Linear Regression (MLR) The descendentmultiple linear regression (MLR) analysis based on theelimination of aberrant descriptors (one by one) until a validmodel (including the critical probability 119875 value lt 005 forall descriptors and the model complete) was employed tofind a linear model of the activity of interest which takes thefollowing form

119884 = 1198860 +119899

sum119894=1

119886119894119909119894 (1)

where 119884 is the studied activity which is the dependent vari-able 1198860 is the intercept of the equation 119909119894 are the moleculardescriptors 119886119894 are the coefficients of those descriptors

This method is one of the most popular methods ofQSARQSPR thanks to its simplicity in operation repro-ducibility and ability to allow easy interpretation of thefeatures used The important advantage of the linear regres-sion analysis is that it is highly transparent therefore thealgorithm is available and predictions can bemade easily [38]It has served also to select the descriptors used as the inputparameters in the artificial neural network (ANN)

Table 2 Software packages used in this work

Drawing chemicalstructures

Marvin Sketch ACDChemSketchand ChemBioDraw

Generating 3D structures Gauss View 30 and ChemBio3DCalculating chemicaldescriptors

Gaussian 03 Marvin Sketch 62ChemSketch and ChemBio3D

Developing QSAR models XLSTAT 2009 and Matlab 790(version 2011)

232 Artificial Neural Networks (ANN) The artificial neuralnetworks (ANN) are used in order to increase the probabilityof characterizing the compounds and to generate a predic-tive QSAR model between the set of molecular descriptorsobtained from the MLR models and the observed activitiesvalues The ANN model is done on the MATLAB R2009bsoftware It consists of three layers of neurons called inputlayer hidden layer and output layer (Figure 2) The inputlayer formed by a number of neurons equal to the numberof descriptors obtained in the multiple linear regressionmodels and the output layer represents the calculated activityvalues For determination of the number of hidden neuronsin the hidden layer where all calculations of parameteroptimization of neural networks are made a parameter 120588has been proposed The parameter 120588 plays a major role indetermining the best artificial neural network architecture[39 40] 120588 is defined as follows

120588 =number of data points in the training set

sum of the number of connections in the ANN (2)

In order to avoid overfitting or underfitting it is recom-mended that the value of 120588 should be between 100 and 300if 120588 lt 1 the network simply memorizes the data whereas if120588 gt 3 the network is not able to generalize [41]

24 Software Packages Used in Our QSARDevelopment StudyThere are various free and commercial software available forQSAR development These include specialized software fordrawing chemical structures generating 3D structures cal-culating chemical descriptors and developing QSARmodelsThe software packages used in our works are represented inTable 2

3 Results and Discussions

31 Principal Component Analysis (PCA) PCA is a usefulstatistical technique for summarizing all the informationencoded in the structures of the compounds It is very helpful


Table 3The absolute values of correlation coefficients with descrip-tors and activitiesDescriptors 119903Promastigotes

Henryrsquos law constant (119870H) 0130Ideal gas thermal capacity (IGTC) 0108Number of H-Bond acceptors (NHA) 0299Number of H-Bond donors (NHD) 0250Balaban index (119869) 0133Shape coefficient (119868) 0135Total valence connectivity (TVC) 0292N 0238H 0260Gibbs free energy (119866) 0119Dipole moment (120583) 0213Electronegativity (120594) 0599Electrophilicity index (120596) 0630Molecular topological index (MTI) 0116Polar surface area (PSA) 0282Total connectivity (TC) 0269Wiener index (119882) 0124119864HOMO 0554119864LUMO 0632119864Gap 0227

AmastigotesTotal energy 119864 0160119864HOMO 0286Electronegativity (120594) 0316Polar surface area (PSA) 0391O 0105Index of refraction (119899) 0548Density (119889) 0242Critical pressure (CP) 0176Gibbs free energy (119866) 0195Henryrsquos law constant (119870H) 0387Number of H-Bond acceptors (NHA) 0198Number of H-Bond donors (NHD) 0587Number of rotatable bonds (NRB) 0139Sum of valence degrees (SVD) 0146Dipole moment (120583) 0224119864LUMO 0338Electrophilicity index (120596) 0335N 0436C 0155Surface tension (120574) 0553Boiling point (TB) 0254Critical temperature (CT) 0276Heat of formation (119867∘) 0152log119875 0248Melting point (119879) 0314Partition coefficient (PC) 0163Shape coefficient (119868) 0176Winner index (119882) 0103

for understanding the distribution of the compounds Thisis an essentially descriptive statistical method which aimsto extract the maximum of information contained in thedataset compounds [42 43] In this work the PCA is usedto overview the examined compounds for similarities anddissimilarities and to select descriptors that show a high

Table 4 Descriptors selected by the principal component analysis(PCA) and software packages used in the calculation of descriptors

Software Descriptors

Gaussian 03

Highest occupied molecular orbital energy119864HOMO (eV) lowest unoccupied molecularorbital energy 119864LUMO (eV) hardness 120578 (eV)= (119864LUMO minus 119864HOMO)2 electronegativity 120594(eV) = minus(119864LUMO + 119864HOMO)2 electrophilicityindex 120596 (eV) = 12059422120578 total energy 119864 (eV)dipole moment 120583 (Debye) energy gapbetween 119864HOMO and 119864LUMO values 119864Gap (eV)

ChemOffice

Heat of formation119867∘ (kJmolminus1) Gibbs freeenergy 119866 (kJmolminus1) ideal gas thermalcapacity (IGTC) (Jmolminus1 Kminus1) melting point119879 (Kelvin) critical temperature (CT)(Kelvin) boiling point (TB) (Kelvin) criticalpressure (CP) (Bar) Henryrsquos law constant119870H total valence connectivity (TVC)partition coefficient (PC) moleculartopological index (MTI) number ofrotatable bonds (NRB) shape coefficient 119868sum of valence degrees (SVD) totalconnectivity (TC)

ChemSketchPercent ratios of nitrogen hydrogenoxygen and carbon atoms (N H Oand C) surface tension 120574 (dynecm) indexof refraction (119899) density (119889)

Marvin Sketchlog119875 Winner index (119882) number ofH-Bond acceptors (NHA) number ofH-Bond donors (NHD) Balaban index (119869)polar surface area (PSA) (119860∘)2

correlation with the response activity this one gives extraweight because it will be more effective at prediction [44]Tables 3 and 4 present the descriptors with a correlationcoefficient with the activity higher in absolute value than01 The absence of any serious multicollinearity betweenthe descriptors present in the model was confirmed by thecorrelation matrix

32 Univariate Partitioning (UP) The aim of the UP was therecognition of groups of objects based on their similarity thismethod is based on the criterion of partitioning proposedby Fisher [45] In this study the division of the dataset intotraining and test sets has been performed In this one fromeach obtained cluster one compound for the training setwas selected randomly to be used as test set compound Thepartitioning results are given in Table 5

33 Multiple Linear Regression (MLR) The QSAR analysiswas performed using the values of the chemical descriptorsselected by the PCA method and on the other hand theexperimental values of the antileishmanial activities for 60 ofthe acridines derivatives (effect) Table S1 in SupplementaryMaterial available online at httpdxdoiorg10115520165137289 shows the value of each molecular descriptor that isconfigured in established MLR models and the QSAR model


Table 5 The partitioning results of compounds into training and test sets

ClassesThe test set compounds

Promastigotes Amastigotes3 6 10 14 17 21 37 38 42 64 5 6 26 27 29 36 37 44 46 50

1 1 8 37 40 56 1 10 15 16 34 48 50 57 642 2 14 29 34 48 2 25 38 44 47 51 523 3 18 19 3 26 40 564 4 24 25 27 30 42 58 4 7 8 11 13 17 21 465 6 26 5 14 206 7 9 12 21 33 39 41 47 67 10 11 16 31 32 43 45 18 29 588 15 17 23 28 36 49 52 54 27 639 20 38 46 62 24 28 31 35 36 45 60 6510 50 64 32 33 37

built is represented by the following equations and the valuesof the statistical parameters

MLRModel for pIC50 Promastigotes

pIC50(PRO) = 3030 minus 8214 10minus02N + 0239120583

minus 1264120596 minus 0233NHA + 0732NHD

+ 3311 10minus05MTI + 3184 104TVC

(3)

Statistical Parameters 1198772 = 0723 119877 = 0850 1198772adj = 0664MSE = 0189 P value lt 10minus4

MLRModel for pIC50 Amastigotes

pIC50(AMA) = minus7314 + 1191119864HOMO

+ 2543 10minus02CT minus 0350119870H

minus 1632 10minus02119879 + 0652NHA

+ 1262NHD

(4)

Statistical Parameters 1198772 = 0663 119877 = 0814 1198772adj = 0598MSE = 0208 119875 value lt 10minus4

(i) For the two models P value is lower than 00001it means that we would be taking a lower than001 risk in assuming that the null hypothesis (noeffect of the explanatory variables) is wrong and thatthe regressions equations have statistical significanceTherefore we can conclude with confidence that theselected variables do bring a significant amount ofinformation

(ii) Higher value of 1198772 and 1198772adj and lower mean squarederror (MSE) indicate that the two proposed modelsare predictive and reliable

(iii) Our obtained models were validated internally by theleave-one-out cross validation technique the crossvalidation coefficient 1198772cv for the two models was

determined based on the predictive ability of themodel The value of 1198772cv is higher than 05 (1198772cv =0536 for the MLR pIC

50(PRO) model and 1198772cv =

0525 for the MLR pIC50

(AMA) model) indicatinggood predictability of the model

(iv) True predictive power of these models is to test theirability to predict perfectly pIC

50of compounds from

an external test set (compounds that were not usedfor the developed model) pIC

50of the remaining set

of 10 compounds are deduced from the quantitativemodels proposed with the compounds used in train-ing set by MLR These models will be able to predictthe activities of test set molecules in agreement withthe experimentally determined value The observedand calculated pIC

50values are given in Table 6

The predictive capacity of the models was judgedthe higher value of 1198772test (1198772test = 0660 for theMLR pIC

50(PRO) model and 1198772test = 0718 for the

MLR pIC50

(AMA) model) indicates the improvedpredictability of the model

(v) Xternal Validation Plus indicates the absence of sys-tematic errors in the model and a moderate perfor-mance of prediction quality of a QSAR model basedon proposed MAE-based criteria (Table S2)

(vi) The values of calculated activities from (3) and (4) aregiven in Table S1 and the correlations of calculatedand observed activities values are illustrated in Fig-ure 3

In the first model (pIC50

PRO) the descriptors influencingnegatively the activities are the percent ratio of nitrogen(N) the number of H-Bond acceptors (NHA) and theelectrophilicity index (120596) and the parameters influencingpositively the activities are the dipole moment (120583) thenumber ofH-Bonddonors (NHD) themolecular topologicalindex (MTI) and the total valence connectivity (TVC)

(i) The number of H-Bond acceptors (NHA) has a nega-tive sign in the model which suggests that increasedactivity can be achieved by decreasing the number


Table 6 The values of chemical descriptors and the observed and predicted activities using the MLR models for the test set

Test set (promastigotes) pIC50PRO

Number N 120583 120596 NHA NHD MTI TVC Obs MLR3 12030 4259 5024 3 2 14075 3891 10minus07 minus1669 minus20476 9270 5640 4654 5 2 28763 1716 10minus10 minus0230 minus101410 10780 5730 5267 3 2 16459 4902 10minus08 minus1061 minus183214 11760 2698 3062 3 1 4387 3050 10minus05 0301 minus001217 8580 4969 3431 4 1 10329 5188 10minus07 minus0380 minus066421 7180 5214 3498 5 1 18600 1334 10minus08 minus0114 minus055137 4460 6939 4779 2 0 8245 4076 10minus06 minus2200 minus178138 10680 5122 4374 2 1 6388 5083 10minus06 minus2245 minus151242 8480 5260 4457 3 1 12005 1307 10minus07 minus1815 minus160864 13170 5297 4388 5 2 47653 1201 10minus10 minus0940 minus0455

Test set (amastigotes) pIC50AMA

Number 119864HOMO 119870H 119879 NHA NHD CT Obs MLR5 minus5850 106461 1300866 3 2 19110 0699 minus07876 minus5785 100595 1247456 5 2 18715 1523 033226 minus5665 583700 926651 2 0 7815 minus1170 minus145627 minus5603 622270 930050 3 0 8827 minus1877 minus162829 minus5640 618850 938380 3 0 9007 minus1561 minus146736 minus5966 676650 1037977 2 1 12201 minus1427 minus077437 minus6025 552770 930967 2 0 8348 minus2185 minus145744 minus5919 749400 1083064 2 1 12828 minus1004 minus097846 minus5790 752980 1078419 3 1 13925 minus0973 minus073350 minus5461 625750 913563 3 2 13449 minus0531 minus1028

minus05

0

05

1

15

2

25

minus02 03 08 13 18 23

ActivesValidation

ActivesValidation

minus16

minus06

04

14

minus05 0 05 1 15

Pred (pIC50 PRO)pIC50 PRO Pred (pIC50 AMA)pIC50 AMA

pIC

50

PRO

Pred (pIC50 PRO)

pIC

50

AM

A

Pred (pIC50 AMA)

Figure 3 Correlations of observed and predicted activities (training set in black and test set in red) values calculated using MLR models

of heteroatoms (nitrogen or oxygen atoms) mostlythe nitrogen ones for decreasing the ratio of nitrogen(N) having also a negative sign in the model

(ii) The electrophilicity index 120596 has a negative sign inthe model which suggests that increased activity canbe achieved by decreasing the electrophilicity of the

acridine derivatives (a high value of electrophilicitydescribes a good electrophile while a small value ofelectrophilicity describes a good nucleophile)

(iii) The dipolemoment 120583 has a positive sign in themodelwhich suggests that increased activity can be achievedby increasing the polarity of the acridine derivatives


(iv) The number of H-Bond donors (NHD) has a positivesign in themodel which suggests that increased activ-ity can be achieved by increasing of the heteroatomwith one or more hydrogen atoms

(v) The molecular topological index (MTI) coefficient(topological parameter) has a negative sign in themodel which suggests that increased activity can beachieved by increasing the flexibility of the substituentside chain

(vi) The total valence connectivity (TVC) (topologicalparameter) has a positive sign in the model whichsuggests that increased activity can be achieved byincreasing in branching of the acridine derivativesbecause the overall connectivity increases with bothmolecule size and complexity as expressed in branch-ing and cyclicity of molecular skeleton [46]

In the second model (pIC50

AMA) the descriptors influ-encing negatively the activity are Henryrsquos law constant (119870H)and the melting point (119879) and the parameters influencingpositively the activities are 119864HOMO energy the critical tem-perature (CT) the number of H-Bond acceptors (NHA) andthe number of H-Bond donors (NHD)

(i) Henryrsquos law constant (119870H) has a negative sign in themodel which suggests that increased activity can beachieved by decreasing solubility consequently bydecreasing the polarity 119870H = 119888119901 (119888 solubility 119901pressure)

(ii) The melting point (119879) has a negative sign in themodel which suggests that increased activity can beachieved by decreasing the polarity and the branch-ing of molecule (increasing branching makes themolecule less compact As the surface area of themolecule decreases it will becomemore compact andthus easier to pack)

(iii) The highest occupied molecular orbital energy119864HOMO (negative values) has a positive sign in themodel which suggests that the higher of 119864HOMO theweaker donating electron ability is showing the factthat the nucleophilic reaction occurs more easily andthe activity of the compound is higher

(iv) The critical temperature (CT) has a positive sign inthe model which suggests that increased activity canbe achieved by increasing the critical temperature

(v) The number of H-Bond acceptors (NHA) has a posi-tive sign in the model which suggests that increasedactivity can be achieved by increasing the number ofheteroatoms (nitrogen or oxygen atoms)

(vi) The number of H-Bond donors (NHD) has a positivesign in themodel which suggests that increased activ-ity can be achieved by increasing of the heteroatomswith one or more hydrogen atoms

Comparing the importance of each descriptor on pIC50

ofacridines we must know the standardized coefficient and the119905-test values of them in the MLR equations The bigger the

absolute value of the 119905-test value is the greater the influenceof the descriptor is

In (3) the 119905-test values are 1773 minus4035 7408 1720minus3361 minus2429 and minus4570 for N 120583 120596 NHA NHD MTIand TVC respectively Moreover in (4) the 119905-test values are3196 3556minus3764minus3802minus3838 andminus6012 for119879119870H CT119864HOMO NHA and NHD respectively

This meant that the 119905-test values of 120596 120583 NHD andTVC are both larger than those of the other descriptorswhich indicates that in this model the influence of thesedescriptors on activity is stronger than that of the others Itshows also the importance of electrophilicity index in theprediction of pIC

50PRO Moreover the 119905-test value of NHD

is larger than that of the other descriptors which indicatesthat the influence of this descriptor in this model on activityis stronger than that of the others

In the conclusion these results illustrate that to increasethe antileishmanial activity against promastigotes parasiteswe will decrease the electrophilicity and increase the branch-ing the polarity and the number of hydrogen atoms attachedin the heteroatom of the acridine derivatives Moreoverto increase the antileishmanial activity against amastigotesparasites we will decrease the solubility the polarity and thebranching and increase the electrophilicity and the numberof heteroatoms mostly attached in the hydrogen atoms andthe critical temperature of the acridine derivatives

(i) Detection of Outliners Applicability Domain (AD) Theapplicability domain (AD) of these models was evaluated byleverage analysis expressed as Williams plot (Figures 4 and5) in which the standardized residuals (119903) and the leveragethreshold values (ℎlowast = 0585 and 0553 for pIC

