screening automatization @dsfmms.dsfarm.unipd.it/files/lezioni/psf/pdf/lbdd_1_2018_pdf.pdf · m m s...

MM S S.MORO – PSF 2018/2019Confidential and Property of ©2005 Molecular Modeling Section

Dept. Pharmaceutical and Pharmacological Sciences – University of Padova - Italy

ScreeningScreening automatizationautomatization @DSF@DSF



whenwhen potencypotency isis aa goodgood potency?potency?

David C. Rees, Miles Congreve,, Christopher W. Murray & Robin Carr Nature Reviews Drug Discovery 3, 660-672, 2004



WhatWhat aboutabout RiluzoleRiluzole??

MW = 234.2

logP = 2.3

IC50 = 16.1 M

Stephanie FedericoUNITS

Maicol BissaroMMS@UNIPD



xx.000 to 1?xx.000 to 1?Is this ratio really acceptable Is this ratio really acceptable

for a for a pharmapharma company?company?



WeWe surelysurely needneed informaticsinformatics butbut::WeWe surelysurely needneed informaticsinformatics butbut::

RandomRandom ScreeningScreening



MarketingPhase IV

ClinicalTrials

Lead to

Candidate

Hit to Lead

Screen to Hit

Target to

Validated Target

Gene to Function

VirtualScreening (VS)

HTS / in vitroScreening

Focused Synthesis/in vitro Screening

Lead Lead toto

CandidateCandidate

Hit Hit to Leadto Lead

Screen Screen to Hitto Hit

Virtual LeadOptimization

FlowchartsFlowcharts inin DrugDrug DiscoveryDiscovery::FlowchartsFlowcharts inin DrugDrug DiscoveryDiscovery::





Focused Synthesis/in vitro Screening

Lead Lead toto

CandidateCandidate




TargetStructure Determination




Lead Lead toto

CandidateCandidate




Structure-basedVirtual Screening (SBVS)

HT Molecular Docking

Ligand-basedVirtual Screening (LBVS)

Similarity searchPharmacophore matching



Lead Lead toto

CandidateCandidate





Ligand-based Optimization

PharmacophoreQSAR

Structure-based Optimization

Molecular DockingMolecular Dynamics



Menu (touristic) of our PSF course:Menu (touristic) of our PSF course:Menu (touristic) of our PSF course:Menu (touristic) of our PSF course:

••LigandLigand--based “Drug” Design (LBDD)based “Drug” Design (LBDD)

••StructureStructure--based “Drug” Design (SBDD) based “Drug” Design (SBDD)





Lead Lead toto

CandidateCandidate



WeWe dodo thisthis firstfirst fundamentalfundamentalconsiderationconsideration togethertogether::WeWe dodo thisthis firstfirst fundamentalfundamentalconsiderationconsideration togethertogether::

REALREAL

VIRTUALVIRTUAL

•• hitshits– active in assay– defined and confirmed

structures– drug-like potential



TheThe firstfirst realreal helphelp thatthat informaticsinformaticsgivesgives toto chemistrychemistry andand ininparticularparticular medicinalmedicinal chemistrychemistry isistoto ''virtualizevirtualize'' molecularmolecular structuresstructures..

TheThe firstfirst realreal helphelp thatthat informaticsinformaticsgivesgives toto chemistrychemistry andand ininparticularparticular medicinalmedicinal chemistrychemistry isistoto ''virtualizevirtualize'' molecularmolecular structuresstructures..

butbut……



WeWe dodo NEVERNEVER forgetforget::WeWe dodo NEVERNEVER forgetforget::

O

O

O

OH

This is one of itspossible chemical

representations

This is achemical



…… followfollow meme inin thisthis logiclogic comparisoncomparison::…… followfollow meme inin thisthis logiclogic comparisoncomparison::

Chemical Structure (CS)Chemical Structure (CS)Chemical Structure (CS)Chemical Structure (CS)

Chemical Compound (CCChemical Compound (CC))Chemical Compound (CCChemical Compound (CC))

Real worldReal world

Chemical Properties (CP)Chemical Properties (CP)Chemical Properties (CP)Chemical Properties (CP)

Numerical Numerical representations of CSrepresentations of CS

Numerical Numerical representations of CSrepresentations of CS

Virtual worldVirtual world

Molecular Descriptors (MD)Molecular Descriptors (MD)Molecular Descriptors (MD)Molecular Descriptors (MD)



…again the salicylic acid?

C9H8O4O

O

O

OH

WithWith howhow manymany chemicalchemicalrepresentationsrepresentations wewe cancan deal?deal?WithWith howhow manymany chemicalchemicalrepresentationsrepresentations wewe cancan deal?deal?



