Physical binding pocket induction for affinity prediction.

Computational methods for predicting ligand affinity where no protein structure is known generally take the form of regression analysis based on molecular features that have only a tangential relationship to a protein/ligand binding event. Such methods have limited utility when structural variation moves beyond congeneric series. We present a novel approach based on the multiple-instance learning method of Compass, where a physical model of a binding site is induced from ligands and their corresponding activity data. The model consists of molecular fragments that can account for multiple positions of literal protein residues. We demonstrate the method on 5HT1a ligands by training on a series with limited scaffold variation and testing on numerous ligands with variant scaffolds. Predictive error was between 0.5 and 1.0 log units (0.7-1.4 kcal/mol), with statistically significant rank correlations. Accurate activity predictions of novel ligands were demonstrated using a validation approach where a small number of ligands of limited structural variation known at a fixed time point were used to make predictions on a blind test set of widely varying molecules, some discovered at a much later time point.

Accurate and interpretable computational modeling of chemical mutagenicity.

We describe a method for modeling chemical mutagenicity in terms of simple rules based on molecular features. A classification model was built using a rule-based ensemble method called RuleFit, developed by Friedman and Popescu. We show how performance compares favorably against literature methods. Performance was measured through the use of cross-validation and testing on external test sets. All data sets used are publicly available. The method automatically generated transparent rules in terms of molecular structure that agree well with known toxicology. While we have focused on chemical mutagenicity in demonstrating this method, we anticipate that it may be more generally useful in modeling other molecular properties such as other types of chemical toxicity.