A review exploring the translational perspective of artificial intelligence in drug discovery and formulation...

The drug development pipeline remains extraordinarily complex, costly, and time-intensive, typically requiring 10-15years and $2-3 billion per approved drug. This review presents a translational perspective on how artificial intelligence (AI) and machine learning (ML) are renovating pharmaceutical R&D across the entire value chain while maintaining rigorous safety and efficacy standards. In drug discovery, deep learning platforms enable virtual screening of billion-compounds, reducing target identification from years to months while improving hit rates by 30-50%. Preclinical development benefits from AI-powered toxicity prediction, potentially eliminating 40% of animal testing through accurate in silico models. Clinical trials are optimized through digital twin technology, reducing patient cohorts by 25-30% without compromising statistical power. Post-marketing surveillance is accelerated 100-fold through AI-driven real-world evidence analysis. Across the development lifecycle, AI delivers 30-60% time savings and 25-40% cost reductions while increasing success rates through enhanced predictive capabilities. Formulation development benefits from ML algorithms that optimize drug compositions and stability, reducing trial-and-error experimentation. However, challenges persist in data quality, algorithmic bias, and regulatory acceptance of AI-derived evidence. This review provides a balanced perspective on AI's transformative potential in drug discovery and various formulation developments, along with its limitations, offering a roadmap for successful implementation in pharmaceutical R&D.