TheThe crucialcrucial informaticsinformatics differencesdifferences::TheThe crucialcrucial informaticsinformatics differencesdifferences::

C9H8O4

… this is a simple string (sequence) ofalphanumeric characters and it is veryeasy to manage… informatically!!!



JustJust aa simplesimple exampleexample:: areare thesethese twotworepresentationsrepresentations identical?identical?JustJust aa simplesimple exampleexample:: areare thesethese twotworepresentationsrepresentations identical?identical?

C9H8O4

C9H8O4

Time of answer (sec): Time of answer (sec):

O

O

O

OH

O

O

O

OH



O

O

O

OH

BeBe carefulcareful toto thethe chemicalchemical slangslang……BeBe carefulcareful toto thethe chemicalchemical slangslang……

O

O

O

OH

HH

H

HO

O

O

OH

HH

H

H

HH

HO

O

O

O

HH

H

H

HH

HH



O

O

O

OH

Remember,Remember, allall ofof thesethese areare notnotidenticalidentical…… informaticallyinformatically specking!specking!Remember,Remember, allall ofof thesethese areare notnotidenticalidentical…… informaticallyinformatically specking!specking!

O

O

O

O O

O

CH3

O

OH

O

O

CH3

O

OH

HH

H

H ……



andand thisthis isis amazingamazing::andand thisthis isis amazingamazing::

… draw is geometry…… geometry is numbers in space!!!



HereHere isis thethe informaticsinformatics anatomyanatomy ofofthisthis representationrepresentation::HereHere isis thethe informaticsinformatics anatomyanatomy ofofthisthis representationrepresentation::

O

O

O

OHy

x0

... the introduction of Connection Table:



ISIS

13 131.0986 -13.6500 0.0000 C1.0974 -14.4773 0.0000 C 1.8122 -14.8902 0.0000 C2.5287 -14.4769 0.0000 C2.5258 -13.6463 0.0000 C1.8104 -13.2372 0.0000 C 1.8080 -12.4122 0.0000 O1.0923 -12.0018 0.0000 C1.0898 -11.1768 0.0000 O0.3790 -12.4165 0.0000 C3.2387 -13.2311 0.0000 C3.9547 -13.6409 0.0000 O3.2356 -12.4061 0.0000 O

1 2 26 7 1 3 4 27 8 18 9 24 5 18 10 12 3 15 11 1 5 6 2

11 12 26 1 1

11 13 1M END

O

O

O

O

Atom typeNumber of atoms

Number of bonds

Coordinates:

x y z

The first bond is betweenatom 1 and 2 and its bondorder is 2 (double bond). 6

54

3

2

1

78

9

1011

12

13



justjust forforinformaticsinformaticsaddicted!addicted!

justjust forforinformaticsinformaticsaddicted!addicted!

Transformations:



Now,Now, thisthis isis perfectperfect understandable!understandable!Now,Now, thisthis isis perfectperfect understandable!understandable!

y

x0

z



MOE2007 3D

21 21 1.1522 0.5113 -0.0537 C0.2183 1.2057 -0.8346 C0.5665 2.3445 -1.5679 O

-1.0985 0.7524 -0.9667 C-1.8050 1.2920 -1.5911 H-1.5038 -0.3936 -0.2868 C-2.5276 -0.7473 -0.3775 H-0.5938 -1.0870 0.5099 C-0.9101 -1.9816 1.0411 H0.7269 -0.6400 0.6244 C1.4145 -1.2086 1.2469 H0.5056 3.5095 -0.8113 C0.9559 4.6611 -1.6596 C0.9409 5.5777 -1.0629 H0.2773 4.7842 -2.5073 H 1.9783 4.4900 -2.0055 H0.1372 3.6103 0.3517 O 2.5419 0.9700 0.0441 C3.0443 1.9108 -0.5504 O3.2755 0.2254 0.8972 O4.1677 0.6310 0.8800 H

1 10 2 1 2 1 1 18 1...20 21 1

M END

……clear?clear?……clear?clear?



