Combining machine‐learning and molecular‐modeling methods for drug‐target affinity predictions

AbstractMachine learning (ML) techniques offer a novel and exciting approach in the drug discovery field. One might even argue that their current expansion may push traditional MM modeling techniques to a secondary role in modeling methods. In this review article, we advocate that a combination of both techniques could be the most efficient implementation in the coming years. Focusing on drug‐target affinity predictions, we first review pure ML approaches. Then, we introduced recent developments in mixing ML and MM methods in a single combined manner. Finally, we show the detailed implementation of a real industrial prospective study where nanomolar hits, on a kinase target, were obtained by combination of state of the art Monte Carlo MM simulations (PELE) with a ML ranking function.This article is categorized under: Structure and Mechanism > Computational Biochemistry and Biophysics Data Science > Artificial Intelligence/Machine Learning Molecular and Statistical Mechanics > Molecular Mechanics