chance correlation in qsar studies

Chance Correlation in QSAR studies

Ahmadreza MehdipourMedicinal & Natural Product Chemistry Research Center

Correlation or causation?

Correlation is essential but not sufficient Correlation is meaningless unless its

cause (or role) in the biological activity is interpreted

A satisfactory QSAR correlation does not mean that a particular descriptor causes the efficient action of a compound

Chance Correlation

•Topliss Ratio (J. Med. Chem. 1972, 35, 1066)

• A misconception

• Ratio of variables in model to Sample Size

• Ratio of variables in Data Pool to Sample Size

• Revalidation of problem by Livingstone(J. Med. Chem. 2005, 48, 6661)

• Topliss et al. demonstrated that the more independent variables (X) that are available for selection in a multiple linear regression model, the more likely a model will be found by chance. These authors recommended that in order to reduce the risk of chance correlations there should be a certain ratio of data points to the number of independent variables available. Unfortunately, this ratio was often misinterpreted as the number of data points to the number of independent variables in the final model, a practice that did very little if anything to reduce chance effects.

D.W. Salt, S. Ajmani, R. Crichton, D.J. Livingstone, An improved approximation to the estimation of the critical F values in best subset regression. J. Chem. Inf. Model. 47 (2007) 143-149.

Chance CorrelationHow does it occur?

•A Trial Example with random data

•Characteristics:

• N (Sample Size)=20

• K (Number of variables in data pool)=10, 20, 50, 75, 100

N=20 K=10

N=20 K=20

N=20 K=50

N=20 K=75

N=20 K=100

Avoiding chance correlation

What should we do?

Solutions for detection of chance correlation

Fmax critical Randomization of Y (input scrambling) Validation procedures

Fmax Critical

Linvingstone Approach Normal tabulated F is significant

ONLY WHEN

K=PK= number of variables in data poolP= number of variables in model

Fmax Critical

However, in most cases K>>PK= number of variables in data poolP= number of variables in modelN=Sample Size

Introduction of Fmax Critical Simulated random data Run 1000 times Different N, K and P Obtain Fmax for each combination

(for a significance level of 5%)

Check for some Known data sets www.cmd.port.ac.uk

Randomization of Y

Ys are randomly attributed to samples

Y-randomization

However This method should also be performed during

Variable selection process

if, R2max and Q2

max are low

Then, the risk of chance correlation is low

Cross-validation Process

Different N, K, P N=10, 20, 30, 40, 50, 80, 100 P=1-8 N=p, 10, 20, 30, 50, 100

Run 1000 times Evaluation factorsR2 of training setQ21 = Q2 for LOO CVQ220% = Q2 for Leave-20% of samples-Out CVQ250% = Q2 for Leave-50% of samples-Out CVR2P = R2 of one random test set (25% of samples)

0

0.2

0.4

0.6

0.8

1

1 3 5 7 9p

R2 max

n=10

n=20

n=30

n=40n=50

n=80n=100

0.0

0.2

0.4

0.6

0.8

1.0

1 3 5 7 9p

Q2 1max

n=10

n=100

0.0

0.2

0.4

0.6

0.8

1.0

1 3 5 7 9

p

Q2 20%max

n=10

n=100

0.000

0.200

0.400

0.600

0.800

1.000

1 3 5 7 9p

Q2 50%max

n=10

n=100

0.0

0.2

0.4

0.6

0.8

1.0

1 3 5 7 9p

R2pmax

n=10

n=20

n=30

n=40

n=50

n=80n=100

Cross-validation Process

Leave-one-out Vs Leave-group-out Q2

L50%O is independent of N, K, P

Hemmateenejad B, Mehdipour AR, Bagheri L, Miri R, Judging the significance of the multiple linear regression-based QSAR models by cross-validation. To be submitted

Concluding Remarks

Be aware of N to K ratio

Not only N to P ratio

Check different approaches for chance correlation

Models are not real but

sometimes are helpful