Has Artificial Intelligence Impacted Drug Discovery?

Abstract

Artificial intelligence (AI) tools find increasing application in drug discovery supporting every stage of the Design-Make-Test-Analyse (DMTA) cycle. The main focus of this chapter is the application in molecular generation with the aid of deep neural networks (DNN). We present a historical overview of the main advances in the field. We analyze the concepts of distribution and goal-directed learning and then highlight some of the recent applications of generative models in drug design with a focus into research work from the biopharmaceutical industry. We present in some more detail REINVENT which is an open-source software developed within our group in AstraZeneca and the main platform for AI molecular design support for a number of medicinal chemistry projects in the company and we also demonstrate some of our work in library design. Finally, we present some of the main challenges in the application of AI in Drug Discovery and different approaches to respond to these challenges which define areas for current and future work.

Keywords: AI; Active learning; CADD; CNN; ChEMBL; Chemical space; Computer-aided drug design; DMPK; DNN; Data augmentation; De novo generation; Deep generative models; Deep neural networks; Docking; Drug design; FEP; GAN; LSTM; Library design; Long short-term memory; MPO; Medicinal chemistry; Molecular design; Molecular graph; Multi-objective optimization; Neural networks; PubChem; QSAR; REINVENT; RNN; Reinforcement Learning; SMILES; Scoring function; Synthetic accessibility; VAE.