50(PRO)

and pIC50

(AMA) resp) were plotted Any new value ofpredicted pIC

50data must be considered reliable only for

those compounds that fall within this ADonwhich themodelwas constructed

From these figures it is obvious that there is no responseoutlier in training set and no response outside in test set Onlyone chemical is identified as outside forMLRmodel for pIC

50

PROand two chemicals forMLRmodel for pIC50AMA these

outsides compounds are given as follows

For pIC50 PRO Compound number 64 in test set has higherleverage which is greater than ℎlowast value of 0585 and all thecompounds have a standard deviation into plusmn119909 interval (119909 =3)

For pIC50 AMA Compounds numbers 5 and 6 in test set havea standard deviation value greater than the plusmn119909 interval (119909 =3)

These results are confirmed using the simple approach(Tables S3 and S4) the chemical number 8 is identified asoutlier and the chemical number 64 is identified as outsideforMLRmodel for pIC

50PRO and any chemical is identified

as outside or outlier for MLR model for pIC50AMA

These erroneous predictions could probably be attributedto wrong experimental data or to the structure of theseoutsides Cl F or NMe2 substitutes the tree compoundsat the para position of the phenyl ring (Figure 6) maybe


0 02 04 06

minus2

minus15

minus1

minus05

0

05

1

15

2

12

4

7

8

9

11

12

15

16

18

19

20

23

24

25

26

27

28

29

30

3132

3334

36

39

40

41

43

45

46

47

48

49

50

52

54

56

58

62

3

6 10

1417

21

37

38

42

64

William plot train samples in black test samples in red

Leverage

Stan

dard

ized

resid

uals

Figure 4 Williams plot of standardized residual versus leverage forpIC50(PRO)MLRmodel (with ℎlowast = 0585 and residual limits= plusmn3)

the selected descriptors do not pay much attention to thesespecial substructures The predictions of tree compounds areextrapolations of themodel but fortunately they are all ldquogoodleveragerdquo chemicals

34 Artificial Neural Network (ANN) In this study we used afeed forward network with two layers with a sigmoid transferfunction in the hidden layer and a linear transfer function inthe output layer (Figure 2) The architecture of the artificialneural network used in this work was 7-3-1 and 120588 = 182 forpIC50(PRO) and 6-3-1 and 120588 = 2 for pIC

50(AMA)

The output layer represents the calculated activity values(predicted pIC

50)The calculation result and the performance

of established models are recorded in the output layerTo justify the predictive quality of models total data

are distributed randomly into three groups The first group(70 of the total data) used to drive the system The secondgroup (15 of the total data) will be used to validate thenetwork and the remaining 15 that did not participate inthe learning models will be used as an independent test ofnetwork generalizationThe distribution of the total data intotraining validation and test sets is shown in Table 7

The correlation between the experimental and calculatedvalues using the artificial neural network models is highlysignificant as illustrated in Figure 7 and as indicated bybetter 119877 and 1198772 and the small MSE values for all threephases training validation and test (Table 7) The predictedactivities calculated with the artificial neural network and theobserved values are given in Table S5

The results obtained byMLR and ANN are very sufficientto conclude the performance of the models A comparison

0 01 02 03 04 05

minus4

minus3

minus2

minus1

0

1

2

12

3

47

8

1011

13

14

15

16

17

18

20

21

24

2528

31 32

33

34

35

38

40

45

47

48

5152

56

57

58

60

63

6465

5

6

26

2729

36 37

44

46

50


LeverageSt

anda

rdiz

ed re

sidua

ls

Figure 5 Williams plot of standardized residual versus leverage forpIC50

(AMA) MLR model (with ℎlowast = 0553 and residual limits =plusmn3)

of the quality of the statistical terms of these models showsthat the ANN has substantially better predictive capabilityANN was able to establish a more satisfactory relationshipbetween the molecular descriptors and the activity of thestudied compounds compared toMLR but themost negativeside of this method is the fact that it is poorly transparentwhereas transparency of MLR approach gives the most inter-pretable results and gives a good explanation of activities withdescriptors Consequently we can design new compoundswith improved values of activity compared to the studiedcompounds using the MLR models Taking into accountthe above results we added suitable substitutions and thenwe moved to calculate their activities using the proposedmodels equations ((3) and (4)) Therefore the suggestedmodels will reduce the time and cost of synthesis as wellas the determination of the antileishmanial activities againstpromastigotes and amastigotes forms of parasites for theacridine derivatives

35 Design New Compounds with Higher AntileishmanialActivities According to the above discussions the MLRmodels could be applied to other acridines derivativesaccording to Table 1 and could add further knowledge in theimprovement of new way in antileishmanial drug researchIf we develop a new compound with better values than theexisting ones it may give rise to the development of moreactive compounds than those currently in use

In this way we carried out structural modification start-ing from compounds having the highest pIC

50values as

template (number 14 and number 20) The structures of thedesigned compounds and their parameter values calculated


N

OO

Cl Cl

N

OO

F F

N

HN NH

O O

N N

Me

Me

Me

Me

Compound number 5 Compound number 6

Compound number 64

Figure 6 Chemical structures of the outsides compounds

minus05

0

05

1

15

2

25

minus05 0 05 1 15 2



minus17

minus12

minus07

minus02

03

08

13

18

23

minus2 minus1 0 1 2


pIC

50

PRO

Pred (pIC50 PRO)

pIC

50

AM

A

Pred (pIC50 AMA)

Figure 7 Correlations between observed and predicted activities values calculated using ANN models (training set in black validation setin red and test set in blue)

Table 7The coefficient of determination the coe1fficient of correlation and the MSE obtained by the model established by the ANN for thethree phases Training validation and test

pIC50PRO

Model 119899 = 51 MSE = 0079 1198772 = 0870 119877 = 0933

Training set [1 3 4 5 6 7 11 12 13 14 16 19 21 25 26 27 28 30 3234 35 36 37 38 39 40 41 43 44 46 47 48 49 50 51] MSE = 0044 1198772 = 0914 119877 = 0956

Validation set [8 17 18 22 23 24 29 31] MSE = 0125 1198772 = 0922 119877 = 0960Test set [2 9 10 15 20 33 42 45] MSE = 0186 1198772 = 0725 119877 = 0851pIC50AMA

Model 119899 = 48 MSE = 0104 1198772 = 0918 119877 = 0843

Training set [2 3 4 5 6 7 10 11 13 16 18 19 20 21 23 27 28 2930 31 32 33 34 35 36 37 38 39 41 43 44 45 46 47] MSE = 0060 1198772 = 0897 119877 = 0947

Validation set [1 8 14 15 17 24 25] MSE = 0198 1198772 = 0710 119877 = 0843Test set [9 12 22 26 40 42 48] MSE = 0228 1198772 = 0791 119877 = 0890

Advances in Physical Chemistry 13Ta

ble8Va

lues

ofdescrip

torsantileish

manialactivity

pIC 50and

leverages(ℎ)

forthe

newdesig

nedcompo

unds

Designedcompo

unds

Num

ber

Descriptorsvalues

pIC 50(PRO

)Num

ber

Descriptorsvalues

pIC 50(A

MA)

N

R 2R 1

NH

2

1198831

R 1=OHR2=CH3

N=12490120583

=2232120596=

3099

NHA=3NHD=2MTI

=3651

TVC=334

10minus05

0571

119884 1

R 1=OCH3R2=CF3

119864 HOMO=minus5

235C

T=885365

119870H=93

02

119879=5814

30N

HA=6NHD=1

1395

119884 2

R 1=OHR2=CF3

119864 HOMO=minus5

312CT

=903801

119870H=12056

119879=647130N

HA=6NHD=2

099

8

119884 3

R 1=OCH3R2=F

119864 HOMO=minus5

034C

T=893897

119870H=10174

119879=566560NHA=4NHD=1

048

5

1198832

R 1=OHR2=OH

N=12380120583

=10

80120596

=3131

NHA=4NHD=3MTI

=3497

TVC=14

910minus05

0170

119884 4

R 1=R 2

=OC 2

H5

119864 HOMO=minus4

696C

T=9272

41

119870H=11222

119879=622010NHA=4NHD=1

046

4

119884 5

R 1=R 2

=OCH3

119864 HOMO=minus4

731C

T=916967

119870H=11469

119879=5994

70N

HA=4NHD=1

044

2

119884 6

R 1=OCH3R2=OC(

CH3) 3

119864 HOMO=minus4

872C

T=928656

119870H=11099

119879=635700NHA=4NHD=1

0109

119884 7

R 1=OCH3R2=CH2NH2

119864 HOMO=minus4

801C

T=934561

119870H=1418

5119879=66

0500NHA=4NHD=2

0122

N

O

O

R 2R 1

NH

2

1198833

R 1=OHR2=meta-NH2

N=11250120583

=5944120596=3392

NHA=5NHD=3MTI

=17128

TVC=223

10minus08

083

8119884 8

R 1=OCH3R2=param

eta-

F119864 H

OMO=minus5

081C

T=1006097

119870H=13267119879

=762370NHA=

6NHD=1

030

8

1198834

R 1=OCH3R2=para-N

H2

N=10850120583

=7114120596

=3295

NHA=5NHD=2MTI

=19138

TVC=204

10minus08

060

7119884 9

R 1=OCH3R2=para

NH2

119864 HOMO=minus4

966C

T=1066417

119870H=16853119879

=8319

3NHA=

5NHD=2

0199

1198835

R 1=OCH3R2=meta-NH2

N=10850120583

=6193120596=3353

NHA=5NHD=2MTI

=18992

TVC=204

10minus08

030

9119884 10

R 1=OCH3R2=metaN

H2

119864 HOMO=minus4

993C

T=1066417

119870H=16853119879

=8319

30N

HA=

5NHD=2

0166

1198836

R 1=OHR2=H

N=78

20120583

=5631120596=3483

NHA=4NHD=2MTI

=15585

TVC=447

10minus08

038

1


N

R1

R2

R3

R4R5

R6

R7

Figure 8

by the same methods as well as pIC50

values theoreticallypredicted by the MLR models are listed in Table 8

From the predicted activities it has been observed thatthe designed compounds have higher pIC

50values than the

existing compounds in the case of the 60 studied compounds(Table 1)

The leverage values (ℎ) for the new designed com-pounds are 0268 0602 0775 0527 0386 and 0369 for1198831 1198832 1198836 respectively and 0649 0797 0201 02510235 0166 0176 0339 0169 and 0166 for 1198841 1198842 11988410respectively (Table 8) Only compounds 1198832 1198833 1198841 and1198842 are defined as outliers and consequently they are notbe considered because they have higher leverage which isgreater than ℎlowast (0585 for pIC

50(PRO) and 0553 for pIC

50

(AMA)) we suggest all other compounds as candidates thatwill be synthetized and evaluated as antileishmanial drugs

4 Conclusion

Following the five principles of the Organisation for Eco-nomic Co-operation and Development (OECD) for thevalidation of QSAR models two different modelling meth-ods multiple linear regression (MLR) and artificial neuralnetwork (ANN) were used in the construction of QSARmodels for the antileishmanial activities against two formsof parasites (promastigotes and amastigotes) of acridinesderivatives The accuracy and predictability of the proposedmodels were proven by the comparison of key statisticalterms of models The good results obtained with the internaland external validations show that the proposed models inthis paper are able to predict activities with a great perfor-mance and that the selected descriptors are pertinent Theapplicability domain (AD) of the MLR model was definedThe resulting models have shown that we have establisheda relationship between some descriptors and the activitiesin satisfactory manners The ANN results have substantiallybetter predictive capability than the MLR but the latter givesthe most important interpretable results

The obtained results show that to increase antileishma-nial activity against promastigotes parasites we will increaseelectrophilicity anddecrease branching polarity andnumberof hydrogen atoms attached in the heteroatom of the acridinederivatives Moreover to increase antileishmanial activityagainst amastigotes parasites we will increase solubilitypolarity and branching and decrease electrophilicity andnumber of heteroatoms mostly attached in the hydrogenatoms and critical temperature of acridine derivatives Com-paring 119905-test and standardized coefficient values of descrip-tors indicates that the influences of the electrophilicity index

(120596) on pIC50

(PRO) and of the number of H-Bond donors(NHD) on pIC

50(AMA) are stronger than those of the others

The most important finding from this research is that wehave designed and proposed new compounds with highervalues of activities compared to existing ones by addingsuitable substituents and calculating their activity using theregression equations Consequently the proposed modelswill reduce the time and cost of synthesis as well as thedetermination of the antileishmanial activities against pro-mastigotes and amastigotes forms of parasites of acridinederivatives

Competing Interests

The authors declare that they have no competing interests

Acknowledgments

The authors are grateful to the ldquoAssociation Marocainedes Chimistes Theoriciensrdquo (AMCT) for its pertinent helpconcerning the programs

References

[1] M R Moein R S Pawar S I Khan B L Tekwani and I AKhan ldquoAntileishmanial antiplasmodial amp cytotoxic activities of12 16-dideoxyaegyptinone B from Zhumeria majdae Rech f ampWendelbordquo Phytotherapy Research vol 22 pp 283ndash285 2008

[2] L G Rocha J R G S Almeida R O Macedo and J MBarbosa-Filho ldquoA review of natural products with antileishma-nial activityrdquo Phytomedicine vol 12 no 6-7 pp 514ndash535 2005

[3] O Kayser A F Kiderlen and S L Croft ldquoNatural productsas potential antiparasitic drugsrdquo Studies in Natural ProductsChemistry vol 26 pp 779ndash848 2002

[4] WHOmdashWorld Health Organization 2011 httpwwwwhointleishmaniasisdisease epidemiologyenindexhtml

[5] S M B Jeronimo P Duggal R F S Braz et al ldquoAn emergingperi-urban pattern of infection with Leishmania chagasi theprotozoan causing visceral leishmaniasis in northeast BrazilrdquoScandinavian Journal of Infectious Diseases vol 36 no 6-7 pp443ndash449 2004

[6] M V L Marlet D K Sang K Ritmeijer R O Muga JOnsongo and R N Davidson ldquoEmergence or re-emergence ofvisceral leishmaniasis in areas of Somalia northeastern Kenyaand south-eastern Ethiopia in 2000-2001rdquo Transactions of theRoyal Society of Tropical Medicine amp Hygiene vol 97 no 5 pp515ndash518 2003

[7] J Querido ldquoEmergency initiative to reduce leishmaniasis inAfghanistanrdquo The Lancet Infectious Diseases vol 4 no 10 p599 2004

[8] B L Herwaldt ldquoLeishmaniasisrdquo The Lancet vol 354 no 9185pp 1191ndash1199 1999

[9] Control of the leishmaniases Control of the leishmaniasesControl of the leishmaniases 2010 Geneva Switzerland httpappswhointirisbitstream10665444121WHO TRS 949 engpdf

[10] P Desjeux ldquoThe increase in risk factors for Leishmania-sis worldwiderdquo Transactions of the Royal Society of TropicalMedicine amp Hygiene vol 95 no 3 pp 239ndash243 2001


[11] G Chakrabarti A Basu P P Manna S B Mahato NB Mandal and S Bandyopadhyay ldquoIndolylquinoline deriva-tives are cytotoxic to Leishmania donovani promastigotes andamastigotes in vitro and are effective in treating murine visceralleishmaniasisrdquo Journal of Antimicrobial Chemotherapy vol 43no 3 pp 359ndash366 1999

[12] S A Gamage D P Figgitt S J Wojcik et al ldquoStructure-activity relationships for the antileishmanial and antitrypanoso-mal activities of 1rsquo-substituted 9-anilinoacridinesrdquo Journal ofMedicinal Chemistry vol 40 no 16 pp 2634ndash2642 1997

[13] M O F Khan S E Austin C Chan et al ldquoUse of anadditional hydrophobic binding site the Z site in the rationaldrug design of a new class of stronger trypanothione reductaseinhibitor quaternary alkylammonium phenothiazinesrdquo Journalof Medicinal Chemistry vol 43 no 16 pp 3148ndash3156 2000

[14] D Pathak M Yadav N Siddiqui and S Kushawah ldquoAntileish-manial agents an updated reviewrdquoDer Pharma Chemica vol 3no 1 pp 39ndash249 2011

[15] L Gupta A Talwar Nishi S Palne S Gupta and P M SChauhan ldquoSynthesis of marine alkaloid 89-Dihydrocoscin-amide B and its analogues as novel class of antileishmanialagentsrdquo Bioorganic and Medicinal Chemistry Letters vol 17 no14 pp 4075ndash4079 2007

[16] WADenny ldquoAcridine derivatives as chemotherapeutic agentsrdquoCurrent Medicinal Chemistry vol 9 no 18 pp 1655ndash1665 2002

[17] M Wainwright ldquoAcridinemdasha neglected antibacterial chro-mophorerdquo Journal of Antimicrobial Chemotherapy vol 47 no1 pp 1ndash13 2001

[18] I Antonini ldquoDNA-binding antitumor agents From pyrim-ido[561-de]acridines to other intriguing classes of acridinederivativesrdquo Current Medicinal Chemistry vol 9 no 18 pp1701ndash1716 2002

[19] M Demeunynck F Charmantray and A Martelli ldquoInterest ofacridine derivatives in the anticancer chemotherapyrdquo CurrentPharmaceutical Design vol 7 no 17 pp 1703ndash1724 2001

[20] S A Gamage D P Figgitt S J Wojcik et al ldquoStructure-activity relationships for the antileishmanial and antitrypanoso-mal activities of 11015840-substituted 9-anilinoacridinesrdquo Journal ofMedicinal Chemistry vol 40 no 16 pp 2634ndash2642 1997

