Artificial Intelligence in Drug Discovery and Development: Raising Quality per Decision

Drug research and development continuously encounters prolonged timelines, escalating costs, and high attrition rates. In this narrative review, we integrated recent advances in artificial intelligence across target identification, drug repurposing, de novo molecular design, structural biology, safety prediction, and artificial intelligence-supported clinical development, aligning these innovations with evolving global regulatory frameworks. Predictive and interpretable artificial intelligence could enhance the quality of decision-making throughout the research and development process when combined with causal or mechanistic priors, synthesis-aware and physics-informed molecular design, external validation with clear applicability domains, and governance systems aligned with multiple regulatory guidelines and qualified digital endpoint applications. Case studies of artificial intelligence-assisted discovery and repurposing demonstrate shorter development timelines, improved compound quality, and higher-level early-phase success, while underscoring challenges such as overfitting, model generalizability, and dataset bias. Establishing a context-of-use-based "credibility plan" and adopting equity-by-design through the inclusion of non-European datasets and subgroup performance evaluation are essential for achieving generalizable impact. Artificial intelligence integration with new approach methodologies and adaptive or covariate-adjusted clinical trials may help reduce development inefficiency without compromising scientific or ethical rigor.