TheThe crucialcrucial questionquestion:: whowho givesgives ususthethe goodgood coordinates?coordinates?TheThe crucialcrucial questionquestion:: whowho givesgives ususthethe goodgood coordinates?coordinates?

y

x0

z



List of Commonly Used Abbreviationsof coordinates archives (files):List of Commonly Used Abbreviationsof coordinates archives (files):

xxx.xxx.mol mol :: MDL (Molecular Design Limited, Inc) Molfile

xxx.xxx.mol2 mol2 : : SYBYL SYBYL MolfileMolfile

xxx.xxx.sdfsdf : : structure-data file

xxx.xxx.pdbpdb :: proteinprotein--datadata bankbank filefile

xxx.xxx.chmchm : : ChemDraw file

…



OpenBabel: a precious tool to convertone format to another… and much more!!OpenBabel: a precious tool to convertone format to another… and much more!!

http://openbabel.org/wiki/Main_Page



Object:Object: string (1D)string (1D)

xC9H8O4

Object:Object: 2D graph2D graphyO

OHO

O

Object:Object: 3D graph3D graph

z



AreAre wewe fullyfully satisfied?satisfied?AreAre wewe fullyfully satisfied?satisfied?

O

O

O

OH



WhatWhat wewe areare reallyreally missingmissing……WhatWhat wewe areare reallyreally missingmissing……

x

y

z



…… thethe illusionillusion ofof time!time!…… thethe illusionillusion ofof time!time!



CanCan youyou understandunderstand whatwhat isis this?this?CanCan youyou understandunderstand whatwhat isis this?this?



…… butbut thisthis illusionillusion isis incrediblyincrediblyexciting!!!exciting!!!…… butbut thisthis illusionillusion isis incrediblyincrediblyexciting!!!exciting!!!

G. Deganutti A. Cuzzolin



…… butbut thisthis illusionillusion isis incrediblyincrediblyexciting!!!exciting!!!…… butbut thisthis illusionillusion isis incrediblyincrediblyexciting!!!exciting!!!

V. Salmaso M. Sturlese



AnotherAnother importantimportant informaticsinformaticsdifference!difference!AnotherAnother importantimportant informaticsinformaticsdifference!difference!

C9H8O4O

O

O

OH

1.299 byte 2.051 byte6 byte

1 byte = 8 bit (1 bit = 0 o 1, true o false)

73.728 byte



DoDo youyou remember?remember?DoDo youyou remember?remember?

C9H8O4

C9H8O4

Time of answer (sec): Time of answer (sec):

O

O

O

OH

O

O

O

OH



String_of_characters

O

O

O

OH

CombiningCombining businessbusiness withwith pleasurepleasure ??CombiningCombining businessbusiness withwith pleasurepleasure ??

… possibly, using only a keyboard?… possibly, using only a keyboard?



SMILESSMILES (SSimplified MMolecular IInput LLine EEntry SSpecification)

String_of_charactersO

O

O

OH

CombiningCombining businessbusiness withwith pleasurepleasure ??CombiningCombining businessbusiness withwith pleasurepleasure ??



Using simple rules it is possible to representthe “connections” between “molecularfragments” (as in the structural formula) in asimple “string” of “alphanumericcharacters” (as in the bruta formula).


Here is some examples:

The original SMILES specification was initiated by DavidWeininger at the USEPA Mid-Continent Ecology DivisionLaboratory in Duluth in the 1980s.

Anderson E, Veith GD, Weininger D (1987). SMILES: A line notation and computerized interpreter forchemical structures. Duluth, MN: U.S. EPA, Environmental Research Laboratory-Duluth. Report No.EPA/600/M-87/021.Weininger D "SMILES, a chemical language and information system. 1. Introduction to methodologyand encoding rules". Journal of Chemical Information and Modeling. 28 (1): 31–6.



C methane

CC ethane

CCC propane

CC(C)C 2-methil-propane

C1CCCCC1 cycloesane

HH

H

H

H

H

HH

H

HH

H

HH

HH

HH

H

H

H

HH

HH

HHH

Some SMILES rules:




11

11

C1CCCCC1 cycloesane




C=C ethene (ethylene)

C=CC propene

C/C=C/C trans (E)-2-butane

C/C=C\C cis (Z)-2-butane

C#C ethyne (acetylene)

H

HH

H

CH3

HH

H

CH3

HCH3

H

CH3

HH

CH3

HH




c1ccccc1 benzene

Cc1ccccc1 toluene

Cc1ccc(N)cc1 4-methil-anilineNc1ccc(C)cc1

c12c(cccc1)cccc2 naphthalene

CH3

CH3

NH2




Examples for naphtalene:




CO

CC=O

CC(=O)C

CC(=O)O

OHH

H

H

OH

OH

H

H


OH

H

H

H

HH

H

OH

H

H

methanol

ethanal

acetone

acetic acid



In SMILES, tetrahedral centers may be indicated by asimplified chrial specification (@ or @@) written as anatomic property following the atomic symbol of the chiralatom.

Looking at the chiral center from the direction of the "from" atom (as peratom order in SMILES), @ means "the other three atoms are listed anti-clockwise; @@ means clockwise.