[21] C M Mesa-Valle J Castilla-Calvente M Sanchez-Moreno VMoraleda-Lindez J Barbe and A Osuna ldquoActivity and modeof action of acridine compounds against Leishmania donovanirdquoAntimicrobial Agents and Chemotherapy vol 40 no 3 pp 684ndash690 1996

[22] C Di Giorgio K Shimi G Boyer F Delmas and J-P GalyldquoSynthesis and antileishmanial activity of 6-mono-substitutedand 36-di-substituted acridines obtained by acylation ofproflavinerdquo European Journal of Medicinal Chemistry vol 42no 10 pp 1277ndash1284 2007

[23] C Di Giorgio F Delmas N Filloux et al ldquoIn vitro activitiesof 7-substituted 9-chloro and 9-amino-2-methoxyacridines andtheir bis- and tetra-acridine complexes against Leishmaniainfantumrdquo Antimicrobial Agents and Chemotherapy vol 47 no1 pp 174ndash180 2003

[24] GCarole DMMichel C Julien et al ldquoSynthesis and antileish-manial activities of 45-di-substituted acridines as compared totheir 4-mono-substituted homologuesrdquo Bioorganic ampMedicinalChemistry vol 13 no 19 pp 5560ndash5568 2005

[25] Adamoand Baron (2000) Parac ampGrimme (2003) Gaussian 03[26] ACDLABS 10 ldquoAdvanced Chemistry Development Inc

Toronto ON Canadardquo 2015 httpwwwacdlabscom

[27] MarvinSketch 5114 ldquoChem Axonrdquo 2012 httpwwwche-maxoncom

[28] ChemBioOffice PerkinElmer Informatics 2010 httpwwwcambridgesoftcom

[29] J C Dearden M T D Cronin and K L E Kaiser ldquoHow not todevelop a quantitative structure-activity or structure-propertyrelationship (QSARQSPR)rdquo SAR and QSAR in EnvironmentalResearch vol 20 no 3-4 pp 241ndash266 2009

[30] K Roy R N Das P Ambure and R B Aher ldquoBe aware of errormeasures Further studies on validation of predictive QSARmodelsrdquo Chemometrics and Intelligent Laboratory Systems vol152 pp 18ndash33 2016

[31] OECD Guidance Document on the Validation of QSAR ModelsOrganization for Economic Co-Operation amp DevelopmentParis France 2007

[32] L Eriksson J Jaworska A P Worth M T D Cronin RM McDowell and P Gramatica ldquoMethods for reliability anduncertainty assessment and for applicability evaluations ofclassification- and regression-based QSARsrdquo EnvironmentalHealth Perspectives vol 111 no 10 pp 1361ndash1375 2003

[33] P Gramatica ldquoPrinciples of QSAR models validation internaland externalrdquo QSAR and Combinatorial Science vol 26 no 5pp 694ndash701 2007

[34] G E Batista and D F Silva ldquoHow k-nearest neighbor param-eters affect its performancerdquo in Proceedings of the ArgentineSymposium on Artificial Intelligence pp 1ndash12 Instituto deCiencias Matematicase de Computa cao Sao Carlos Brazil2009

[35] T I Netzeva A P Worth T Aldenberg et al ldquoCurrentstatus of methods for defining the applicability domain of(quantitative) structure-activity relationshipsrdquo Alternatives toLaboratory Animals vol 33 no 2 pp 155ndash173 2005

[36] J C Dearden ldquoThe history and development of quantitativestructure-activity relationships (QSARs)rdquo International Journalof Quantitative Structure-Property Relationships vol 1 no 1 pp1ndash44 2016

[37] K Roy S Kar and P Ambure ldquoOn a simple approach for deter-mining applicability domain of QSAR modelsrdquo Chemometricsand Intelligent Laboratory Systems vol 145 pp 22ndash29 2015

[38] K Roy S Kar and R Das Narayan ldquoUnderstanding the Basicsof QSAR for Applications in Pharmaceutical Sciences amp RiskAssessmentrdquo in Chapter 6-Selected Statistical Methods in QSARpp 191ndash229 Academic Press Boston Mass USA 2015

[39] S-S So and W Graham Richards ldquoApplication of neuralnetworks quantitative structure-activity relationships of thederivatives of 24-diamino-5-(substituted-benzyl)pyrimidinesas DHFR inhibitorsrdquo Journal ofMedicinal Chemistry vol 35 no17 pp 3201ndash3207 1992

[40] T A Andrea and H Kalayeh ldquoApplications of neural networksin quantitative structure-activity relationships of dihydrofolatereductase inhibitorsrdquo Journal of Medicinal Chemistry vol 34no 9 pp 2824ndash2836 1991

[41] A Golbraikh and A Tropsha ldquoBeware of q2rdquo Journal ofMolecular Graphics and Modelling vol 20 no 4 pp 269ndash2762002

[42] S Chtita RHmamouchiM LarifMGhamaliM Bouachrineand T Lakhlifi ldquoQSPR studies of 9-aniliioacridine derivativesfor their DNA drug binding properties based on densityfunctional theory using statistical methods model validationand influencing factorsrdquo Journal of TaibahUniversity for Sciencevol 10 no 6 pp 868ndash876 2016


[43] S Chtita M Larif M Ghamali M Bouachrine and TLakhlifi ldquoQuantitative structurendashactivity relationship studies ofdibenzo[ad]cycloalkenimine derivatives for non-competitiveantagonists of N-methyl-d-aspartate based on density func-tional theory with electronic and topological descriptorsrdquoJournal of Taibah University for Science vol 9 no 2 pp 143ndash154 2015

[44] R G Brereton Chemometrics Data Analysis for the Laboratoryand Chemical Plant John Wiley amp Sons Chichester UK 2003

[45] WD Fisher ldquoOn grouping formaximumhomogeneityrdquo Journalof the American Statistical Association vol 53 no 284 pp 789ndash798 1958

[46] A N Choudhary A Kumar and V Juyal ldquoQuantitativestructure activity relationship (QSAR) analysis of substituted4-oxothiazolidines and 5-arylidines as lipoxygenase inhibitorsrdquoMini-Reviews inMedicinal Chemistry vol 10 no 8 pp 705ndash7142010

Submit your manuscripts athttpwwwhindawicom

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Inorganic ChemistryInternational Journal of

Hindawi Publishing Corporation httpwwwhindawicom Volume 2014

International Journal ofPhotoenergy


Carbohydrate Chemistry

International Journal of


Journal of

Chemistry


Advances in

Physical Chemistry

Hindawi Publishing Corporationhttpwwwhindawicom

Analytical Methods in Chemistry

Journal of

Volume 2014

Bioinorganic Chemistry and ApplicationsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

SpectroscopyInternational Journal of


The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Medicinal ChemistryInternational Journal of


Chromatography Research International


Applied ChemistryJournal of



Theoretical ChemistryJournal of


Journal of

Spectroscopy

Analytical ChemistryInternational Journal of


Journal of


Quantum Chemistry


Organic Chemistry International

ElectrochemistryInternational Journal of



CatalystsJournal of


strains the toxicity of current drugs severe side effects andhigh cost andor restricted therapeutic spectrum a need fordevelopment of new and safer drugs is warranted [2 3 10]A great number of natural and synthetic compounds havebeen tested in the past years in antileishmanial assays Theirstructures are diverse and often contain nitrogen heterocyclessuch as quinolines pyrimidines acridines phenothiazinesand indoles [11ndash13]

Many experiments have been performed with the com-pounds bearing the heterocyclic ring structures to exploretheir effectiveness against Leishmania These studies sug-gested their similar pharmacophoric feature of the hetero-cyclic scaffold as a potential target for drug discovery ofantileishmanial drugs [14]

Leishmania parasites exist in two forms one is promastig-otes and the other is amastigotes The promastigotes areflagellated and found in sand fly while the amastigotes areovoid and nonflagellated form of Leishmania [15] Antileish-manial activity is performed against promastigotes and thenamastigotes forms of parasites Heterocyclic system may alsobe formed by fusion with other rings either carbocyclic orheterocyclic

Since their discovery in the 1880s acridines family havedemonstrated a broad spectrum of pharmacological prop-erties [16] First employed as antibacterial agents duringthe beginning of the twentieth century [17] They havebeen rapidly revealing interesting antiproliferative activitiesagainst both protozoa and tumor cells [18 19] Consequentlythey have been extensively used in antiparasitic chemother-apy and a wide range of new acridines derivatives have beensynthesized and successfully assessed for their antileishma-nial activities [20 21]

In order to open a new way in antileishmanial drugresearch a series of sixty acridines derivatives were synthe-sized [22ndash24] and studied for their antileishmanial (againstpromastigotes and amastigotes form of parasites) activitiesThe aim of this study was to develop a QSAR model able tocorrelate the structural features of the acridines derivativeswith their biological activities

In general the QSAR methods are based on the assump-tion that the activity of a certain chemical compound relatedto its structure through a certain mathematical algorithmThis relationship can be used in the prediction interpre-tation and assessment of new compounds with desiredactivities reducing and rationalizing time efforts and cost ofsynthesis and new product development

The basic assumption to drive a QSARmodel is presenteddue to a mathematical function of the chemical propertieswhich is related to the effect (activity) Therefore the effectis like the function ldquo119891rdquo of the chemical properties ldquo119909rdquo119910 = 119891(119909) To find this algorithm we use a number ofchemical compounds with known values of the studied effect(119910) For each chemical compound we calculate a series ofparameters (called chemical descriptors) Then we find analgorithm that provides a quite accurate value similar tothe real experimental value The final step is to check if theobtained algorithm is able to predict the activity values forother chemicals not used to build up the model (externalvalidation)

Indeed it is very important to generate a model whichworked not only for the chemical substances used withinthe training set but also for other similar chemicals Con-sequently the challenge is to define the correct statisticalproperties of the model

2 Materials and Methods

The current QSAR study investigates prediction and inter-pretation of the studied compounds and was also used fordesigning new proposed compounds by using linear andnonlinear methods It consists of four stages selection ofdataset and generation of molecular descriptors descriptiveanalysis statistical analysis (prediction and evaluation) andsuggestion of novel compounds

A flow chart for the development of the QSAR modelalong with the various validation methods used in this workis demonstrated in Figure 1

21 Selection of Dataset and Generation ofMolecular Descriptors

211 Selection of Dataset In this stage the datasets ofthe antileishmanial activities (against promastigotes andamastigotes forms of parasites) of various acridine derivatives(4-monosubstituted acridines 36-disubstituted acridines45-disubstituted acridines and 7-monosubstituted 9-chloroand 9-amino-2-methoxy acridines) were collected from pre-vious works [22ndash24] The molecular structures of the studiedmolecules with their antileishmanial activities are presentedin Table 1 All experimental IC50 antileishmanial activityvalues (120583M) were converted to the negative logarithm ofIC50 (pIC50 = minuslog

10(IC50))

212 Molecular Descriptors Generation A wide variety ofmolecular descriptors was calculated using Gaussian 03ACDChemSketch Marvin Sketch and ChemOffice pro-grams [25ndash28] to predict the correlation between thesedescriptors for the studied molecules with their antileishma-nial activities and to develop linear (multiple linear regression(MLR)) and nonlinear (artificial neural network (ANN))models Tables 3 and 4 show the selected descriptors (usingthe PCA method see more in descriptive analysis results) tobe used in this study

22 Descriptive Analysis In this stage the principal compo-nent analysis (PCA) was used to determine the nonlinearityand nonmulticollinearity among variables (descriptors) andto select descriptors that correlate with the activity Afterthat the univariate partitioning (UP) method was used toform dissimilar clusters of compounds to which the querycompounds would be compared for determining the degreeof similarity and dividing the dataset into training and testsets

23 Statistical Analysis (Models Development and Evaluation)In this stage linear and nonlinear QSAR models weredeveloped and evaluated to predict the activities of the testcompoundsThe study we conducted consists of the multiple


N

1


1 14330 4752 5141 3 2 82052 13080 4353 5023 3 2 108354 10070 4124 4469 3 2 255717 8800 0805 4167 5 2 355298 7820 0141 3989 7 2 45907

62 8310 3311 4511 5 2 39911

Obs25332204063406811207



0308131823

minus2 minus1 0 1 2


23

4




Activity



models


activities











pIC50

R5

R6

R7

R4

R1

R2

R3


Pred (pIC50 AMA)

pIC

50

AM

A










Table1Ch

emicalstructurea


ofstu

died

compo

unds

(see

Figure

8)

Num

ber

R 1R 2

R 3R 4

R 5R 6

R 7pIC 50

b

PRO

cAMA

d

1H

NHCO

CH3

HH

HNHCO

CH3

Hminus2

533

minus0653

2H

NHCO

C 2H5

HH

HNHCO

C 2H5

Hminus2

204

minus0886

3H

NHCO

C 3H7

HH

HNHCO

C 3H7

Hminus1669

minus110

74

HNHCO

phH

HH

NHCO

PhH

minus0634

minus0041

5H

NHCO

-p-PhC

lH

HH

NHCO

-p-PhC

lH

mdash0699

6H

NHCO

-p-PhF

HH

HNHCO

-p-PhF

Hminus0

230

1523

7H

NHCO

-p-PhO

Me

HH

HNHCO

-p-PhO

Me

Hminus0

681

minus0041

8H

NHCO

-mp-Ph(OMe)2

HH

HNHCO

-mp-Ph(OMe)2

Hminus1207

0097

9H

NHCO

Me

HH

HNHCO

phH

minus2270

Toxa

10H

NHCO

Me

HH

HNHCO

-p-PhC

lH

minus1061

minus0462

11H

NHCO

Me

HH

HNHCO

-p-PhF

Hminus2

123

0174

12H

NHCO

Me

HH

HNHCO

-p-PhO

Me

Hminus1939

Toxa

13H

NHCO

Me

HH

HNHCO

-mp-Ph(OMe)2

Hmdash

minus0114

14H

HCH3

NH2

OMe

HH

0301

0398

15H

HCH2OH

NH2

OMe

HH

minus0732

minus0613

16H

HCH2Br

NH2

OMe

HH

minus0556

minus0663

17H

H(C

H2) 2OCO

OMe

NH2

OMe

HH

minus0380

minus0114

18H

H(C

H2) 2OCO

(CH2) 2CH3

NH2

OMe

HH

minus0491

minus039

819

HH

(CH2) 2OCO

CH2CH

(CH3) 2

NH2

OMe

HH

minus0342

Toxa

20H

H(C

H2) 2OCO

PhNH2

OMe

HH

0398

0699

21H

H(C

H2) 2OCO

PhF

NH2

OMe

HH

minus0114

0222

23H

H(C

H2) 2OCO

PhOMe

NH2

OMe

HH

minus0279

Toxa

24H

HCH3

ClOMe

HH

minus0041

minus0362

25H

HCH2OH

ClOMe

HH

minus2262

minus0959

26H

HCH2Br

ClOMe

HH

minus1703

minus117

027

HH

(CH2) 2OCO

OMe

ClOMe

HH

minus217

8minus1877

28H

H(C

H2) 2OCO

(CH2) 2CH3

ClOMe

HH

minus1707

minus1628

29H

H(C

H2) 2OCO

CH2CH

(CH3) 2

ClOMe

HH

minus1446

minus1561

30H

H(C

H2) 2OCO

PhCl

OMe

HH

minus213

5To

xa31

HH

(CH2) 2OCO

PhF

ClOMe

HH

minus2295

minus1645

32H

H(C

H2) 2OCO

PhCl

ClOMe

HH

minus219

4minus2

191


Table1Con

tinued

Num

ber

R 1R 2

R 3R 4

R 5R 6

R 7pIC 50

b

PRO

cAMA

d

33H

H(C

H2) 2OCO

PhOMe

ClOMe

HH

minus2098

minus2088

34CH2NH2

HH

HH

HH

minus0230

minus053

135

CH2OH

HH

HH

HH

Toxa

minus1515

36CH2NHCO

(CH2) 3Cl

HH

HH

HH

minus1655

minus1427

37CH2OCO

(CH2) 3Cl

HH

HH

HH

minus2200

minus218

538

CH2NHCO

CH=C

H2

HH

HH

HH

minus2245

minus0833

39CH2OCO

CH=C

H2

HH

HH

HH

minus1464

mdash40

CH2NHCO

PhH

HH

HH

Hminus1654

minus112

141

CH2OCO

PhH

HH

HH

Hminus1885

Toxa

42CH2NHCO

-p-PhF

HH

HH

HH

minus1815

mdash43

CH2OCO

-p-PhF

HH

HH

HH

minus174

1To

xa44

CH2NHCO

-p-PhC

lH

HH

HH

HTo

xaminus1004

45CH2OCO

-p-PhC

lH

HH

HH

Hminus1790

minus1433

46CH2NHCO

-p-PhO

Me

HH

HH

HH

minus1513

minus0973

47CH2OCO

-p-PhO

Me

HH

HH

HH

minus1471

minus0869

48CH2NHCO

-p-PhN

Me 2

HH

HH

HH

minus153

9minus0

672

49CH2OCO

-p-PhN

Me 2

HH

HH

HH

minus1819

Toxa

50CH2NH2

HH

HH

HCH2NH2

minus0820

minus053

151

CH2OH

HH

HH

HCH2OH

Toxa

0222

52CH2NHCO

(CH2) 3Cl

HH

HH

HCH2NHCO

(CH2) 3Cl

minus0663

minus0813

54CH2NHCO

CH=C

H2

HH

HH

HCH2NHCO

CHCH2

minus1061

Toxa

56CH2NHCO

PhH

HH

HH

CH2NHCO

Phminus0

556

minus1236

57CH2OCO

PhH

HH

HH

CH2OCO

PhTo

xaminus0

716

58CH2NHCO

-p-PhF

HH

HH

HCH2NHCO

-p-PhF

minus0756

minus0255

60CH2NHCO

-p-PhC

lH

HH

HH

CH2NHCO

-p-PhC

lmdash

minus1562

62CH2NHCO

-p-PhO

Me

HH

HH

HCH2NHCO

-p-PhO

Me

minus1797

Toxa

63CH2OCO

-p-PhO

Me

HH

HH

HCH2OCO

-p-PhO

Me

Toxa

minus1825

64CH2NHCO

-p-PhN

Me 2

HH

HH

HCH2NHCO

-p-PhN

Me 2

minus0940

minus0724

65CH2OCO

-p-PhN

Me 2

HH

HH

HCH2OCO

-p-PhN

Me 2

Toxa

minus1667

a Toxictoxicity

observed

onhu

man

macroph


thatdidno

tdisp


-p-para-m

-meta

b pIC50=minuslog(

IC50)

c pIC50AMAantileish

manialactivity

againstamastig

otes


Oantileish

manialactivity

againstp

romastig

otes

parasiteform


Inputw

b

w

b

+ +


Output



119894(119883119879 119883)

minus1



119884 = 1198860 +119899

sum119894=1

119886119894119909119894 (1)























