Artificial Intelligence in Drug Development
Abstract
The pharmaceutical industry faces persistent challenges in discovering and developing safe, effective, and affordable medicines. Artificial intelligence (AI) has emerged as a transformative computational technology capable of supporting multiple stages of the drug-development pipeline. Machine learning, deep learning, natural language processing, generative AI, and related approaches can analyze large biological, chemical, and clinical datasets to assist target identification, virtual screening, lead optimization, ADMET prediction, clinical-trial management, drug repurposing, pharmacovigilance, and personalized medicine. This review examines these applications and discusses the limitations associated with data quality, interpretability, bias, privacy, infrastructure, and regulatory validation. Emerging areas including multimodal AI, federated learning, autonomous laboratories, digital twins, and quantum computing are also considered. The evidence suggests that AI is best viewed as an enabling and decision-support technology that complements experimental and clinical research rather than replacing it.