C[C@@H](N)C(=O)ONH2 COOH

NH2 COOH C[C@H](N)C(=O)O





Some examples:

Melatonin (C13H16N2O2) CC(=O)NCCc1c[nH]c2ccc(OC)cc12

Nicotine (C10H14N2) CN1CCC[C@H]1c2cccnc2

Glucose (glucopyranose) (C6H12O6)

OC[C@@H](O1)[C@@H](O)[C@H](O)[C@@H](O)[C@@H](O)1

Thiamine (C12H17N4OS+) (vitamin B1) OCCc1c(C)[n+](cs1)Cc2cnc(C)nc2N




Some examples:

Cephalostatin-1, a steroidic trisdecacyclic pyrazine with the empirical formula C54H74N2O10

Starting with the left-most methyl group in the figure:

CC(C)(O1)C[C@@H](O)[C@@]1(O2)[C@@H](C)[C@@H]3CC=C4[C@]3(C2)C(=O)C[C@H]5[C@H]4CC[C@@H](C6)[C@]5(C)Cc(n7)c6nc(C[C@@]89(C))c7C[C@@H]8CC[C@@H]%10[C@@H]9C[C@@H](O)[C@@]%11(C)C%10=C[C@H](O%12)[C@]%11(O)[C@H](C)[C@]%12(O%13)[C@H](O)C[C@@]%13(C)CO




Generation of SMILES:Break cycles, then write as branches off a mainbackbone. (Ciprofloxacin)



NON CANONICALNON CANONICALSMILESSMILES

CANONICALCANONICALSMILESSMILES

OCCC(O)CCCO

CCO

“CANONICAL” AIMS:“CANONICAL” AIMS:••One chemical one SMILES string;One chemical one SMILES string;••Same SMILES string coming from different programs;Same SMILES string coming from different programs;••Improving search provess in chemical databases.Improving search provess in chemical databases.





Morgan’s algorithmMorgan’s algorithmH. L. Morgan, J. Chem. Doc., 1965, 5, 107; LG, S.7

FROM CHEMICAL STRUCTURE… …TO MOLECULAR GRAPH




H. L. Morgan, J. Chem. Doc., 1965, 5, 107; LG, S.7

Replace the value of a given node with the sumof the values of the nodes connected

Morgan’s algorithmMorgan’s algorithm





Said… and done!


O(O=)Cc1ccccc1OC(=O)C



C9H8O4O

O

O

OH


reassuming:

Three faces of the same medal!!!Three faces of the same medal!!!




DoDo youyou needneed aa SMILES’sSMILES’s translator?translator?DoDo youyou needneed aa SMILES’sSMILES’s translator?translator?

http://mms.dsfarm.unipd.it/MMsINC/search/



ChemicalChemical archivesarchives……



TheThe secondsecond majormajor aidaid thatthatinformaticsinformatics givesgives toto thethe medicinalmedicinalchemistrychemistry isis thethe 'intelligent'intelligent storagestorage''ofof molecularmolecular structuresstructures..

TheThe secondsecond majormajor aidaid thatthatinformaticsinformatics givesgives toto thethe medicinalmedicinalchemistrychemistry isis thethe 'intelligent'intelligent storagestorage''ofof molecularmolecular structuresstructures..



Why:

archiviationarchiviation…… thethe firstfirst crucialcrucialvirtualizationvirtualization processprocess..

to ensure the uniqueness of representation inthe virtual world of an object in the real world!



andand itit isis clear,clear, too!too!

p-Phenylenediamine

PM 108.14

CAS 106-50-3



Let'sLet's startstart buildingbuilding ourour molecularmoleculardatabase?database?Let'sLet's startstart buildingbuilding ourour molecularmoleculardatabase?database?



O

O

O

OH

QueryQuery

SearchingSearching inin aa databasedatabase isis nownow veryveryeasy!easy!SearchingSearching inin aa databasedatabase isis nownow veryveryeasy!easy!




AA bitbit ofof…… sequencesequence alignmentalignment..AA bitbit ofof…… sequencesequence alignmentalignment..

Sequence A: C3H6OSequence B: C6H6O

11.. IdentityIdentity searchsearch::

aa.. IfIf thethe numbernumber ofof characterscharacters thethe same?same?

GoGo on!on!notnotidenticalidentical

NONO YESYES



AA bitbit ofof…… sequencesequence alignmentalignment..AA bitbit ofof…… sequencesequence alignmentalignment..

bb.. IfIf thethe numbernumber ofof characterscharacters isis thethe samesame::

C 3 H6 OC3H6O

The main diagonal represents inthe correspondence matrix theplace where we find the highest'similarity' between the twosequences.