Gaussian 03


ChemOffice











1 1 8 37 40 56 1 10 15 16 34 48 50 57 642 2 14 29 34 48 2 25 38 44 47 51 523 3 18 19 3 26 40 564 4 24 25 27 30 42 58 4 7 8 11 13 17 21 465 6 26 5 14 206 7 9 12 21 33 39 41 47 67 10 11 16 31 32 43 45 18 29 588 15 17 23 28 36 49 52 54 27 639 20 38 46 62 24 28 31 35 36 45 60 6510 50 64 32 33 37





+ 3311 10minus05MTI + 3184 104TVC

(3)




+ 2543 10minus02CT minus 0350119870H

minus 1632 10minus02119879 + 0652NHA

+ 1262NHD

(4)










50of compounds from







MLR pIC50













minus05

0

05

1

15

2

25

minus02 03 08 13 18 23

ActivesValidation

ActivesValidation

minus16

minus06

04

14

minus05 0 05 1 15


pIC

50

PRO

Pred (pIC50 PRO)

pIC

50

AM

A

Pred (pIC50 AMA)



























50(PRO)

and pIC50





50










0 02 04 06

minus2

minus15

minus1

minus05

0

05

1

15

2

12

4

7

8

9

11

12

15

16

18

19

20

23

24

25

26

27

28

29

30

3132

3334

36

39

40

41

43

45

46

47

48

49

50

52

54

56

58

62

3

6 10

1417

21

37

38

42

64


Leverage

Stan

dard

ized

resid

uals




50(AMA)







0 01 02 03 04 05

minus4

minus3

minus2

minus1

0

1

2

12

3

47

8

1011

13

14

15

16

17

18

20

21

24

2528

31 32

33

34

35

38

40

45

47

48

5152

56

57

58

60

63

6465

5

6

26

2729

36 37

44

46

50


LeverageSt

anda

rdiz

ed re

sidua

ls






50values as



N

OO

Cl Cl

N

OO

F F

N

HN NH

O O

N N

Me

Me

Me

Me


Compound number 64


minus05

0

05

1

15

2

25

minus05 0 05 1 15 2



minus17

minus12

minus07

minus02

03

08

13

18

23

minus2 minus1 0 1 2


pIC

50

PRO

Pred (pIC50 PRO)

pIC

50

AM

A

Pred (pIC50 AMA)



pIC50PRO

Model 119899 = 51 MSE = 0079 1198772 = 0870 119877 = 0933

Training set [1 3 4 5 6 7 11 12 13 14 16 19 21 25 26 27 28 30 3234 35 36 37 38 39 40 41 43 44 46 47 48 49 50 51] MSE = 0044 1198772 = 0914 119877 = 0956


Model 119899 = 48 MSE = 0104 1198772 = 0918 119877 = 0843

Training set [2 3 4 5 6 7 10 11 13 16 18 19 20 21 23 27 28 2930 31 32 33 34 35 36 37 38 39 41 43 44 45 46 47] MSE = 0060 1198772 = 0897 119877 = 0947



ble8Va

lues

ofdescrip

torsantileish

manialactivity

pIC 50and

leverages(ℎ)

forthe

newdesig

nedcompo

unds

Designedcompo

unds

Num

ber

Descriptorsvalues

pIC 50(PRO

)Num

ber

Descriptorsvalues

pIC 50(A

MA)

N

R 2R 1

NH

2

1198831

R 1=OHR2=CH3

N=12490120583

=2232120596=

3099

NHA=3NHD=2MTI

=3651

TVC=334

10minus05

0571

119884 1

R 1=OCH3R2=CF3

119864 HOMO=minus5

235C

T=885365

119870H=93

02

119879=5814

30N

HA=6NHD=1

1395

119884 2

R 1=OHR2=CF3

119864 HOMO=minus5

312CT

=903801

119870H=12056

119879=647130N

HA=6NHD=2

099

8

119884 3

R 1=OCH3R2=F

119864 HOMO=minus5

034C

T=893897

119870H=10174

119879=566560NHA=4NHD=1

048

5

1198832

R 1=OHR2=OH

N=12380120583

=10

80120596

=3131

NHA=4NHD=3MTI

=3497

TVC=14

910minus05

0170

119884 4

R 1=R 2

=OC 2

H5

119864 HOMO=minus4

696C

T=9272

41

119870H=11222

119879=622010NHA=4NHD=1

046

4

119884 5

R 1=R 2

=OCH3

119864 HOMO=minus4

731C

T=916967

119870H=11469

119879=5994

70N

HA=4NHD=1

044

2

119884 6

R 1=OCH3R2=OC(

CH3) 3

119864 HOMO=minus4

872C

T=928656

119870H=11099

119879=635700NHA=4NHD=1

0109

119884 7

R 1=OCH3R2=CH2NH2

119864 HOMO=minus4

801C

T=934561

119870H=1418

5119879=66

0500NHA=4NHD=2

0122

N

O

O

R 2R 1

NH

2

1198833

R 1=OHR2=meta-NH2

N=11250120583

=5944120596=3392

NHA=5NHD=3MTI

=17128

TVC=223

10minus08

083

8119884 8

R 1=OCH3R2=param

eta-

F119864 H

OMO=minus5

081C

T=1006097

119870H=13267119879

=762370NHA=

6NHD=1

030

8

1198834

R 1=OCH3R2=para-N

H2

N=10850120583

=7114120596

=3295

NHA=5NHD=2MTI

=19138

TVC=204

10minus08

060

7119884 9

R 1=OCH3R2=para

NH2

119864 HOMO=minus4

966C

T=1066417

119870H=16853119879

=8319

3NHA=

5NHD=2

0199

1198835

R 1=OCH3R2=meta-NH2

N=10850120583

=6193120596=3353

NHA=5NHD=2MTI

=18992

TVC=204

10minus08

030

9119884 10

R 1=OCH3R2=metaN

H2

119864 HOMO=minus4

993C

T=1066417

119870H=16853119879

=8319

30N

HA=

5NHD=2

0166

1198836

R 1=OHR2=H

N=78

20120583

=5631120596=3483

NHA=4NHD=2MTI

=15585

TVC=447

10minus08

038

1


N

R1

R2

R3

R4R5

R6

R7

Figure 8




50values than the




50


4 Conclusion



(120596) on pIC50




Competing Interests


Acknowledgments


References


























































Journal of

Chemistry


Advances in

Physical Chemistry



Journal of

Volume 2014














Journal of

Spectroscopy



Journal of


Quantum Chemistry






CatalystsJournal of


N

1


1 14330 4752 5141 3 2 82052 13080 4353 5023 3 2 108354 10070 4124 4469 3 2 255717 8800 0805 4167 5 2 355298 7820 0141 3989 7 2 45907

62 8310 3311 4511 5 2 39911

Obs25332204063406811207



0308131823

minus2 minus1 0 1 2


23

4




Activity



models


activities











pIC50

R5

R6

R7

R4

R1

R2

R3


Pred (pIC50 AMA)

pIC

50

AM

A










Table1Ch

emicalstructurea


ofstu

died

compo

unds

(see

Figure

8)

Num

ber

R 1R 2

R 3R 4

R 5R 6

R 7pIC 50

b

PRO

cAMA

d

1H

NHCO

CH3

HH

HNHCO

CH3

Hminus2

533

minus0653

2H

NHCO

C 2H5

HH

HNHCO

C 2H5

Hminus2

204

minus0886

3H

NHCO

C 3H7

HH

HNHCO

C 3H7

Hminus1669

minus110

74

HNHCO

phH

HH

NHCO

PhH

minus0634

minus0041

5H

NHCO

-p-PhC

lH

HH

NHCO

-p-PhC

lH

mdash0699

6H

NHCO

-p-PhF

HH

HNHCO

-p-PhF

Hminus0

230

1523

7H

NHCO

-p-PhO

Me

HH

HNHCO

-p-PhO

Me

Hminus0

681

minus0041

8H

NHCO

-mp-Ph(OMe)2

HH

HNHCO

-mp-Ph(OMe)2

Hminus1207

0097

9H

NHCO

Me

HH

HNHCO

phH

minus2270

Toxa

10H

NHCO

Me

HH

HNHCO

-p-PhC

lH

minus1061

minus0462

11H

NHCO

Me

HH

HNHCO

-p-PhF

Hminus2

123

0174

12H

NHCO

Me

HH

HNHCO

-p-PhO

Me

Hminus1939

Toxa

13H

NHCO

Me

HH

HNHCO

-mp-Ph(OMe)2

Hmdash

minus0114

14H

HCH3

NH2

OMe

HH

0301

0398

15H

HCH2OH

NH2

OMe

HH

minus0732

minus0613

16H

HCH2Br

NH2

OMe

HH

minus0556

minus0663

17H

H(C

H2) 2OCO

OMe

NH2

OMe

HH

minus0380

minus0114

18H

H(C

H2) 2OCO

(CH2) 2CH3

NH2

OMe

HH

minus0491

minus039

819

HH

(CH2) 2OCO

CH2CH

(CH3) 2

NH2

OMe

HH

minus0342

Toxa

20H

H(C

H2) 2OCO

PhNH2

OMe

HH

0398

0699

21H

H(C

H2) 2OCO

PhF

NH2

OMe

HH

minus0114

0222

23H

H(C

H2) 2OCO

PhOMe

NH2

OMe

HH

minus0279

Toxa

24H

HCH3

ClOMe

HH

minus0041

minus0362

25H

HCH2OH

ClOMe

HH

minus2262

minus0959

26H

HCH2Br

ClOMe

HH

minus1703

minus117

027

HH

(CH2) 2OCO

OMe

ClOMe

HH

minus217

8minus1877

28H

H(C

H2) 2OCO

(CH2) 2CH3

ClOMe

HH

minus1707

minus1628

29H

H(C

H2) 2OCO

CH2CH

(CH3) 2

ClOMe

HH

minus1446

minus1561

30H

H(C

H2) 2OCO

PhCl

OMe

HH

minus213

5To

xa31

HH

(CH2) 2OCO

PhF

ClOMe

HH

minus2295

minus1645

32H

H(C

H2) 2OCO

PhCl

ClOMe

HH

minus219

4minus2

191


Table1Con

tinued

Num

ber

R 1R 2

R 3R 4

R 5R 6

R 7pIC 50

b

PRO

cAMA

d

33H

H(C

H2) 2OCO

PhOMe

ClOMe

HH

minus2098

minus2088

34CH2NH2

HH

HH

HH

minus0230

minus053

135

CH2OH

HH

HH

HH

Toxa

minus1515

36CH2NHCO

(CH2) 3Cl

HH

HH

HH

minus1655

minus1427

37CH2OCO

(CH2) 3Cl

HH

HH

HH

minus2200

minus218

538

CH2NHCO

CH=C

H2

HH

HH

HH

minus2245

minus0833

39CH2OCO

CH=C

H2

HH

HH

HH

minus1464

mdash40

CH2NHCO

PhH

HH

HH

Hminus1654

minus112

141

CH2OCO

PhH

HH

HH

Hminus1885

Toxa

42CH2NHCO

-p-PhF

HH

HH

HH

minus1815

mdash43

CH2OCO

-p-PhF

HH

HH

HH

minus174

1To

xa44

CH2NHCO

-p-PhC

lH

HH

HH

HTo

xaminus1004

45CH2OCO

-p-PhC

lH

HH

HH

Hminus1790

minus1433

46CH2NHCO

-p-PhO

Me

HH

HH

HH

minus1513

minus0973

47CH2OCO

-p-PhO

Me

HH

HH

HH

minus1471

minus0869

48CH2NHCO

-p-PhN

Me 2

HH

HH

HH

minus153

9minus0

672

49CH2OCO

-p-PhN

Me 2

HH

HH

HH

minus1819

Toxa

50CH2NH2

HH

HH

HCH2NH2

minus0820

minus053

151

CH2OH

HH

HH

HCH2OH

Toxa

0222

52CH2NHCO

(CH2) 3Cl

HH

HH

HCH2NHCO

(CH2) 3Cl

minus0663

minus0813

54CH2NHCO

CH=C

H2

HH

HH

HCH2NHCO

CHCH2

minus1061

Toxa

56CH2NHCO

PhH

HH

HH

CH2NHCO

Phminus0

556

minus1236

57CH2OCO

PhH

HH

HH

CH2OCO

PhTo

xaminus0

716

58CH2NHCO

-p-PhF

HH

HH

HCH2NHCO

-p-PhF

minus0756

minus0255

60CH2NHCO

-p-PhC

lH

HH

HH

CH2NHCO

-p-PhC

lmdash

minus1562

62CH2NHCO

-p-PhO

Me

HH

HH

HCH2NHCO

-p-PhO

Me

minus1797

Toxa

63CH2OCO

-p-PhO

Me

HH

HH

HCH2OCO

-p-PhO

Me

Toxa

minus1825

64CH2NHCO

-p-PhN

Me 2

HH

HH

HCH2NHCO

-p-PhN

Me 2

minus0940

minus0724

65CH2OCO

-p-PhN

Me 2

HH

HH

HCH2OCO

-p-PhN

Me 2

Toxa

minus1667

a Toxictoxicity

observed

onhu

man

macroph


thatdidno

tdisp


-p-para-m

-meta

b pIC50=minuslog(

IC50)

c pIC50AMAantileish

manialactivity

againstamastig

otes


Oantileish

manialactivity

againstp

romastig

otes

parasiteform


Inputw

b

w

b

+ +


Output



119894(119883119879 119883)

minus1



119884 = 1198860 +119899

sum119894=1

119886119894119909119894 (1)























Gaussian 03


ChemOffice











1 1 8 37 40 56 1 10 15 16 34 48 50 57 642 2 14 29 34 48 2 25 38 44 47 51 523 3 18 19 3 26 40 564 4 24 25 27 30 42 58 4 7 8 11 13 17 21 465 6 26 5 14 206 7 9 12 21 33 39 41 47 67 10 11 16 31 32 43 45 18 29 588 15 17 23 28 36 49 52 54 27 639 20 38 46 62 24 28 31 35 36 45 60 6510 50 64 32 33 37





+ 3311 10minus05MTI + 3184 104TVC

(3)




+ 2543 10minus02CT minus 0350119870H

minus 1632 10minus02119879 + 0652NHA

+ 1262NHD

(4)










50of compounds from







MLR pIC50













minus05

0

05

1

15

2

25

minus02 03 08 13 18 23

ActivesValidation

ActivesValidation

minus16

minus06

04

14

minus05 0 05 1 15


pIC

50

PRO

Pred (pIC50 PRO)

pIC

50

AM

A

Pred (pIC50 AMA)



























50(PRO)

and pIC50





50










0 02 04 06

minus2

minus15

minus1

minus05

0

05

1

15

2

12

4

7

8

9

11

12

15

16

18

19

20

23

24

25

26

27

28

29

30

3132

3334

36

39

40

41

43

45

46

47

48

49

50

52

54

56

58

62

3

6 10

1417

21

37

38

42

64


Leverage

Stan

dard

ized

resid

uals




50(AMA)







0 01 02 03 04 05

minus4

minus3

minus2

minus1

0

1

2

12

3

47

8

1011

13

14

15

16

17

18

20

21

24

2528

31 32

33

34

35

38

40

45

47

48

5152

56

57

58

60

63

6465

5

6

26

2729

36 37

44

46

50


LeverageSt

anda

rdiz

ed re

sidua

ls






50values as



N

OO

Cl Cl

N

OO

F F

N

HN NH

O O

N N

Me

Me

Me

Me


Compound number 64


minus05

0

05

1

15

2

25

minus05 0 05 1 15 2



minus17

minus12

minus07

minus02

03

08

13

18

23

minus2 minus1 0 1 2


pIC

50

PRO

Pred (pIC50 PRO)

pIC

50

AM

A

Pred (pIC50 AMA)



pIC50PRO

Model 119899 = 51 MSE = 0079 1198772 = 0870 119877 = 0933

Training set [1 3 4 5 6 7 11 12 13 14 16 19 21 25 26 27 28 30 3234 35 36 37 38 39 40 41 43 44 46 47 48 49 50 51] MSE = 0044 1198772 = 0914 119877 = 0956


Model 119899 = 48 MSE = 0104 1198772 = 0918 119877 = 0843

Training set [2 3 4 5 6 7 10 11 13 16 18 19 20 21 23 27 28 2930 31 32 33 34 35 36 37 38 39 41 43 44 45 46 47] MSE = 0060 1198772 = 0897 119877 = 0947



ble8Va

lues

ofdescrip

torsantileish

manialactivity

pIC 50and

leverages(ℎ)

forthe

newdesig

nedcompo

unds

Designedcompo

unds

Num

ber

Descriptorsvalues

pIC 50(PRO

)Num

ber

Descriptorsvalues

pIC 50(A

MA)

