Artificial intelligence in drug discovery: from algorithmic foundations to clinical translation
Abstract
Artificial intelligence is quietly reconfiguring the landscape of drug discovery. It sharpens the search for new targets, refines lead compounds, and helps tailor clinical trial designs. Behind these advances sits a family of methods-convolutional, recurrent, and graph neural networks, generative adversarial networks, variational autoencoders, diffusion models, and Transformers-that has found its way into every stage of the pipeline. Tasks that once demanded years of trial and error, from pulling meaningful features out of molecules to predicting drug-target affinity and crystal structures with polymorph stability, now run faster and often with greater accuracy. This early insight lets researchers flag solid-form properties that influence bioavailability and manufacturability, trimming timelines and, in principle, lifting success rates. To map a field that keeps shifting shape, we searched PubMed, Web of Science, Scopus, and Google Scholar for peer-reviewed reports published between January 2007 and April 2026. The story the clinical cases tell is double-edged: one AI-discovered candidate has reached Phase IIa with encouraging efficacy, another stalled in Phase I when safety signals surfaced. AI can catch adverse effects earlier in toxicity assessments, yet nagging hurdles endure-biological complexity, patchy data, and a shortage of scientists fluent in both machine learning and pharmaceutics. Digging ourselves out will demand data standardization, nimble regulatory…
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