Precision Drug Discovery in the Era of Artificial Intelligence: A Critical Review.
TL;DR
A paradigm shift toward autonomous scientific agents capable of causal reasoning and end-to-end experimental guidance is highlighted, and persistent challenges are discussed, including data bias, limited interpretability, and in silico-to-wet lab translation.
Abstract
Traditional drug development suffers from high costs, low success rates, and patient response variability. Precision drug discovery seeks to overcome these limitations by targeting specific genetic and molecular mechanisms but faces challenges in integrating cross-scale, multimodal biomedical data. Recent advances in artificial intelligence (AI), especially deep learning, provide powerful tools to navigate these complexities. This review surveys representative AI methods across four core stages of precision drug discovery: (a) target identification and validation using omics-, drug-, and structure-based approaches; (b) structure- and sequence-guided virtual screening of active compounds; (c) individualized drug response prediction integrating cell-line, single-cell, and multimodal data; and (d) AI-enabled toxicology and safety modeling to anticipate adverse liabilities and improve translational success. We further highlight a paradigm shift toward autonomous scientific agents capable of causal reasoning and end-to-end experimental guidance. Finally, we discuss persistent challenges, including data bias, limited interpretability, and in silico-to-wet lab translation.