N

R 2R 1

NH

2

1198831

R 1=OHR2=CH3

N=12490120583

=2232120596=

3099

NHA=3NHD=2MTI

=3651

TVC=334

10minus05

0571

119884 1

R 1=OCH3R2=CF3

119864 HOMO=minus5

235C

T=885365

119870H=93

02

119879=5814

30N

HA=6NHD=1

1395

119884 2

R 1=OHR2=CF3

119864 HOMO=minus5

312CT

=903801

119870H=12056

119879=647130N

HA=6NHD=2

099

8

119884 3

R 1=OCH3R2=F

119864 HOMO=minus5

034C

T=893897

119870H=10174

119879=566560NHA=4NHD=1

048

5

1198832

R 1=OHR2=OH

N=12380120583

=10

80120596

=3131

NHA=4NHD=3MTI

=3497

TVC=14

910minus05

0170

119884 4

R 1=R 2

=OC 2

H5

119864 HOMO=minus4

696C

T=9272

41

119870H=11222

119879=622010NHA=4NHD=1

046

4

119884 5

R 1=R 2

=OCH3

119864 HOMO=minus4

731C

T=916967

119870H=11469

119879=5994

70N

HA=4NHD=1

044

2

119884 6

R 1=OCH3R2=OC(

CH3) 3

119864 HOMO=minus4

872C

T=928656

119870H=11099

119879=635700NHA=4NHD=1

0109

119884 7

R 1=OCH3R2=CH2NH2

119864 HOMO=minus4

801C

T=934561

119870H=1418

5119879=66

0500NHA=4NHD=2

0122

N

O

O

R 2R 1

NH

2

1198833

R 1=OHR2=meta-NH2

N=11250120583

=5944120596=3392

NHA=5NHD=3MTI

=17128

TVC=223

10minus08

083

8119884 8

R 1=OCH3R2=param

eta-

F119864 H

OMO=minus5

081C

T=1006097

119870H=13267119879

=762370NHA=

6NHD=1

030

8

1198834

R 1=OCH3R2=para-N

H2

N=10850120583

=7114120596

=3295

NHA=5NHD=2MTI

=19138

TVC=204

10minus08

060

7119884 9

R 1=OCH3R2=para

NH2

119864 HOMO=minus4

966C

T=1066417

119870H=16853119879

=8319

3NHA=

5NHD=2

0199

1198835

R 1=OCH3R2=meta-NH2

N=10850120583

=6193120596=3353

NHA=5NHD=2MTI

=18992

TVC=204

10minus08

030

9119884 10

R 1=OCH3R2=metaN

H2

119864 HOMO=minus4

993C

T=1066417

119870H=16853119879

=8319

30N

HA=

5NHD=2

0166

1198836

R 1=OHR2=H

N=78

20120583

=5631120596=3483

NHA=4NHD=2MTI

=15585

TVC=447

10minus08

038

1


N

R1

R2

R3

R4R5

R6

R7

Figure 8




50values than the




50


4 Conclusion



(120596) on pIC50




Competing Interests


Acknowledgments


References


























































Journal of

Chemistry


Advances in

Physical Chemistry



Journal of

Volume 2014














Journal of

Spectroscopy



Journal of


Quantum Chemistry






CatalystsJournal of


Table1Ch

emicalstructurea


ofstu

died

compo

unds

(see

Figure

8)

Num

ber

R 1R 2

R 3R 4

R 5R 6

R 7pIC 50

b

PRO

cAMA

d

1H

NHCO

CH3

HH

HNHCO

CH3

Hminus2

533

minus0653

2H

NHCO

C 2H5

HH

HNHCO

C 2H5

Hminus2

204

minus0886

3H

NHCO

C 3H7

HH

HNHCO

C 3H7

Hminus1669

minus110

74

HNHCO

phH

HH

NHCO

PhH

minus0634

minus0041

5H

NHCO

-p-PhC

lH

HH

NHCO

-p-PhC

lH

mdash0699

6H

NHCO

-p-PhF

HH

HNHCO

-p-PhF

Hminus0

230

1523

7H

NHCO

-p-PhO

Me

HH

HNHCO

-p-PhO

Me

Hminus0

681

minus0041

8H

NHCO

-mp-Ph(OMe)2

HH

HNHCO

-mp-Ph(OMe)2

Hminus1207

0097

9H

NHCO

Me

HH

HNHCO

phH

minus2270

Toxa

10H

NHCO

Me

HH

HNHCO

-p-PhC

lH

minus1061

minus0462

11H

NHCO

Me

HH

HNHCO

-p-PhF

Hminus2

123

0174

12H

NHCO

Me

HH

HNHCO

-p-PhO

Me

Hminus1939

Toxa

13H

NHCO

Me

HH

HNHCO

-mp-Ph(OMe)2

Hmdash

minus0114

14H

HCH3

NH2

OMe

HH

0301

0398

15H

HCH2OH

NH2

OMe

HH

minus0732

minus0613

16H

HCH2Br

NH2

OMe

HH

minus0556

minus0663

17H

H(C

H2) 2OCO

OMe

NH2

OMe

HH

minus0380

minus0114

18H

H(C

H2) 2OCO

(CH2) 2CH3

NH2

OMe

HH

minus0491

minus039

819

HH

(CH2) 2OCO

CH2CH

(CH3) 2

NH2

OMe

HH

minus0342

Toxa

20H

H(C

H2) 2OCO

PhNH2

OMe

HH

0398

0699

21H

H(C

H2) 2OCO

PhF

NH2

OMe

HH

minus0114

0222

23H

H(C

H2) 2OCO

PhOMe

NH2

OMe

HH

minus0279

Toxa

24H

HCH3

ClOMe

HH

minus0041

minus0362

25H

HCH2OH

ClOMe

HH

minus2262

minus0959

26H

HCH2Br

ClOMe

HH

minus1703

minus117

027

HH

(CH2) 2OCO

OMe

ClOMe

HH

minus217

8minus1877

28H

H(C

H2) 2OCO

(CH2) 2CH3

ClOMe

HH

minus1707

minus1628

29H

H(C

H2) 2OCO

CH2CH

(CH3) 2

ClOMe

HH

minus1446

minus1561

30H

H(C

H2) 2OCO

PhCl

OMe

HH

minus213

5To

xa31

HH

(CH2) 2OCO

PhF

ClOMe

HH

minus2295

minus1645

32H

H(C

H2) 2OCO

PhCl

ClOMe

HH

minus219

4minus2

191


Table1Con

tinued

Num

ber

R 1R 2

R 3R 4

R 5R 6

R 7pIC 50

b

PRO

cAMA

d

33H

H(C

H2) 2OCO

PhOMe

ClOMe

HH

minus2098

minus2088

34CH2NH2

HH

HH

HH

minus0230

minus053

135

CH2OH

HH

HH

HH

Toxa

minus1515

36CH2NHCO

(CH2) 3Cl

HH

HH

HH

minus1655

minus1427

37CH2OCO

(CH2) 3Cl

HH

HH

HH

minus2200

minus218

538

CH2NHCO

CH=C

H2

HH

HH

HH

minus2245

minus0833

39CH2OCO

CH=C

H2

HH

HH

HH

minus1464

mdash40

CH2NHCO

PhH

HH

HH

Hminus1654

minus112

141

CH2OCO

PhH

HH

HH

Hminus1885

Toxa

42CH2NHCO

-p-PhF

HH

HH

HH

minus1815

mdash43

CH2OCO

-p-PhF

HH

HH

HH

minus174

1To

xa44

CH2NHCO

-p-PhC

lH

HH

HH

HTo

xaminus1004

45CH2OCO

-p-PhC

lH

HH

HH

Hminus1790

minus1433

46CH2NHCO

-p-PhO

Me

HH

HH

HH

minus1513

minus0973

47CH2OCO

-p-PhO

Me

HH

HH

HH

minus1471

minus0869

48CH2NHCO

-p-PhN

Me 2

HH

HH

HH

minus153

9minus0

672

49CH2OCO

-p-PhN

Me 2

HH

HH

HH

minus1819

Toxa

50CH2NH2

HH

HH

HCH2NH2

minus0820

minus053

151

CH2OH

HH

HH

HCH2OH

Toxa

0222

52CH2NHCO

(CH2) 3Cl

HH

HH

HCH2NHCO

(CH2) 3Cl

minus0663

minus0813

54CH2NHCO

CH=C

H2

HH

HH

HCH2NHCO

CHCH2

minus1061

Toxa

56CH2NHCO

PhH

HH

HH

CH2NHCO

Phminus0

556

minus1236

57CH2OCO

PhH

HH

HH

CH2OCO

PhTo

xaminus0

716

58CH2NHCO

-p-PhF

HH

HH

HCH2NHCO

-p-PhF

minus0756

minus0255

60CH2NHCO

-p-PhC

lH

HH

HH

CH2NHCO

-p-PhC

lmdash

minus1562

62CH2NHCO

-p-PhO

Me

HH

HH

HCH2NHCO

-p-PhO

Me

minus1797

Toxa

63CH2OCO

-p-PhO

Me

HH

HH

HCH2OCO

-p-PhO

Me

Toxa

minus1825

64CH2NHCO

-p-PhN

Me 2

HH

HH

HCH2NHCO

-p-PhN

Me 2

minus0940

minus0724

65CH2OCO

-p-PhN

Me 2

HH

HH

HCH2OCO

-p-PhN

Me 2

Toxa

minus1667

a Toxictoxicity

observed

onhu

man

macroph


thatdidno

tdisp


-p-para-m

-meta

b pIC50=minuslog(

IC50)

c pIC50AMAantileish

manialactivity

againstamastig

otes


Oantileish

manialactivity

againstp

romastig

otes

parasiteform


Inputw

b

w

b

+ +


Output



119894(119883119879 119883)

minus1



119884 = 1198860 +119899

sum119894=1

119886119894119909119894 (1)























Gaussian 03


ChemOffice











1 1 8 37 40 56 1 10 15 16 34 48 50 57 642 2 14 29 34 48 2 25 38 44 47 51 523 3 18 19 3 26 40 564 4 24 25 27 30 42 58 4 7 8 11 13 17 21 465 6 26 5 14 206 7 9 12 21 33 39 41 47 67 10 11 16 31 32 43 45 18 29 588 15 17 23 28 36 49 52 54 27 639 20 38 46 62 24 28 31 35 36 45 60 6510 50 64 32 33 37





+ 3311 10minus05MTI + 3184 104TVC

(3)




+ 2543 10minus02CT minus 0350119870H

minus 1632 10minus02119879 + 0652NHA

+ 1262NHD

(4)










50of compounds from







MLR pIC50













minus05

0

05

1

15

2

25

minus02 03 08 13 18 23

ActivesValidation

ActivesValidation

minus16

minus06

04

14

minus05 0 05 1 15


pIC

50

PRO

Pred (pIC50 PRO)

pIC

50

AM

A

Pred (pIC50 AMA)



























50(PRO)

and pIC50





50










0 02 04 06

minus2

minus15

minus1

minus05

0

05

1

15

2

12

4

7

8

9

11

12

15

16

18

19

20

23

24

25

26

27

28

29

30

3132

3334

36

39

40

41

43

45

46

47

48

49

50

52

54

56

58

62

3

6 10

1417

21

37

38

42

64


Leverage

Stan

dard

ized

resid

uals




50(AMA)







0 01 02 03 04 05

minus4

minus3

minus2

minus1

0

1

2

12

3

47

8

1011

13

14

15

16

17

18

20

21

24

2528

31 32

33

34

35

38

40

45

47

48

5152

56

57

58

60

63

6465

5

6

26

2729

36 37

44

46

50


LeverageSt

anda

rdiz

ed re

sidua

ls






50values as



N

OO

Cl Cl

N

OO

F F

N

HN NH

O O

N N

Me

Me

Me

Me


Compound number 64


minus05

0

05

1

15

2

25

minus05 0 05 1 15 2



minus17

minus12

minus07

minus02

03

08

13

18

23

minus2 minus1 0 1 2


pIC

50

PRO

Pred (pIC50 PRO)

pIC

50

AM

A

Pred (pIC50 AMA)



pIC50PRO

Model 119899 = 51 MSE = 0079 1198772 = 0870 119877 = 0933

Training set [1 3 4 5 6 7 11 12 13 14 16 19 21 25 26 27 28 30 3234 35 36 37 38 39 40 41 43 44 46 47 48 49 50 51] MSE = 0044 1198772 = 0914 119877 = 0956


Model 119899 = 48 MSE = 0104 1198772 = 0918 119877 = 0843

Training set [2 3 4 5 6 7 10 11 13 16 18 19 20 21 23 27 28 2930 31 32 33 34 35 36 37 38 39 41 43 44 45 46 47] MSE = 0060 1198772 = 0897 119877 = 0947



ble8Va

lues

ofdescrip

torsantileish

manialactivity

pIC 50and

leverages(ℎ)

forthe

newdesig

nedcompo

unds

Designedcompo

unds

Num

ber

Descriptorsvalues

pIC 50(PRO

)Num

ber

Descriptorsvalues

pIC 50(A

MA)

N

R 2R 1

NH

2

1198831

R 1=OHR2=CH3

N=12490120583

=2232120596=

3099

NHA=3NHD=2MTI

=3651

TVC=334

10minus05

0571

119884 1

R 1=OCH3R2=CF3

119864 HOMO=minus5

235C

T=885365

119870H=93

02

119879=5814

30N

HA=6NHD=1

1395

119884 2

R 1=OHR2=CF3

119864 HOMO=minus5

312CT

=903801

119870H=12056

119879=647130N

HA=6NHD=2

099

8

119884 3

R 1=OCH3R2=F

119864 HOMO=minus5

034C

T=893897

119870H=10174

119879=566560NHA=4NHD=1

048

5

1198832

R 1=OHR2=OH

N=12380120583

=10

80120596

=3131

NHA=4NHD=3MTI

=3497

TVC=14

910minus05

0170

119884 4

R 1=R 2

=OC 2

H5

119864 HOMO=minus4

696C

T=9272

41

119870H=11222

119879=622010NHA=4NHD=1

046

4

119884 5

R 1=R 2

=OCH3

119864 HOMO=minus4

731C

T=916967

119870H=11469

119879=5994

70N

HA=4NHD=1

044

2

119884 6

R 1=OCH3R2=OC(

CH3) 3

119864 HOMO=minus4

872C

T=928656

119870H=11099

119879=635700NHA=4NHD=1

0109

119884 7

R 1=OCH3R2=CH2NH2

119864 HOMO=minus4

801C

T=934561

119870H=1418

5119879=66

0500NHA=4NHD=2

0122

N

O

O

R 2R 1

NH

2

1198833

R 1=OHR2=meta-NH2

N=11250120583

=5944120596=3392

NHA=5NHD=3MTI

=17128

TVC=223

10minus08

083

8119884 8

R 1=OCH3R2=param

eta-

F119864 H

OMO=minus5

081C

T=1006097

119870H=13267119879

=762370NHA=

6NHD=1

030

8

1198834

R 1=OCH3R2=para-N

H2

N=10850120583

=7114120596

=3295

NHA=5NHD=2MTI

=19138

TVC=204

10minus08

060

7119884 9

R 1=OCH3R2=para

NH2

119864 HOMO=minus4

966C

T=1066417

119870H=16853119879

=8319

3NHA=

5NHD=2

0199

1198835

R 1=OCH3R2=meta-NH2

N=10850120583

=6193120596=3353

NHA=5NHD=2MTI

=18992

TVC=204

10minus08

030

9119884 10

R 1=OCH3R2=metaN

H2

119864 HOMO=minus4

993C

T=1066417

119870H=16853119879

=8319

30N

HA=

5NHD=2

0166

1198836

R 1=OHR2=H

N=78

20120583

=5631120596=3483

NHA=4NHD=2MTI

=15585

TVC=447

10minus08

038

1


N

R1

R2

R3

R4R5

R6

R7

Figure 8




50values than the




50


4 Conclusion



(120596) on pIC50




Competing Interests


Acknowledgments


References


























































Journal of

Chemistry


Advances in

Physical Chemistry



Journal of

Volume 2014














Journal of

Spectroscopy



Journal of


Quantum Chemistry






CatalystsJournal of


Table1Con

tinued

Num

ber

R 1R 2

R 3R 4

R 5R 6

R 7pIC 50

b

PRO

cAMA

d

33H

H(C

H2) 2OCO

PhOMe

ClOMe

HH

minus2098

minus2088

34CH2NH2

HH

HH

HH

minus0230

minus053

135

CH2OH

HH

HH

HH

Toxa

minus1515

36CH2NHCO

(CH2) 3Cl

HH

HH

HH

minus1655

minus1427

37CH2OCO

(CH2) 3Cl

HH

HH

HH

minus2200

minus218

538

CH2NHCO

CH=C

H2

HH

HH

HH

minus2245

minus0833

39CH2OCO

CH=C

H2

HH

HH

HH

minus1464

mdash40

CH2NHCO

PhH

HH

HH

Hminus1654

minus112

141

CH2OCO

PhH

HH

HH

Hminus1885

Toxa

42CH2NHCO

-p-PhF

HH

HH

HH

minus1815

mdash43

CH2OCO

-p-PhF

HH

HH

HH

minus174

1To

xa44

CH2NHCO

-p-PhC

lH

HH

HH

HTo

xaminus1004

45CH2OCO

-p-PhC

lH

HH

HH

Hminus1790

minus1433

46CH2NHCO

-p-PhO

Me

HH

HH

HH

minus1513

minus0973

47CH2OCO

-p-PhO

Me

HH

HH

HH

minus1471

minus0869

48CH2NHCO

-p-PhN

Me 2

HH

HH

HH

minus153

9minus0

672

49CH2OCO

-p-PhN

Me 2

HH

HH

HH

minus1819

Toxa

50CH2NH2

HH

HH

HCH2NH2

minus0820

minus053

151

CH2OH

HH

HH

HCH2OH

Toxa

0222

52CH2NHCO

(CH2) 3Cl

HH

HH

HCH2NHCO

(CH2) 3Cl

minus0663

minus0813

54CH2NHCO

CH=C

H2

HH

HH

HCH2NHCO

CHCH2

minus1061

Toxa

56CH2NHCO

PhH

HH

HH

CH2NHCO

Phminus0

556

minus1236

57CH2OCO

PhH

HH

HH

CH2OCO

PhTo

xaminus0

716

58CH2NHCO

-p-PhF

HH

HH

HCH2NHCO

-p-PhF

minus0756

minus0255

60CH2NHCO

-p-PhC

lH

HH

HH

CH2NHCO

-p-PhC

lmdash

minus1562

62CH2NHCO

-p-PhO

Me

HH

HH

HCH2NHCO

-p-PhO

Me

minus1797

Toxa

63CH2OCO

-p-PhO

Me

HH

HH

HCH2OCO

-p-PhO

Me

Toxa

minus1825

64CH2NHCO

-p-PhN

Me 2

HH

HH

HCH2NHCO

-p-PhN

Me 2

minus0940

minus0724

65CH2OCO

-p-PhN

Me 2

HH

HH

HCH2OCO

-p-PhN

Me 2

Toxa

minus1667

a Toxictoxicity

observed

onhu

man

macroph


thatdidno

tdisp


-p-para-m

-meta

b pIC50=minuslog(

IC50)

c pIC50AMAantileish

manialactivity

againstamastig

otes


Oantileish

manialactivity

againstp

romastig

otes

parasiteform


Inputw

b

w

b

+ +


Output



119894(119883119879 119883)

minus1



119884 = 1198860 +119899

sum119894=1

119886119894119909119894 (1)























