Practical constraints with machine learning in drug discovery

Abstract

Nowadays, machine learning methods, as a part of Artificial Intelligence, play a significant role at almost all stages of drug discovery. With their help, scientists can: identify promising targets for drug discovery and druggable binding pockets in them, assist in performing protein-ligand docking, assess docking results, build models for virtual screening of preprepared databases of potential drug molecules, perform de novo generation of novel molecules with desired biological activity, build QSAR models for lead optimization and assessing toxicity, ADME, environmental and hazardous properties of designed drugs, plan ways of their synthesis, predict side effects and interactions with other medications, etc [1–4]. At the same time, however, one should be aware that the use of machine learning in drug discovery always comes with certain practical constraints.