Artificial Intelligence, Machine Learning, and Deep Learning in Real-Life Drug Design Cases

Abstract

The discovery and development of drugs is a long and expensive process with a high attrition rate. Computational drug discovery contributes to ligand discovery and optimization, by using models that describe the properties of ligands and their interactions with biological targets. In recent years, artificial intelligence (AI) has made remarkable modeling progress, driven by new algorithms and by the increase in computing power and storage capacities, which allow the processing of large amounts of data in a short time. This review provides the current state of the art of AI methods applied to drug discovery, with a focus on structure- and ligand-based virtual screening, library design and high-throughput analysis, drug repurposing and drug sensitivity, de novo design, chemical reactions and synthetic accessibility, ADMET, and quantum mechanics.

Keywords: ADMET; Artificial intelligence; De novo design; Deep learning; Drug design; Drug discovery; Drug repurposing; Drug sensitivity; HTS; Library design; Ligand-based virtual screening; Machine learning; QSAR; Quantum mechanics; Structure-based virtual screening; Synthesis planning.