Gaussian 03


ChemOffice











1 1 8 37 40 56 1 10 15 16 34 48 50 57 642 2 14 29 34 48 2 25 38 44 47 51 523 3 18 19 3 26 40 564 4 24 25 27 30 42 58 4 7 8 11 13 17 21 465 6 26 5 14 206 7 9 12 21 33 39 41 47 67 10 11 16 31 32 43 45 18 29 588 15 17 23 28 36 49 52 54 27 639 20 38 46 62 24 28 31 35 36 45 60 6510 50 64 32 33 37





+ 3311 10minus05MTI + 3184 104TVC

(3)




+ 2543 10minus02CT minus 0350119870H

minus 1632 10minus02119879 + 0652NHA

+ 1262NHD

(4)










50of compounds from







MLR pIC50













minus05

0

05

1

15

2

25

minus02 03 08 13 18 23

ActivesValidation

ActivesValidation

minus16

minus06

04

14

minus05 0 05 1 15


pIC

50

PRO

Pred (pIC50 PRO)

pIC

50

AM

A

Pred (pIC50 AMA)



























50(PRO)

and pIC50





50










0 02 04 06

minus2

minus15

minus1

minus05

0

05

1

15

2

12

4

7

8

9

11

12

15

16

18

19

20

23

24

25

26

27

28

29

30

3132

3334

36

39

40

41

43

45

46

47

48

49

50

52

54

56

58

62

3

6 10

1417

21

37

38

42

64


Leverage

Stan

dard

ized

resid

uals




50(AMA)







0 01 02 03 04 05

minus4

minus3

minus2

minus1

0

1

2

12

3

47

8

1011

13

14

15

16

17

18

20

21

24

2528

31 32

33

34

35

38

40

45

47

48

5152

56

57

58

60

63

6465

5

6

26

2729

36 37

44

46

50


LeverageSt

anda

rdiz

ed re

sidua

ls






50values as



N

OO

Cl Cl

N

OO

F F

N

HN NH

O O

N N

Me

Me

Me

Me


Compound number 64


minus05

0

05

1

15

2

25

minus05 0 05 1 15 2



minus17

minus12

minus07

minus02

03

08

13

18

23

minus2 minus1 0 1 2


pIC

50

PRO

Pred (pIC50 PRO)

pIC

50

AM

A

Pred (pIC50 AMA)



pIC50PRO

Model 119899 = 51 MSE = 0079 1198772 = 0870 119877 = 0933

Training set [1 3 4 5 6 7 11 12 13 14 16 19 21 25 26 27 28 30 3234 35 36 37 38 39 40 41 43 44 46 47 48 49 50 51] MSE = 0044 1198772 = 0914 119877 = 0956


Model 119899 = 48 MSE = 0104 1198772 = 0918 119877 = 0843

Training set [2 3 4 5 6 7 10 11 13 16 18 19 20 21 23 27 28 2930 31 32 33 34 35 36 37 38 39 41 43 44 45 46 47] MSE = 0060 1198772 = 0897 119877 = 0947



ble8Va

lues

ofdescrip

torsantileish

manialactivity

pIC 50and

leverages(ℎ)

forthe

newdesig

nedcompo

unds

Designedcompo

unds

Num

ber

Descriptorsvalues

pIC 50(PRO

)Num

ber

Descriptorsvalues

pIC 50(A

MA)

N

R 2R 1

NH

2

1198831

R 1=OHR2=CH3

N=12490120583

=2232120596=

3099

NHA=3NHD=2MTI

=3651

TVC=334

10minus05

0571

119884 1

R 1=OCH3R2=CF3

119864 HOMO=minus5

235C

T=885365

119870H=93

02

119879=5814

30N

HA=6NHD=1

1395

119884 2

R 1=OHR2=CF3

119864 HOMO=minus5

312CT

=903801

119870H=12056

119879=647130N

HA=6NHD=2

099

8

119884 3

R 1=OCH3R2=F

119864 HOMO=minus5

034C

T=893897

119870H=10174

119879=566560NHA=4NHD=1

048

5

1198832

R 1=OHR2=OH

N=12380120583

=10

80120596

=3131

NHA=4NHD=3MTI

=3497

TVC=14

910minus05

0170

119884 4

R 1=R 2

=OC 2

H5

119864 HOMO=minus4

696C

T=9272

41

119870H=11222

119879=622010NHA=4NHD=1

046

4

119884 5

R 1=R 2

=OCH3

119864 HOMO=minus4

731C

T=916967

119870H=11469

119879=5994

70N

HA=4NHD=1

044

2

119884 6

R 1=OCH3R2=OC(

CH3) 3

119864 HOMO=minus4

872C

T=928656

119870H=11099

119879=635700NHA=4NHD=1

0109

119884 7

R 1=OCH3R2=CH2NH2

119864 HOMO=minus4

801C

T=934561

119870H=1418

5119879=66

0500NHA=4NHD=2

0122

N

O

O

R 2R 1

NH

2

1198833

R 1=OHR2=meta-NH2

N=11250120583

=5944120596=3392

NHA=5NHD=3MTI

=17128

TVC=223

10minus08

083

8119884 8

R 1=OCH3R2=param

eta-

F119864 H

OMO=minus5

081C

T=1006097

119870H=13267119879

=762370NHA=

6NHD=1

030

8

1198834

R 1=OCH3R2=para-N

H2

N=10850120583

=7114120596

=3295

NHA=5NHD=2MTI

=19138

TVC=204

10minus08

060

7119884 9

R 1=OCH3R2=para

NH2

119864 HOMO=minus4

966C

T=1066417

119870H=16853119879

=8319

3NHA=

5NHD=2

0199

1198835

R 1=OCH3R2=meta-NH2

N=10850120583

=6193120596=3353

NHA=5NHD=2MTI

=18992

TVC=204

10minus08

030

9119884 10

R 1=OCH3R2=metaN

H2

119864 HOMO=minus4

993C

T=1066417

119870H=16853119879

=8319

30N

HA=

5NHD=2

0166

1198836

R 1=OHR2=H

N=78

20120583

=5631120596=3483

NHA=4NHD=2MTI

=15585

TVC=447

10minus08

038

1


N

R1

R2

R3

R4R5

R6

R7

Figure 8




50values than the




50


4 Conclusion



(120596) on pIC50




Competing Interests


Acknowledgments


References


























































Journal of

Chemistry


Advances in

Physical Chemistry



Journal of

Volume 2014














Journal of

Spectroscopy



Journal of


Quantum Chemistry






CatalystsJournal of


Inputw

b

w

b

+ +


Output



119894(119883119879 119883)

minus1



119884 = 1198860 +119899

sum119894=1

119886119894119909119894 (1)























Gaussian 03


ChemOffice











1 1 8 37 40 56 1 10 15 16 34 48 50 57 642 2 14 29 34 48 2 25 38 44 47 51 523 3 18 19 3 26 40 564 4 24 25 27 30 42 58 4 7 8 11 13 17 21 465 6 26 5 14 206 7 9 12 21 33 39 41 47 67 10 11 16 31 32 43 45 18 29 588 15 17 23 28 36 49 52 54 27 639 20 38 46 62 24 28 31 35 36 45 60 6510 50 64 32 33 37





+ 3311 10minus05MTI + 3184 104TVC

(3)




+ 2543 10minus02CT minus 0350119870H

minus 1632 10minus02119879 + 0652NHA

+ 1262NHD

(4)










50of compounds from







MLR pIC50













minus05

0

05

1

15

2

25

minus02 03 08 13 18 23

ActivesValidation

ActivesValidation

minus16

minus06

04

14

minus05 0 05 1 15


pIC

50

PRO

Pred (pIC50 PRO)

pIC

50

AM

A

Pred (pIC50 AMA)



























50(PRO)

and pIC50





50










0 02 04 06

minus2

minus15

minus1

minus05

0

05

1

15

2

12

4

7

8

9

11

12

15

16

18

19

20

23

24

25

26

27

28

29

30

3132

3334

36

39

40

41

43

45

46

47

48

49

50

52

54

56

58

62

3

6 10

1417

21

37

38

42

64


Leverage

Stan

dard

ized

resid

uals




50(AMA)







0 01 02 03 04 05

minus4

minus3

minus2

minus1

0

1

2

12

3

47

8

1011

13

14

15

16

17

18

20

21

24

2528

31 32

33

34

35

38

40

45

47

48

5152

56

57

58

60

63

6465

5

6

26

2729

36 37

44

46

50


LeverageSt

anda

rdiz

ed re

sidua

ls






50values as



N

OO

Cl Cl

N

OO

F F

N

HN NH

O O

N N

Me

Me

Me

Me


Compound number 64


minus05

0

05

1

15

2

25

minus05 0 05 1 15 2



minus17

minus12

minus07

minus02

03

08

13

18

23

minus2 minus1 0 1 2


pIC

50

PRO

Pred (pIC50 PRO)

pIC

50

AM

A

Pred (pIC50 AMA)



pIC50PRO

Model 119899 = 51 MSE = 0079 1198772 = 0870 119877 = 0933

Training set [1 3 4 5 6 7 11 12 13 14 16 19 21 25 26 27 28 30 3234 35 36 37 38 39 40 41 43 44 46 47 48 49 50 51] MSE = 0044 1198772 = 0914 119877 = 0956


Model 119899 = 48 MSE = 0104 1198772 = 0918 119877 = 0843

Training set [2 3 4 5 6 7 10 11 13 16 18 19 20 21 23 27 28 2930 31 32 33 34 35 36 37 38 39 41 43 44 45 46 47] MSE = 0060 1198772 = 0897 119877 = 0947



ble8Va

lues

ofdescrip

torsantileish

manialactivity

pIC 50and

leverages(ℎ)

forthe

newdesig

nedcompo

unds

Designedcompo

unds

Num

ber

Descriptorsvalues

pIC 50(PRO

)Num

ber

Descriptorsvalues

pIC 50(A

MA)

N

R 2R 1

NH

2

1198831

R 1=OHR2=CH3

N=12490120583

=2232120596=

3099

NHA=3NHD=2MTI

=3651

TVC=334

10minus05

0571

119884 1

R 1=OCH3R2=CF3

119864 HOMO=minus5

235C

T=885365

119870H=93

02

119879=5814

30N

HA=6NHD=1

1395

119884 2

R 1=OHR2=CF3

119864 HOMO=minus5

312CT

=903801

119870H=12056

119879=647130N

HA=6NHD=2

099

8

119884 3

R 1=OCH3R2=F

119864 HOMO=minus5

034C

T=893897

119870H=10174

119879=566560NHA=4NHD=1

048

5

1198832

R 1=OHR2=OH

N=12380120583

=10

80120596

=3131

NHA=4NHD=3MTI

=3497

TVC=14

910minus05

0170

119884 4

R 1=R 2

=OC 2

H5

119864 HOMO=minus4

696C

T=9272

41

119870H=11222

119879=622010NHA=4NHD=1

046

4

119884 5

R 1=R 2

=OCH3

119864 HOMO=minus4

731C

T=916967

119870H=11469

119879=5994

70N

HA=4NHD=1

044

2

119884 6

R 1=OCH3R2=OC(

CH3) 3

119864 HOMO=minus4

872C

T=928656

119870H=11099

119879=635700NHA=4NHD=1

0109

119884 7

R 1=OCH3R2=CH2NH2

119864 HOMO=minus4

801C

T=934561

119870H=1418

5119879=66

0500NHA=4NHD=2

0122

N

O

O

R 2R 1

NH

2

1198833

R 1=OHR2=meta-NH2

N=11250120583

=5944120596=3392

NHA=5NHD=3MTI

=17128

TVC=223

10minus08

083

8119884 8

R 1=OCH3R2=param

eta-

F119864 H

OMO=minus5

081C

T=1006097

119870H=13267119879

=762370NHA=

6NHD=1

030

8

1198834

R 1=OCH3R2=para-N

H2

N=10850120583

=7114120596

=3295

NHA=5NHD=2MTI

=19138

TVC=204

10minus08

060

7119884 9

R 1=OCH3R2=para

NH2

119864 HOMO=minus4

966C

T=1066417

119870H=16853119879

=8319

3NHA=

5NHD=2

0199

1198835

R 1=OCH3R2=meta-NH2

N=10850120583

=6193120596=3353

NHA=5NHD=2MTI

=18992

TVC=204

10minus08

030

9119884 10

R 1=OCH3R2=metaN

H2

119864 HOMO=minus4

993C

T=1066417

119870H=16853119879

=8319

30N

HA=

5NHD=2

0166

1198836

R 1=OHR2=H

N=78

20120583

=5631120596=3483

NHA=4NHD=2MTI

=15585

TVC=447

10minus08

038

1


N

R1

R2

R3

R4R5

R6

R7

Figure 8




50values than the




50


4 Conclusion



(120596) on pIC50




Competing Interests


Acknowledgments


References


























































Journal of

Chemistry


Advances in

Physical Chemistry



Journal of

Volume 2014














Journal of

Spectroscopy



Journal of


Quantum Chemistry






CatalystsJournal of








Gaussian 03


ChemOffice











1 1 8 37 40 56 1 10 15 16 34 48 50 57 642 2 14 29 34 48 2 25 38 44 47 51 523 3 18 19 3 26 40 564 4 24 25 27 30 42 58 4 7 8 11 13 17 21 465 6 26 5 14 206 7 9 12 21 33 39 41 47 67 10 11 16 31 32 43 45 18 29 588 15 17 23 28 36 49 52 54 27 639 20 38 46 62 24 28 31 35 36 45 60 6510 50 64 32 33 37





+ 3311 10minus05MTI + 3184 104TVC

(3)




+ 2543 10minus02CT minus 0350119870H

minus 1632 10minus02119879 + 0652NHA

+ 1262NHD

(4)










50of compounds from







MLR pIC50













minus05

0

05

1

15

2

25

minus02 03 08 13 18 23

ActivesValidation

ActivesValidation

minus16

minus06

04

14

minus05 0 05 1 15


pIC

50

PRO

Pred (pIC50 PRO)

pIC

50

AM

A

Pred (pIC50 AMA)



























50(PRO)

and pIC50





50










0 02 04 06

minus2

minus15

minus1

minus05

0

05

1

15

2

12

4

7

8

9

11

12

15

16

18

19

20

23

24

25

26

27

28

29

30

3132

3334

36

39

40

41

43

45

46

47

48

49

50

52

54

56

58

62

3

6 10

1417

21

37

38

42

64


Leverage

Stan

dard

ized

resid

uals




50(AMA)







0 01 02 03 04 05

minus4

minus3

minus2

minus1

0

1

2

12

3

47

8

1011

13

14

15

16

17

18

20

21

24

2528

31 32

33

34

35

38

40

45

47

48

5152

56

57

58

60

63

6465

5

6

26

2729

36 37

44

46

50


LeverageSt

anda

rdiz

ed re

sidua

ls






50values as



N

OO

Cl Cl

N

OO

F F

N

HN NH

O O

N N

Me

Me

Me

Me


Compound number 64


minus05

0

05

1

15

2

25

minus05 0 05 1 15 2



minus17

minus12

minus07

minus02

03

08

13

18

23

minus2 minus1 0 1 2


pIC

50

PRO

Pred (pIC50 PRO)

pIC

50

AM

A

Pred (pIC50 AMA)



pIC50PRO

Model 119899 = 51 MSE = 0079 1198772 = 0870 119877 = 0933

Training set [1 3 4 5 6 7 11 12 13 14 16 19 21 25 26 27 28 30 3234 35 36 37 38 39 40 41 43 44 46 47 48 49 50 51] MSE = 0044 1198772 = 0914 119877 = 0956


