Bayesian Model Averaging for Ligand Discovery

High-throughput screening (HTS) is now a standard approach used in the pharmaceutical industry to identify potential drug-like lead molecules. The analysis linking biological data with molecular properties is a major goal in both academic and pharmaceutical research. This paper presents a Bayesian analysis of high-dimensional descriptor data using Markov chain Monte Carlo (MCMC) simulations for learning classification trees as a novel method for pharmacophore and ligand discovery. We use experimentally determined binding affinity data with the protein pyruvate kinase to train and assess our model averaging algorithm and then apply it to a large database of over 3.7 million molecules. We compare the results of a number of variations on the central Bayesian theme to that of two Neural Network (NN) architectures and that of Support Vector Machines (SVM). The main Bayesian algorithm, in addition to achieving high specificity and sensitivity, also lends itself naturally to classifying test sets with missing data and providing a ranking for the classified compounds. The approach has been used to select and rank potential biologically active compounds and could provide a powerful tool in compound testing.