Scatter plots showing coefficient of correlation (r) and best line of fit for training set compounds (a1 and a2 = AutoDock Vina score and AutoDock Vina rescore; b1 and b2 = Chem score and Chem rescore; c1 and c2 = ASP score and ASP rescore; d1 and d2 = Goldscore and Gold rescore; and e1 and e2 = Consensus score and Consensus rescore respectively).

<p>Scatter plots showing coefficient of correlation (r) and best line of fit for training set compounds (a1 and a2 = AutoDock Vina score and AutoDock Vina rescore; b1 and b2 = Chem score and Chem rescore; c1 and c2 = ASP score and ASP rescore; d1 and d2 = Goldscore and Gold rescore; and e1 and e2 = Consensus score and Consensus rescore respectively).</p

The Augmenting Effects of Desolvation and Conformational Energy Terms on the Predictions of Docking Programs against mPGES-1

<div><p>In this study we introduce a rescoring method to improve the accuracy of docking programs against mPGES-1. The rescoring method developed is a result of extensive computational study in which different scoring functions and molecular descriptors were combined to develop consensus and rescoring methods. 127 mPGES-1 inhibitors were collected from literature and were segregated into training and external test sets. Docking of the 27 training set compounds was carried out using default settings in AutoDock Vina, AutoDock, DOCK6 and GOLD programs. The programs showed low to moderate correlation with the experimental activities. In order to introduce the contributions of desolvation penalty and conformation energy of the inhibitors various molecular descriptors were calculated. Later, rescoring method was developed as empirical sum of normalised values of docking scores, LogP and Nrotb. The results clearly indicated that LogP and Nrotb recuperate the predictions of these docking programs. Further the efficiency of the rescoring method was validated using 100 test set compounds. The accurate prediction of binding affinities for analogues of the same compounds is a major challenge for many of the existing docking programs; in the present study the high correlation obtained for experimental and predicted pIC₅₀ values for the test set compounds validates the efficiency of the scoring method.</p></div

Scatter plots showing coefficient of correlation (r) between the experimental pIC₅₀and predicted pIC₅₀ by (a) AutoDock Vina rescore, (b) Chem rescore, (c) ASP rescore, (d) Gold rescore and (e) Consensus rescore; for test set compounds.

<p>Scatter plots showing coefficient of correlation (r) between the experimental pIC₅₀and predicted pIC₅₀ by (a) AutoDock Vina rescore, (b) Chem rescore, (c) ASP rescore, (d) Gold rescore and (e) Consensus rescore; for test set compounds.</p

Normalized scores of various docking programs and molecular descriptors.

<p>After data normalization and calculation of consensus score, correlation coefficient between the activity (pIC₅₀) and the consensus score was calculated. It was compared with correlation coefficient of all docking programs.</p><p>Normalized scores of various docking programs and molecular descriptors.</p

Correlation of normalized docking scores and molecular descriptors with pIC₅₀.

<p>Correlation of normalized docking scores and molecular descriptors with pIC₅₀.</p

Structure of training set compounds.

<p>Structure of training set compounds.</p