Model 119899 = 48 MSE = 0104 1198772 = 0918 119877 = 0843

Training set [2 3 4 5 6 7 10 11 13 16 18 19 20 21 23 27 28 2930 31 32 33 34 35 36 37 38 39 41 43 44 45 46 47] MSE = 0060 1198772 = 0897 119877 = 0947



ble8Va

lues

ofdescrip

torsantileish

manialactivity

pIC 50and

leverages(ℎ)

forthe

newdesig

nedcompo

unds

Designedcompo

unds

Num

ber

Descriptorsvalues

pIC 50(PRO

)Num

ber

Descriptorsvalues

pIC 50(A

MA)

N

R 2R 1

NH

2

1198831

R 1=OHR2=CH3

N=12490120583

=2232120596=

3099

NHA=3NHD=2MTI

=3651

TVC=334

10minus05

0571

119884 1

R 1=OCH3R2=CF3

119864 HOMO=minus5

235C

T=885365

119870H=93

02

119879=5814

30N

HA=6NHD=1

1395

119884 2

R 1=OHR2=CF3

119864 HOMO=minus5

312CT

=903801

119870H=12056

119879=647130N

HA=6NHD=2

099

8

119884 3

R 1=OCH3R2=F

119864 HOMO=minus5

034C

T=893897

119870H=10174

119879=566560NHA=4NHD=1

048

5

1198832

R 1=OHR2=OH

N=12380120583

=10

80120596

=3131

NHA=4NHD=3MTI

=3497

TVC=14

910minus05

0170

119884 4

R 1=R 2

=OC 2

H5

119864 HOMO=minus4

696C

T=9272

41

119870H=11222

119879=622010NHA=4NHD=1

046

4

119884 5

R 1=R 2

=OCH3

119864 HOMO=minus4

731C

T=916967

119870H=11469

119879=5994

70N

HA=4NHD=1

044

2

119884 6

R 1=OCH3R2=OC(

CH3) 3

119864 HOMO=minus4

872C

T=928656

119870H=11099

119879=635700NHA=4NHD=1

0109

119884 7

R 1=OCH3R2=CH2NH2

119864 HOMO=minus4

801C

T=934561

119870H=1418

5119879=66

0500NHA=4NHD=2

0122

N

O

O

R 2R 1

NH

2

1198833

R 1=OHR2=meta-NH2

N=11250120583

=5944120596=3392

NHA=5NHD=3MTI

=17128

TVC=223

10minus08

083

8119884 8

R 1=OCH3R2=param

eta-

F119864 H

OMO=minus5

081C

T=1006097

119870H=13267119879

=762370NHA=

6NHD=1

030

8

1198834

R 1=OCH3R2=para-N

H2

N=10850120583

=7114120596

=3295

NHA=5NHD=2MTI

=19138

TVC=204

10minus08

060

7119884 9

R 1=OCH3R2=para

NH2

119864 HOMO=minus4

966C

T=1066417

119870H=16853119879

=8319

3NHA=

5NHD=2

0199

1198835

R 1=OCH3R2=meta-NH2

N=10850120583

=6193120596=3353

NHA=5NHD=2MTI

=18992

TVC=204

10minus08

030

9119884 10

R 1=OCH3R2=metaN

H2

119864 HOMO=minus4

993C

T=1066417

119870H=16853119879

=8319

30N

HA=

5NHD=2

0166

1198836

R 1=OHR2=H

N=78

20120583

=5631120596=3483

NHA=4NHD=2MTI

=15585

TVC=447

10minus08

038

1


N

R1

R2

R3

R4R5

R6

R7

Figure 8




50values than the




50


4 Conclusion



(120596) on pIC50




Competing Interests


Acknowledgments


References


























































Journal of

Chemistry


Advances in

Physical Chemistry



Journal of

Volume 2014














Journal of

Spectroscopy



Journal of


Quantum Chemistry






CatalystsJournal of





1 1 8 37 40 56 1 10 15 16 34 48 50 57 642 2 14 29 34 48 2 25 38 44 47 51 523 3 18 19 3 26 40 564 4 24 25 27 30 42 58 4 7 8 11 13 17 21 465 6 26 5 14 206 7 9 12 21 33 39 41 47 67 10 11 16 31 32 43 45 18 29 588 15 17 23 28 36 49 52 54 27 639 20 38 46 62 24 28 31 35 36 45 60 6510 50 64 32 33 37





+ 3311 10minus05MTI + 3184 104TVC

(3)




+ 2543 10minus02CT minus 0350119870H

minus 1632 10minus02119879 + 0652NHA

+ 1262NHD

(4)










50of compounds from







MLR pIC50













minus05

0

05

1

15

2

25

minus02 03 08 13 18 23

ActivesValidation

ActivesValidation

minus16

minus06

04

14

minus05 0 05 1 15


pIC

50

PRO

Pred (pIC50 PRO)

pIC

50

AM

A

Pred (pIC50 AMA)



























50(PRO)

and pIC50





50










0 02 04 06

minus2

minus15

minus1

minus05

0

05

1

15

2

12

4

7

8

9

11

12

15

16

18

19

20

23

24

25

26

27

28

29

30

3132

3334

36

39

40

41

43

45

46

47

48

49

50

52

54

56

58

62

3

6 10

1417

21

37

38

42

64


Leverage

Stan

dard

ized

resid

uals




50(AMA)







0 01 02 03 04 05

minus4

minus3

minus2

minus1

0

1

2

12

3

47

8

1011

13

14

15

16

17

18

20

21

24

2528

31 32

33

34

35

38

40

45

47

48

5152

56

57

58

60

63

6465

5

6

26

2729

36 37

44

46

50


LeverageSt

anda

rdiz

ed re

sidua

ls






50values as



N

OO

Cl Cl

N

OO

F F

N

HN NH

O O

N N

Me

Me

Me

Me


Compound number 64


minus05

0

05

1

15

2

25

minus05 0 05 1 15 2



minus17

minus12

minus07

minus02

03

08

13

18

23

minus2 minus1 0 1 2


pIC

50

PRO

Pred (pIC50 PRO)

pIC

50

AM

A

Pred (pIC50 AMA)



pIC50PRO

Model 119899 = 51 MSE = 0079 1198772 = 0870 119877 = 0933

Training set [1 3 4 5 6 7 11 12 13 14 16 19 21 25 26 27 28 30 3234 35 36 37 38 39 40 41 43 44 46 47 48 49 50 51] MSE = 0044 1198772 = 0914 119877 = 0956


Model 119899 = 48 MSE = 0104 1198772 = 0918 119877 = 0843

Training set [2 3 4 5 6 7 10 11 13 16 18 19 20 21 23 27 28 2930 31 32 33 34 35 36 37 38 39 41 43 44 45 46 47] MSE = 0060 1198772 = 0897 119877 = 0947



ble8Va

lues

ofdescrip

torsantileish

manialactivity

pIC 50and

leverages(ℎ)

forthe

newdesig

nedcompo

unds

Designedcompo

unds

Num

ber

Descriptorsvalues

pIC 50(PRO

)Num

ber

Descriptorsvalues

pIC 50(A

MA)

N

R 2R 1

NH

2

1198831

R 1=OHR2=CH3

N=12490120583

=2232120596=

3099

NHA=3NHD=2MTI

=3651

TVC=334

10minus05

0571

119884 1

R 1=OCH3R2=CF3

119864 HOMO=minus5

235C

T=885365

119870H=93

02

119879=5814

30N

HA=6NHD=1

1395

119884 2

R 1=OHR2=CF3

119864 HOMO=minus5

312CT

=903801

119870H=12056

119879=647130N

HA=6NHD=2

099

8

119884 3

R 1=OCH3R2=F

119864 HOMO=minus5

034C

T=893897

119870H=10174

119879=566560NHA=4NHD=1

048

5

1198832

R 1=OHR2=OH

N=12380120583

=10

80120596

=3131

NHA=4NHD=3MTI

=3497

TVC=14

910minus05

0170

119884 4

R 1=R 2

=OC 2

H5

119864 HOMO=minus4

696C

T=9272

41

119870H=11222

119879=622010NHA=4NHD=1

046

4

119884 5

R 1=R 2

=OCH3

119864 HOMO=minus4

731C

T=916967

119870H=11469

119879=5994

70N

HA=4NHD=1

044

2

119884 6

R 1=OCH3R2=OC(

CH3) 3

119864 HOMO=minus4

872C

T=928656

119870H=11099

119879=635700NHA=4NHD=1

0109

119884 7

R 1=OCH3R2=CH2NH2

119864 HOMO=minus4

801C

T=934561

119870H=1418

5119879=66

0500NHA=4NHD=2

0122

N

O

O

R 2R 1

NH

2

1198833

R 1=OHR2=meta-NH2

N=11250120583

=5944120596=3392

NHA=5NHD=3MTI

=17128

TVC=223

10minus08

083

8119884 8

R 1=OCH3R2=param

eta-

F119864 H

OMO=minus5

081C

T=1006097

119870H=13267119879

=762370NHA=

6NHD=1

030

8

1198834

R 1=OCH3R2=para-N

H2

N=10850120583

=7114120596

=3295

NHA=5NHD=2MTI

=19138

TVC=204

10minus08

060

7119884 9

R 1=OCH3R2=para

NH2

119864 HOMO=minus4

966C

T=1066417

119870H=16853119879

=8319

3NHA=

5NHD=2

0199

1198835

R 1=OCH3R2=meta-NH2

N=10850120583

=6193120596=3353

NHA=5NHD=2MTI

=18992

TVC=204

10minus08

030

9119884 10

R 1=OCH3R2=metaN

H2

119864 HOMO=minus4

993C

T=1066417

119870H=16853119879

=8319

30N

HA=

5NHD=2

0166

1198836

R 1=OHR2=H

N=78

20120583

=5631120596=3483

NHA=4NHD=2MTI

=15585

TVC=447

10minus08

038

1


N

R1

R2

R3

R4R5

R6

R7

Figure 8




50values than the




50


4 Conclusion



(120596) on pIC50




Competing Interests


Acknowledgments


References


























































Journal of

Chemistry


Advances in

Physical Chemistry



Journal of

Volume 2014














Journal of

Spectroscopy



Journal of


Quantum Chemistry






CatalystsJournal of







minus05

0

05

1

15

2

25

minus02 03 08 13 18 23

ActivesValidation

ActivesValidation

minus16

minus06

04

14

minus05 0 05 1 15


pIC

50

PRO

Pred (pIC50 PRO)

pIC

50

AM

A

Pred (pIC50 AMA)



























50(PRO)

and pIC50





50










0 02 04 06

minus2

minus15

minus1

minus05

0

05

1

15

2

12

4

7

8

9

11

12

15

16

18

19

20

23

24

25

26

27

28

29

30

3132

3334

36

39

40

41

43

45

46

47

48

49

50

52

54

56

58

62

3

6 10

1417

21

37

38

42

64


Leverage

Stan

dard

ized

resid

uals




50(AMA)







0 01 02 03 04 05

minus4

minus3

minus2

minus1

0

1

2

12

3

47

8

1011

13

14

15

16

17

18

20

21

24

2528

31 32

33

34

35

38

40

45

47

48

5152

56

57

58

60

63

6465

5

6

26

2729

36 37

44

46

50


LeverageSt

anda

rdiz

ed re

sidua

ls






50values as



N

OO

Cl Cl

N

OO

F F

N

HN NH

O O

N N

Me

Me

Me

Me


Compound number 64


minus05

0

05

1

15

2

25

minus05 0 05 1 15 2



minus17

minus12

minus07

minus02

03

08

13

18

23

minus2 minus1 0 1 2


pIC

50

PRO

Pred (pIC50 PRO)

pIC

50

AM

A

Pred (pIC50 AMA)



pIC50PRO

Model 119899 = 51 MSE = 0079 1198772 = 0870 119877 = 0933

Training set [1 3 4 5 6 7 11 12 13 14 16 19 21 25 26 27 28 30 3234 35 36 37 38 39 40 41 43 44 46 47 48 49 50 51] MSE = 0044 1198772 = 0914 119877 = 0956


Model 119899 = 48 MSE = 0104 1198772 = 0918 119877 = 0843

Training set [2 3 4 5 6 7 10 11 13 16 18 19 20 21 23 27 28 2930 31 32 33 34 35 36 37 38 39 41 43 44 45 46 47] MSE = 0060 1198772 = 0897 119877 = 0947



ble8Va

lues

ofdescrip

torsantileish

manialactivity

pIC 50and

leverages(ℎ)

forthe

newdesig

nedcompo

unds

Designedcompo

unds

Num

ber

Descriptorsvalues

pIC 50(PRO

)Num

ber

Descriptorsvalues

pIC 50(A

MA)

N

R 2R 1

NH

2

1198831

R 1=OHR2=CH3

N=12490120583

=2232120596=

3099

NHA=3NHD=2MTI

=3651

TVC=334

10minus05

0571

119884 1

R 1=OCH3R2=CF3

119864 HOMO=minus5

235C

T=885365

119870H=93

02

119879=5814

30N

HA=6NHD=1

1395

119884 2

R 1=OHR2=CF3

119864 HOMO=minus5

312CT

=903801

119870H=12056

119879=647130N

HA=6NHD=2

099

8

119884 3

R 1=OCH3R2=F

119864 HOMO=minus5

034C

T=893897

119870H=10174

119879=566560NHA=4NHD=1

048

5

1198832

R 1=OHR2=OH

N=12380120583

=10

80120596

=3131

NHA=4NHD=3MTI

=3497

TVC=14

910minus05

0170

119884 4

R 1=R 2

=OC 2

H5

119864 HOMO=minus4

696C

T=9272

41

119870H=11222

119879=622010NHA=4NHD=1

046

4

119884 5

R 1=R 2

=OCH3

119864 HOMO=minus4

731C

T=916967

119870H=11469

119879=5994

70N

HA=4NHD=1

044

2

119884 6

R 1=OCH3R2=OC(

CH3) 3

119864 HOMO=minus4

872C

T=928656

119870H=11099

119879=635700NHA=4NHD=1

0109

119884 7

R 1=OCH3R2=CH2NH2

119864 HOMO=minus4

801C

T=934561

119870H=1418

5119879=66

0500NHA=4NHD=2

0122

N

O

O

R 2R 1

NH

2

1198833

R 1=OHR2=meta-NH2

N=11250120583

=5944120596=3392

NHA=5NHD=3MTI

=17128

TVC=223

10minus08

083

8119884 8

R 1=OCH3R2=param

eta-

F119864 H

OMO=minus5

081C

T=1006097

119870H=13267119879

=762370NHA=

6NHD=1

030

8

1198834

R 1=OCH3R2=para-N

H2

N=10850120583

=7114120596

=3295

NHA=5NHD=2MTI

=19138

TVC=204

10minus08

060

7119884 9

R 1=OCH3R2=para

NH2

119864 HOMO=minus4

966C

T=1066417

119870H=16853119879

=8319

3NHA=

5NHD=2

0199

1198835

R 1=OCH3R2=meta-NH2

N=10850120583

=6193120596=3353

NHA=5NHD=2MTI

=18992

TVC=204

10minus08

030

9119884 10

R 1=OCH3R2=metaN

H2

119864 HOMO=minus4

993C

T=1066417

119870H=16853119879

=8319

30N

HA=

5NHD=2

0166

1198836

R 1=OHR2=H

N=78

20120583

=5631120596=3483

NHA=4NHD=2MTI

=15585

TVC=447

10minus08

038

1


N

R1

R2

R3

R4R5

R6

R7

Figure 8




50values than the




50


4 Conclusion



(120596) on pIC50




Competing Interests


Acknowledgments


References


























































Journal of

Chemistry


Advances in

Physical Chemistry



Journal of

Volume 2014














Journal of

Spectroscopy



Journal of


Quantum Chemistry






CatalystsJournal of






















50(PRO)

and pIC50





50










0 02 04 06

minus2

minus15

minus1

minus05

0

05

1

15

2

12

4

7

8

9

11

12

15

16

18

19

20

23

24

25

26

27

28

29

30

3132

3334

36

39

40

41

43

45

46

47

48

49

50

52

54

56

58

62

3

6 10

1417

21

37

38

42

64


Leverage

Stan

dard

ized

resid

uals




50(AMA)







0 01 02 03 04 05

minus4

minus3

minus2

minus1

0

1

2

12

3

47

8

1011

13

14

15

16

17

18

20

21

24

2528

31 32

33

34

35

38

40

45

47

48

5152

56

57

58

60

63

6465

5

6

26

2729

36 37

44

46

50


LeverageSt

anda

rdiz

ed re

sidua

ls






50values as



N

OO

Cl Cl

N

OO

F F

N

HN NH

O O

N N

Me

Me

Me

Me


Compound number 64


minus05

0

05

1

15

2

25

minus05 0 05 1 15 2



minus17

minus12

minus07

minus02

03

08

13

18

23

minus2 minus1 0 1 2


pIC

50

PRO

Pred (pIC50 PRO)

pIC

50

AM

A

Pred (pIC50 AMA)



pIC50PRO

Model 119899 = 51 MSE = 0079 1198772 = 0870 119877 = 0933

Training set [1 3 4 5 6 7 11 12 13 14 16 19 21 25 26 27 28 30 3234 35 36 37 38 39 40 41 43 44 46 47 48 49 50 51] MSE = 0044 1198772 = 0914 119877 = 0956