C3H6O|||||C3H6O

100%identity!!!

CORRISPONDENCECORRISPONDENCEMATRIXMATRIX



…… importantimportant molecularmolecular Haystack!Haystack!…… importantimportant molecularmolecular Haystack!Haystack!

Date 21/11/2018 10:55:40 ESTCount 144,815,888 Commercially available organic and

inorganic chemicals

Chemical Abstract Service(www.cas.org)



httphttp:://pubchem//pubchem..ncbincbi..nlmnlm..nihnih..gov/gov/

httphttp:://www//www..emoleculesemolecules..com/com/

httphttp:://www//www..chemspiderchemspider..com/com/

httphttp:://mms//mms..dsfarmdsfarm..unipdunipd..it/MMsINCit/MMsINC..htmlhtml

…… otherother importantimportant molecularmolecular Haystacks!Haystacks!…… otherother importantimportant molecularmolecular Haystacks!Haystacks!



AA veryvery commoncommon situationsituation:: wewe havehave onlyonly oneonecompoundcompound thatthat wewe knowknow toto bebe ''activeactive'' againstagainstourour therapeutictherapeutic targettarget::

Could you suggest a strategy to find a Could you suggest a strategy to find a novel possible candidate?novel possible candidate?



SimilaritySimilarity:: structurestructure versusversus propertiesproperties……



DoDo togethertogether thisthis simplesimple exerciseexercise:: cancanyouyou describedescribe whatwhat youyou areare lookinglooking ??

SimilaritySimilarity lawlaw:: similarsimilar itemsitems tendtend toto bebegroupedgrouped togethertogether..



and,and, sometimes,sometimes, thisthis isis truetrue alsoalso inin medicinalmedicinalchemistrychemistry::

NH

ON

COOH

COOH NH

ON N

H

ON

NH

ON N

H

ONH

NH

ONH2

N+

C

CH2O NHNH

ON

Lidocaine

Lidofenin Bupivacaine Ropivacaine

Aptocaine Tocainide Prilocaine

+ +

NH

ON

NH

ON

COOMe

NH

ON

Pyrrocaine Butanilicaine Quatacaine

Mepivacaine Articaine Clodacaine

Tolycaine Trimecaine Etidocaine

NH

ON N

H

ON




NH

ON

COOH

COOH NH

ON N

H

ON

NH

ON N

H

ONH

NH

ONH2

N+

C

CH2O NHNH

ON

Lidocaine

Lidofenin Bupivacaine Ropivacaine

Aptocaine Tocainide Prilocaine

+ +

NH

ON

NH

ON

COOMe

NH

ON

Pyrrocaine Butanilicaine Quatacaine

Mepivacaine Articaine Clodacaine


NH

ON N

H

ON


IfIf wewe wantwant toto virtualizevirtualize chemicalchemical similaritysimilarity searching,searching,wewe havehave toto describedescribe exactlyexactly whatwhat similaritysimilarity is!is!

AreAre youyou ableable toto scalescale chemicalchemical similaritysimilarity ofof thesetheseanalogsanalogs respectrespect lidocaine?lidocaine?



TellTell meme whatwhat youyou areare thinkingthinking::

NH

ON

Lidocaine



ExactlyExactly ““molecularmolecular fragmentationfragmentation””couldcould bebe aa possiblepossible solution!solution!ExactlyExactly ““molecularmolecular fragmentationfragmentation””couldcould bebe aa possiblepossible solution!solution!

O

O

O

OHO

O

O

OHO

O

O

OH



WeWe needneed somesome rulesrules::WeWe needneed somesome rulesrules::

S

CF3

N

N

OH

S

CF3

N

N

OHRingsRings

11::

S

CF3

N

N

OHLinkersLinkers

22::

S

CF3

N

N

OH

33::

SideSide chainschainsG.W. Bemis, M. A. Murcko, J. Med. Chem. 1996, 39, 2887 –2893.



TheThe sequencesequence ofof fragmentsfragments isis likelike aamolecularmolecular barbar codecode……

OO

O

OH

HowHow wewe cancan buildbuild itit upup……HowHow wewe cancan buildbuild itit upup……



AnyAny standard?standard?AnyAny standard?standard?

MACCSMACCS (Molecular(Molecular ACCessACCess System)System) aacollectioncollection ofof 166166 functionalfunctional groupsgroupsrepresentingrepresenting thethe mostmost accessibleaccessiblemedchemmedchem chemicalchemical spacespace..

