Prediction of Biological Targets for Compounds Using Multiple-Category Bayesian Models Trained on Chemogenomics Databases

Target identification is a critical step following the discovery of small molecules that elicit a biological
phenotype. The present work seeks to provide an in silico correlate of experimental target fishing technologies
in order to rapidly fish out potential targets for compounds on the basis of chemical structure alone. A
multiple-category Laplacian-modified naïve Bayesian model was trained on extended-connectivity fingerprints
of compounds from 964 target classes in the WOMBAT (World Of Molecular BioAcTivity) chemogenomics
database. The model was employed to predict the top three most likely protein targets for all MDDR (MDL
Drug Database Report) database compounds. On average, the correct target was found 77% of the time for
compounds from 10 MDDR activity classes with known targets. For MDDR compounds annotated with
only therapeutic or generic activities such as “antineoplastic”, “kinase inhibitor”, or “anti-inflammatory”,
the model was able to systematically deconvolute the generic activities to specific targets associated with
the therapeutic effect. Examples of successful deconvolution are given, demonstrating the usefulness of the
tool for improving knowledge in chemogenomics databases and for predicting new targets for orphan
compounds.