Artificial intelligence in drug development: From molecular discovery to precision medicine

Drug development remains notoriously slow, expensive, and prone to failure, with the majority of early research discoveries failing to translate into tangible clinical benefit. Artificial intelligence (AI) has emerged as a transformative force capable of integrating diverse molecular, chemical, and clinical datasets, thereby enhancing decision-making across the entire drug development continuum. Despite the proliferation of individual AI tools in pharmaceutical research, a critical gap persists in their rigorous validation and regulatory integration. This review shows that AI influences every stage of drug development, including early-stage lead compound screening, computer-aided optimization, nonclinical simulation, and clinical trial implementation. However, widespread adoption remains hindered by key challenges including data reliability concerns, difficulties in verifying AI predictions, and regulatory supervision issues during its clinical use, all of which limit its widespread application. The optimal use of AI requires domain expertise and thorough verification, positioning AI as a complement to rather than a replacement for experimental research. Overall, determining whether a medicine is effective depends on solid validation of AI methods, adaptation to evolving field standards, and improved prediction of patient responses to medical interventions.