MACCS keys (1979), MDL Information Systems Inc. (originally named Molecular Design Limited, Inc.),San Leandro, CA.

There is no publication describing the MACCS 166 public keys. All of the citations for it either say avariation of "MDL did it"



A useful concept: A useful concept: structural keysstructural keysA useful concept: A useful concept: structural keysstructural keys

1 1 0 0 1 0 0 0

1. Define all possible chemical fragments (structural keys):

2. Assigne un bit for the each corrispondence in our molecule:

OH

OAr

OHO

ORNH2 O S

O

O

O

OH

3. Reiterate this procedure for each molecule of the database :

1 1 0 0 1 0 0 0

1 1 0 1 0 0 0 0

O

O

O

OH

OH

O

OH

OH

OAr

OH O

ORNH2 O S ……

OH

OAr

OHO

ORNH2 O S



whatwhat aboutabout thisthis parallelism?!parallelism?!whatwhat aboutabout thisthis parallelism?!parallelism?!

0000000100000001000000100010000000000010000010000100001000001000O

O

O

OH



…… so,so, nownow itit isis veryvery easyeasy toto transformtransform mymymolecularmolecular databasedatabase inin aa collectioncollection ofof““structuralstructural keyskeys”!”!

…… so,so, nownow itit isis veryvery easyeasy toto transformtransform mymymolecularmolecular databasedatabase inin aa collectioncollection ofof““structuralstructural keyskeys”!”!

000000010000110100000010101000000000011000001000010000100000100001000101100100100101100110100111001111010000001100000001100010000100010100011101010000110000101000010011000010100000000100100000000110111001110111111010000010001000011011011000000010011010000001000101001101000100000000100000000100100000001001000010001010000100011100011101000100001011101100110110010010001101001100001000010111010011010101011111100001000001111110001000010000100010100001000101001111010100001000100000000100100000101001000010001010000001000100010100010100100000000000001010000010000100000100000000010001010001001100000000000000000001010000001000000000000000000001000101000101000000000000001010000100100000000001000000000000000101010101111100111110100000000000011010100011100100001100101000010001010001100001000001100000000001000100000011000000000110000000000001000000000100001000000000000010101000000001000001001000000100010100010100000000100000000000010000000000000100001000011000000100010000110001001010000001010010101110001000010000100010100001000111000101000100001000010011100100100000100011000000001010000101010100010100010100100000000000010010000010010100100100010000



…… well,well, butbut whywhy thisthis transformationtransformation couldcouldhelphelp usus inin measuringmeasuring chemicalchemical similarity?similarity?…… well,well, butbut whywhy thisthis transformationtransformation couldcouldhelphelp usus inin measuringmeasuring chemicalchemical similarity?similarity?

000000010000110100000010101000000000011000001000010000100000100001000101100100100101100110100111001111010000001100000001100010000100010100011101010000110000101000010011000010100000000100100000000110111001110111111010000010001000011011011000000010011010000001000101001101000100000000100000000100100000001001000010001010000100011100011101000100001011101100110110010010001101001100001000010111010011010101011111100001000001111110001000010000100010100001000101001111010100001000100000000100100000101001000010001010000001000100010100010100100000000000001010000010000100000100000000010001010001001100000000000000000001010000001000000000000000000001000101000101000000000000001010000100100000000001000000000000000101010101111100111110100000000000011010100011100100001100101000010001010001100001000001100000000001000100000011000000000110000000000001000000000100001000000000000010101000000001000001001000000100010100010100000000100000000000010000000000000100001000011000000100010000110001001010000001010010101110001000010000100010100001000111000101000100001000010011100100100000100011000000001010000101010100010100010100100000000000010010000010010100100100010000

O

O

O

OHOH

O

OH



DoDo youyou rememberremember thethe EuleroEulero--VennVenn diagrams?diagrams?DoDo youyou rememberremember thethe EuleroEulero--VennVenn diagrams?diagrams?

…… andand thethe intersectionintersection setset AAB?B?…… andand thethe intersectionintersection setset AAB?B?

AA BB

AA BB

AABB

AABB

AABB

AABBAABB



Now,Now, II cancan introduceintroduce youyou thethe JaccardJaccard index!index!Now,Now, II cancan introduceintroduce youyou thethe JaccardJaccard index!index!

BABABA

solosolo

Index Jaccard

==44

11 ++ 11 ++ 44== 00..6666

Originally coined coefficient de communauté by Paul Jaccard, JaccardJaccard,, PP.. ((19011901)) BulletinBulletin deldel lala SociétéSociété VaudoisedesVaudoisedes SciencesSciences NaturellesNaturelles 3737,, 241241--272272 ..

