AI-Assisted Drug Design

Drug discovery is a data-driven, knowledge-intensive, and labor-intensive field.Artificial intelligence (AI) has emerged as a powerful technology, capable of processing vast datasets and uncovering complex interactions.AI's application in pharmaceutical research and development (R&D) dates back to 1964, when Hansch introduced quantitative structure-activity relationships (QSAR).Since 2012, AI-assisted drug design has rapidly advanced with the rise of deep learning, now widely used across drug development stages, including target discovery, compound screening, lead optimization, drug-likeness analysis, and peptide design.AI-assisted drug design is increasingly seen as a key strategy in pharmaceutical R&D. Despite its potential, applying AI in pharmaceutical R&D requires integrating expertise from AI and drug research, posing challenges for newcomers due to its interdisciplinary nature.To address these challenges and meet the growing demand for educational resources, the authors wrote th