GESSE: Predicting Drug Side Effects from Drug–Target Relationships

The in silico prediction of unwanted side effects (SEs) caused by the promiscuous behavior of drugs and their targets is highly relevant to the pharmaceutical industry. Considerable effort is now being put into computational and experimental screening of several suspected off-target proteins in the hope that SEs might be identified early, before the cost associated with developing a drug candidate rises steeply. Following this need, we present a new method called GESSE to predict potential SEs of drugs from their physicochemical properties (three-dimensional shape plus chemistry) and to target protein data extracted from predicted drug–target relationships. The GESSE approach uses a canonical correlation analysis of the full drug–target and drug–SE matrices, and it then calculates a probability that each drug in the resulting drug–target matrix will have a given SE using a Bayesian discriminant analysis (DA) technique. The performance of GESSE is quantified using retrospective (external database) analysis and literature examples by means of area under the ROC curve analysis, “top hit rates”, misclassification rates, and a χ2 independence test. Overall, the robust and very promising retrospective statistics obtained and the many SE predictions that have experimental corroboration demonstrate that GESSE can successfully predict potential drug–SE profiles of candidate drug compounds from their predicted drug–target relationships.