AABB

AABB

AABB



Now,Now, II cancan introduceintroduce youyou thethe JaccardJaccard index!index!Now,Now, II cancan introduceintroduce youyou thethe JaccardJaccard index!index!

BABABA

solosolo

Index Jaccard

Originally coined coefficient de communauté by Paul Jaccard, JaccardJaccard,, PP.. ((19011901)) BulletinBulletin deldel lala SociétéSociété VaudoisedesVaudoisedes SciencesSciences NaturellesNaturelles 3737,, 241241--272272 ..

AA BB

AA BB

JIJI

JIJI

= 1= 1

= 0= 0



1 1 0 0 1 0 0 0

1 1 0 1 0 0 0 0

O

O

O

OH

OH

O

OH

AA

BBFragmentsFragments onlyonly inin AA == 11FragmentsFragments onlyonly inin BB == 11FFrraaggmmeennttss inin AABB == 22

TanimotoTanimoto similaritysimilarity indexindex ==22

11 ++ 11 ++ 22== 00..55

FromFrom JacardJacard…… toto TanimotoTanimoto index!index!FromFrom JacardJacard…… toto TanimotoTanimoto index!index!

OH

OAr

OHO

ORNH2 O S

TanimotoTanimoto, , T.T.T.T. (1957) IBM (1957) IBM InternalInternal Report 17th Report 17th NovNovIn that paper, a "similarity ratio" is given over bitmaps, where each bit of a fixed-size array represents the presence orabsence of a characteristic in the plant being modelled.



SimilaritySimilarity…… isis aa symmetricalsymmetrical property?property?SimilaritySimilarity…… isis aa symmetricalsymmetrical property?property?

Usually we think that if A is similar to B, thenB is similar to A. An example:

SimSim [[ ,, ]] == SimSim [[ ,, ]]

BABABA

solosolo

IndexTversky

but it not always the case in particular when thetwo ensembles are very different populated. Inthis case:

where and are user-defined parameters.



Asymmetric (Tversky) SimilarityAsymmetric (Tversky) Similarity

BABABA

solosolo

IndexTversky

• if = = 1, equation reduces to Tanimoto coefficient;• if , T becomes asymmetric

– where = 1 and = 0, T = AB / Ai.e. the fraction of A which it is has in common with B• when T = 1.0, it indicates that A is a substructure of

B (at the level of fingerprint matching)• when T1.0 it indicates that A is almost a

substructure of B

TT ==11 TT 11



BeforeBefore askingasking thethe chemicalchemical meaningmeaning ofofTanimotoTanimoto similaritysimilarity index,index, let'slet's seesee aacleverclever useuse::

000000010000110100000010101000000000011000001000010000100000100001000101100100100101100110100111001111010000001100000001100010000100010100011101010000110000101000010011000010100000000100100000000110111001110111111010000010001000011011011000000010011010000001000101001101000100000000100000000100100000001001000010001010000100011100011101000100001011101100110110010010001101001100001000010111010011010101011111100001000001111110001000010000100010100001000101001111010100001000100000000100100000101001000010001010000001000100010100010100100000000000001010000010000100000100000000010001010001001100000000000000000001010000001000000000000000000001000101000101000000000000001010000100100000000001000000000000000101010101111100111110100000000000011010100011100100001100101000010001010001100001000001100000000001000100000011000000000110000000000001000000000100001000000000000010101000000001000001001000000100010100010100000000100000000000010000000000000100001000011000000100010000110001001010000001010010101110001000010000100010100001000111000101000100001000010011100100100000100011000000001010000101010100010100010100100000000000010010000010010100100100010000

O

O

O

OH

TanimotoTanimoto0.450.630.750.640.860.340.770.980.960.230.540.560.330.860.650.890.670.56

TanimotoTanimoto0.980.960.890.860.860.770.750.670.650.640.630.560.560.540.450.340.330.23

search all compounds that have a Tanimoto similarity index > 0.85!



AA bitbit ofof…… sortingsorting algorithmalgorithm..AA bitbit ofof…… sortingsorting algorithmalgorithm..

AA sortingsorting algorithmalgorithm isis anan algorithmalgorithm thatthat putsputselementselements ofof aa listlist inin aa certaincertain orderorder..

…… andand exampleexample::…… andand exampleexample::



HereHere isis thethe firstfirst importantimportant informaticsinformaticsapplicationapplication inin medicinalmedicinal chemistrychemistry::

SearchSearch allall chemicalchemicalrepresentationrepresentation closeclose totothethe targettarget oneone withwith aaTanimotoTanimoto similaritysimilarity indexindex>> 00..8585



…… asas forfor anyany tool,tool, therethere areare limitations!limitations!…… asas forfor anyany tool,tool, therethere areare limitations!limitations!