Model 119899 = 48 MSE = 0104 1198772 = 0918 119877 = 0843

Training set [2 3 4 5 6 7 10 11 13 16 18 19 20 21 23 27 28 2930 31 32 33 34 35 36 37 38 39 41 43 44 45 46 47] MSE = 0060 1198772 = 0897 119877 = 0947



ble8Va

lues

ofdescrip

torsantileish

manialactivity

pIC 50and

leverages(ℎ)

forthe

newdesig

nedcompo

unds

Designedcompo

unds

Num

ber

Descriptorsvalues

pIC 50(PRO

)Num

ber

Descriptorsvalues

pIC 50(A

MA)

N

R 2R 1

NH

2

1198831

R 1=OHR2=CH3

N=12490120583

=2232120596=

3099

NHA=3NHD=2MTI

=3651

TVC=334

10minus05

0571

119884 1

R 1=OCH3R2=CF3

119864 HOMO=minus5

235C

T=885365

119870H=93

02

119879=5814

30N

HA=6NHD=1

1395

119884 2

R 1=OHR2=CF3

119864 HOMO=minus5

312CT

=903801

119870H=12056

119879=647130N

HA=6NHD=2

099

8

119884 3

R 1=OCH3R2=F

119864 HOMO=minus5

034C

T=893897

119870H=10174

119879=566560NHA=4NHD=1

048

5

1198832

R 1=OHR2=OH

N=12380120583

=10

80120596

=3131

NHA=4NHD=3MTI

=3497

TVC=14

910minus05

0170

119884 4

R 1=R 2

=OC 2

H5

119864 HOMO=minus4

696C

T=9272

41

119870H=11222

119879=622010NHA=4NHD=1

046

4

119884 5

R 1=R 2

=OCH3

119864 HOMO=minus4

731C

T=916967

119870H=11469

119879=5994

70N

HA=4NHD=1

044

2

119884 6

R 1=OCH3R2=OC(

CH3) 3

119864 HOMO=minus4

872C

T=928656

119870H=11099

119879=635700NHA=4NHD=1

0109

119884 7

R 1=OCH3R2=CH2NH2

119864 HOMO=minus4

801C

T=934561

119870H=1418

5119879=66

0500NHA=4NHD=2

0122

N

O

O

R 2R 1

NH

2

1198833

R 1=OHR2=meta-NH2

N=11250120583

=5944120596=3392

NHA=5NHD=3MTI

=17128

TVC=223

10minus08

083

8119884 8

R 1=OCH3R2=param

eta-

F119864 H

OMO=minus5

081C

T=1006097

119870H=13267119879

=762370NHA=

6NHD=1

030

8

1198834

R 1=OCH3R2=para-N

H2

N=10850120583

=7114120596

=3295

NHA=5NHD=2MTI

=19138

TVC=204

10minus08

060

7119884 9

R 1=OCH3R2=para

NH2

119864 HOMO=minus4

966C

T=1066417

119870H=16853119879

=8319

3NHA=

5NHD=2

0199

1198835

R 1=OCH3R2=meta-NH2

N=10850120583

=6193120596=3353

NHA=5NHD=2MTI

=18992

TVC=204

10minus08

030

9119884 10

R 1=OCH3R2=metaN

H2

119864 HOMO=minus4

993C

T=1066417

119870H=16853119879

=8319

30N

HA=

5NHD=2

0166

1198836

R 1=OHR2=H

N=78

20120583

=5631120596=3483

NHA=4NHD=2MTI

=15585

TVC=447

10minus08

038

1


N

R1

R2

R3

R4R5

R6

R7

Figure 8




50values than the




50


4 Conclusion



(120596) on pIC50




Competing Interests


Acknowledgments


References


























































Journal of

Chemistry


Advances in

Physical Chemistry



Journal of

Volume 2014














Journal of

Spectroscopy



Journal of


Quantum Chemistry






CatalystsJournal of


0 02 04 06

minus2

minus15

minus1

minus05

0

05

1

15

2

12

4

7

8

9

11

12

15

16

18

19

20

23

24

25

26

27

28

29

30

3132

3334

36

39

40

41

43

45

46

47

48

49

50

52

54

56

58

62

3

6 10

1417

21

37

38

42

64


Leverage

Stan

dard

ized

resid

uals




50(AMA)







0 01 02 03 04 05

minus4

minus3

minus2

minus1

0

1

2

12

3

47

8

1011

13

14

15

16

17

18

20

21

24

2528

31 32

33

34

35

38

40

45

47

48

5152

56

57

58

60

63

6465

5

6

26

2729

36 37

44

46

50


LeverageSt

anda

rdiz

ed re

sidua

ls






50values as



N

OO

Cl Cl

N

OO

F F

N

HN NH

O O

N N

Me

Me

Me

Me


Compound number 64


minus05

0

05

1

15

2

25

minus05 0 05 1 15 2



minus17

minus12

minus07

minus02

03

08

13

18

23

minus2 minus1 0 1 2


pIC

50

PRO

Pred (pIC50 PRO)

pIC

50

AM

A

Pred (pIC50 AMA)



pIC50PRO

Model 119899 = 51 MSE = 0079 1198772 = 0870 119877 = 0933

Training set [1 3 4 5 6 7 11 12 13 14 16 19 21 25 26 27 28 30 3234 35 36 37 38 39 40 41 43 44 46 47 48 49 50 51] MSE = 0044 1198772 = 0914 119877 = 0956


Model 119899 = 48 MSE = 0104 1198772 = 0918 119877 = 0843

Training set [2 3 4 5 6 7 10 11 13 16 18 19 20 21 23 27 28 2930 31 32 33 34 35 36 37 38 39 41 43 44 45 46 47] MSE = 0060 1198772 = 0897 119877 = 0947



ble8Va

lues

ofdescrip

torsantileish

manialactivity

pIC 50and

leverages(ℎ)

forthe

newdesig

nedcompo

unds

Designedcompo

unds

Num

ber

Descriptorsvalues

pIC 50(PRO

)Num

ber

Descriptorsvalues

pIC 50(A

MA)

N

R 2R 1

NH

2

1198831

R 1=OHR2=CH3

N=12490120583

=2232120596=

3099

NHA=3NHD=2MTI

=3651

TVC=334

10minus05

0571

119884 1

R 1=OCH3R2=CF3

119864 HOMO=minus5

235C

T=885365

119870H=93

02

119879=5814

30N

HA=6NHD=1

1395

119884 2

R 1=OHR2=CF3

119864 HOMO=minus5

312CT

=903801

119870H=12056

119879=647130N

HA=6NHD=2

099

8

119884 3

R 1=OCH3R2=F

119864 HOMO=minus5

034C

T=893897

119870H=10174

119879=566560NHA=4NHD=1

048

5

1198832

R 1=OHR2=OH

N=12380120583

=10

80120596

=3131

NHA=4NHD=3MTI

=3497

TVC=14

910minus05

0170

119884 4

R 1=R 2

=OC 2

H5

119864 HOMO=minus4

696C

T=9272

41

119870H=11222

119879=622010NHA=4NHD=1

046

4

119884 5

R 1=R 2

=OCH3

119864 HOMO=minus4

731C

T=916967

119870H=11469

119879=5994

70N

HA=4NHD=1

044

2

119884 6

R 1=OCH3R2=OC(

CH3) 3

119864 HOMO=minus4

872C

T=928656

119870H=11099

119879=635700NHA=4NHD=1

0109

119884 7

R 1=OCH3R2=CH2NH2

119864 HOMO=minus4

801C

T=934561

119870H=1418

5119879=66

0500NHA=4NHD=2

0122

N

O

O

R 2R 1

NH

2

1198833

R 1=OHR2=meta-NH2

N=11250120583

=5944120596=3392

NHA=5NHD=3MTI

=17128

TVC=223

10minus08

083

8119884 8

R 1=OCH3R2=param

eta-

F119864 H

OMO=minus5

081C

T=1006097

119870H=13267119879

=762370NHA=

6NHD=1

030

8

1198834

R 1=OCH3R2=para-N

H2

N=10850120583

=7114120596

=3295

NHA=5NHD=2MTI

=19138

TVC=204

10minus08

060

7119884 9

R 1=OCH3R2=para

NH2

119864 HOMO=minus4

966C

T=1066417

119870H=16853119879

=8319

3NHA=

5NHD=2

0199

1198835

R 1=OCH3R2=meta-NH2

N=10850120583

=6193120596=3353

NHA=5NHD=2MTI

=18992

TVC=204

10minus08

030

9119884 10

R 1=OCH3R2=metaN

H2

119864 HOMO=minus4

993C

T=1066417

119870H=16853119879

=8319

30N

HA=

5NHD=2

0166

1198836

R 1=OHR2=H

N=78

20120583

=5631120596=3483

NHA=4NHD=2MTI

=15585

TVC=447

10minus08

038

1


N

R1

R2

R3

R4R5

R6

R7

Figure 8




50values than the




50


4 Conclusion



(120596) on pIC50




Competing Interests


Acknowledgments


References


























































Journal of

Chemistry


Advances in

Physical Chemistry



Journal of

Volume 2014














Journal of

Spectroscopy



Journal of


Quantum Chemistry






CatalystsJournal of


N

OO

Cl Cl

N

OO

F F

N

HN NH

O O

N N

Me

Me

Me

Me


Compound number 64


minus05

0

05

1

15

2

25

minus05 0 05 1 15 2



minus17

minus12

minus07

minus02

03

08

13

18

23

minus2 minus1 0 1 2


pIC

50

PRO

Pred (pIC50 PRO)

pIC

50

AM

A

Pred (pIC50 AMA)



pIC50PRO

Model 119899 = 51 MSE = 0079 1198772 = 0870 119877 = 0933

Training set [1 3 4 5 6 7 11 12 13 14 16 19 21 25 26 27 28 30 3234 35 36 37 38 39 40 41 43 44 46 47 48 49 50 51] MSE = 0044 1198772 = 0914 119877 = 0956


Model 119899 = 48 MSE = 0104 1198772 = 0918 119877 = 0843

Training set [2 3 4 5 6 7 10 11 13 16 18 19 20 21 23 27 28 2930 31 32 33 34 35 36 37 38 39 41 43 44 45 46 47] MSE = 0060 1198772 = 0897 119877 = 0947



ble8Va

lues

ofdescrip

torsantileish

manialactivity

pIC 50and

leverages(ℎ)

forthe

newdesig

nedcompo

unds

Designedcompo

unds

Num

ber

Descriptorsvalues

pIC 50(PRO

)Num

ber

Descriptorsvalues

pIC 50(A

MA)

N

R 2R 1

NH

2

1198831

R 1=OHR2=CH3

N=12490120583

=2232120596=

3099

NHA=3NHD=2MTI

=3651

TVC=334

10minus05

0571

119884 1

R 1=OCH3R2=CF3

119864 HOMO=minus5

235C

T=885365

119870H=93

02

119879=5814

30N

HA=6NHD=1

1395

119884 2

R 1=OHR2=CF3

119864 HOMO=minus5

312CT

=903801

119870H=12056

119879=647130N

HA=6NHD=2

099

8

119884 3

R 1=OCH3R2=F

119864 HOMO=minus5

034C

T=893897

119870H=10174

119879=566560NHA=4NHD=1

048

5

1198832

R 1=OHR2=OH

N=12380120583

=10

80120596

=3131

NHA=4NHD=3MTI

=3497

TVC=14

910minus05

0170

119884 4

R 1=R 2

=OC 2

H5

119864 HOMO=minus4

696C

T=9272

41

119870H=11222

119879=622010NHA=4NHD=1

046

4

119884 5

R 1=R 2

=OCH3

119864 HOMO=minus4

731C

T=916967

119870H=11469

119879=5994

70N

HA=4NHD=1

044

2

119884 6

R 1=OCH3R2=OC(

CH3) 3

119864 HOMO=minus4

872C

T=928656

119870H=11099

119879=635700NHA=4NHD=1

0109

119884 7

R 1=OCH3R2=CH2NH2

119864 HOMO=minus4

801C

T=934561

119870H=1418

5119879=66

0500NHA=4NHD=2

0122

N

O

O

R 2R 1

NH

2

1198833

R 1=OHR2=meta-NH2

N=11250120583

=5944120596=3392

NHA=5NHD=3MTI

=17128

TVC=223

10minus08

083

8119884 8

R 1=OCH3R2=param

eta-

F119864 H

OMO=minus5

081C

T=1006097

119870H=13267119879

=762370NHA=

6NHD=1

030

8

1198834

R 1=OCH3R2=para-N

H2

N=10850120583

=7114120596

=3295

NHA=5NHD=2MTI

=19138

TVC=204

10minus08

060

7119884 9

R 1=OCH3R2=para

NH2

119864 HOMO=minus4

966C

T=1066417

119870H=16853119879

=8319

3NHA=

5NHD=2

0199

1198835

R 1=OCH3R2=meta-NH2

N=10850120583

=6193120596=3353

NHA=5NHD=2MTI

=18992

TVC=204

10minus08

030

9119884 10

R 1=OCH3R2=metaN

H2

119864 HOMO=minus4

993C

T=1066417

119870H=16853119879

=8319

30N

HA=

5NHD=2

0166

1198836

R 1=OHR2=H

N=78

20120583

=5631120596=3483

NHA=4NHD=2MTI

=15585

TVC=447

10minus08

038

1


N

R1

R2

R3

R4R5

R6

R7

Figure 8




50values than the




50


4 Conclusion



(120596) on pIC50




Competing Interests


Acknowledgments


References


























































Journal of

Chemistry


Advances in

Physical Chemistry



Journal of

Volume 2014














Journal of

Spectroscopy



Journal of


Quantum Chemistry






CatalystsJournal of


ble8Va

lues

ofdescrip

torsantileish

manialactivity

pIC 50and

leverages(ℎ)

forthe

newdesig

nedcompo

unds

Designedcompo

unds

Num

ber

Descriptorsvalues

pIC 50(PRO

)Num

ber

Descriptorsvalues

pIC 50(A

MA)

N

R 2R 1

NH

2

1198831

R 1=OHR2=CH3

N=12490120583

=2232120596=

3099

NHA=3NHD=2MTI

=3651

TVC=334

10minus05

0571

119884 1

R 1=OCH3R2=CF3

119864 HOMO=minus5

235C

T=885365

119870H=93

02

119879=5814

30N

HA=6NHD=1

1395

119884 2

R 1=OHR2=CF3

119864 HOMO=minus5

312CT

=903801

119870H=12056

119879=647130N

HA=6NHD=2

099

8

119884 3

R 1=OCH3R2=F

119864 HOMO=minus5

034C

T=893897

119870H=10174

119879=566560NHA=4NHD=1

048

5

1198832

R 1=OHR2=OH

N=12380120583

=10

80120596

=3131

NHA=4NHD=3MTI

=3497

TVC=14

910minus05

0170

119884 4

R 1=R 2

=OC 2

H5

119864 HOMO=minus4

696C

T=9272

41

119870H=11222

119879=622010NHA=4NHD=1

046

4

119884 5

R 1=R 2

=OCH3

119864 HOMO=minus4

731C

T=916967

119870H=11469

119879=5994

70N

HA=4NHD=1

044

2

119884 6

R 1=OCH3R2=OC(

CH3) 3

119864 HOMO=minus4

872C

T=928656

119870H=11099

119879=635700NHA=4NHD=1

0109

119884 7

R 1=OCH3R2=CH2NH2

119864 HOMO=minus4

801C

T=934561

119870H=1418

5119879=66

0500NHA=4NHD=2

0122

N

O

O

R 2R 1

NH

2

1198833

R 1=OHR2=meta-NH2

N=11250120583

=5944120596=3392

NHA=5NHD=3MTI

=17128

TVC=223

10minus08

083

8119884 8

R 1=OCH3R2=param

eta-

F119864 H

OMO=minus5

081C

T=1006097

119870H=13267119879

=762370NHA=

6NHD=1

030

8

1198834

R 1=OCH3R2=para-N

H2

N=10850120583

=7114120596

=3295

NHA=5NHD=2MTI

=19138

TVC=204

10minus08

060

7119884 9

R 1=OCH3R2=para

NH2

119864 HOMO=minus4

966C

T=1066417

119870H=16853119879

=8319

3NHA=

5NHD=2

0199

1198835

R 1=OCH3R2=meta-NH2

N=10850120583

=6193120596=3353

NHA=5NHD=2MTI

=18992

TVC=204

10minus08

030

9119884 10

R 1=OCH3R2=metaN

H2

119864 HOMO=minus4

993C

T=1066417

119870H=16853119879

=8319

30N

HA=

5NHD=2

0166

1198836

R 1=OHR2=H

N=78

20120583

=5631120596=3483

NHA=4NHD=2MTI

=15585

TVC=447

10minus08

038

1


N

R1

R2

R3

R4R5

R6

R7

Figure 8




50values than the




50


4 Conclusion



(120596) on pIC50




Competing Interests


Acknowledgments


References


























































Journal of

Chemistry


Advances in

Physical Chemistry



Journal of

Volume 2014














Journal of

Spectroscopy



Journal of


Quantum Chemistry






CatalystsJournal of


N

R1

R2

R3

R4R5

R6

R7

Figure 8




50values than the




50


4 Conclusion



(120596) on pIC50




Competing Interests


Acknowledgments


References


























































Journal of

Chemistry


Advances in

Physical Chemistry



Journal of

Volume 2014














Journal of

Spectroscopy



Journal of


Quantum Chemistry






CatalystsJournal of
















































Journal of

Chemistry


Advances in

Physical Chemistry



Journal of

Volume 2014














Journal of

Spectroscopy



Journal of


Quantum Chemistry






CatalystsJournal of

research article investigation of antileishmanial activities of … · 2019. 7. 30. · research...

Documents