O

O

O

OHO

O

O

OH

O

O

O OH

1 1 0 0 1 0 0 0

1 1 0 0 1 0 0 0

1 1 0 0 1 0 0 0

OH

OAr

OHO

ORNH2 O S



…… butbut wewe cancan alsoalso runrun aa subsub--structurestructuresearch!search!…… butbut wewe cancan alsoalso runrun aa subsub--structurestructuresearch!search!

00000000000010000000000000000000000000000000000000000000000000000

DatabaseDatabase

000000010000110100000010101000000000011000001000010000100000100001000101100100100101100110100111001111010000001100000001100010000100010100011101010000110000101000010011000010100000000100100000000110111001110111111010000010001000011011011000000010011010000001000101001101000100000000100000000100100000001001000010001010000100011100011101000100001011101100110110010010001101001100001000010111010011010101011111100001000001111110001000010000100010100001000101001111010100001000100000000100100000101001000010001010000001000100010100010100100000000000001010000010000100000100000000010001010001001100000000000000000001010000001000000000000000000001000101000101000000000000001010000100100000000001000000000000000101010101111100111110100000000000011010100011100100001100101000010001010001100001000001100000000001000100000011000000000110000000000001000000000100001000000000000010101000000001000001001000000100010100010100000000100000000000010000000000000100001000011000000100010000110001001010000001010010101110001000010000100010100001000111000101000100001000010011100100100000100011000000001010000101010100010100010100100000000000010010000010010100100100010000

OH

OAr

OH ONH2 O S

QueryQuery

O

CH3 O

OO



R Cl

O

R ClS

OO R-NO2 R1N

NR2

R1

R2

P X

Restriction rulesRestriction rulesDatabase In

Database Out

…… oror wewe cancan easilyeasily REMOVEREMOVE compoundcompound withwithundesirableundesirable fragmentsfragments (chemical(chemical ororbiologicalbiological reactivity,reactivity, toxicitytoxicity etcetc..)!)!

…… oror wewe cancan easilyeasily REMOVEREMOVE compoundcompound withwithundesirableundesirable fragmentsfragments (chemical(chemical ororbiologicalbiological reactivity,reactivity, toxicitytoxicity etcetc..)!)!



Baell J. and Walters M.A. Nature 513, 481-483 (2014)

Pan-Assay Interference compouNdS

AA veryvery nicenice example!example!AA veryvery nicenice example!example!



Pan-assay interference compounds (PAINS) encompasssome 400 structural classes, but more than half of PAINS ina typical library fall into just 16 easily recognizablecategories..Baell J. and Walters M.A. Nature 513, 481-483 (2014)



BackBack toto thethe horriblehorrible ratioratio xxxx..000000::11Discover a new drug is equal to find a…Discover a new drug is equal to find a…



AnalyzeAnalyze thethe nearestnearest chemicalchemical space?space?

SimilarSimilar chemicalchemical propertiesproperties similarsimilar chemicalchemicalstructuresstructures andand similarsimilar chemicalchemical properties?properties?



AnalyzeAnalyze thethe nearestnearest chemicalchemical space?space?

|x1 – x2| + |y1 – y2|



HereHere isis thethe secondsecond importantimportant informaticsinformaticsapplicationapplication inin medicinalmedicinal chemistrychemistry::

SearchSearch allall chemicalchemicalrepresentationrepresentation closeclose totothethe targettarget oneone withwith aaTanimotoTanimoto similaritysimilarity indexindex>> 00..8585 andand withwith aa PMPM ==360360 ±± 100100 andand aa logPlogP == 33..88±± 11



……andand don’tdon’t forgetforget::

1- similarity = diversity



AnAn alsoalso thethe RANDOMRANDOM SCREENINGSCREENING isis lesslessRANDOM,RANDOM, now!now!

RandomRandom ScreeningScreening



“Progettazione e Sviluppodi un Farmaco”

Stefano Moro

Chimica e Tecnologia Farmaceutiche



EvenEven ifif wewe MUSTMUST rememberremember thethe ““similaritysimilarityparadoxparadox””::

LL--DOPADOPA DD--DOPADOPA

OH

O

NH2

OH

OH

OH

O

NH2

OH

OH



MMsINC webMMsINC web--platform:platform:



CCN(CC)CC(=O)Nc1c(C)cccc1C

screening automatization @dsfmms.dsfarm.unipd.it/files/lezioni/psf/pdf/lbdd_1_2018_pdf.pdf · m m s...

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