AI-enabled drug and molecular discovery: computational methods, platforms, and translational horizons

The integration of artificial intelligence (AI) with bioinformatics has initiated a transformative shift in drug discovery, redefining how pharmaceutical research and development are conducted. This review examines both the current state and emerging prospects of AI-driven strategies across the drug discovery pipeline, from target identification and molecular design to clinical applications. Advances in machine learning, deep learning, graph neural networks, transformers, foundation models, and quantum computing have shown remarkable potential in overcoming long-standing bottlenecks of conventional drug development, which on average requires 12.5 years and over $2 billion to bring a single drug to market. The AI in pharmaceuticals market, valued at $1.8 billion in 2023, is projected to reach $13.1 billion by 2030, reflecting a compound annual growth rate of 18.8%. Landmark breakthroughs such as AlphaFold, which has generated over 200 million predicted protein structures and more than 20,000 citations, have established new paradigms in structural biology and drug design, while emerging tools like RFdiffusion and AlphaFold3 further extend capabilities into de novo protein design and multi-omics integration. AI-enabled workflows have demonstrated the ability to compress discovery timelines from five years to as little as 12–18 months and reduce costs by up to 40%. Despite these advances, significant challenges remain, including issues of data quality and bias, model transparency and interpretability, computational resource demands, and ethical concerns regarding privacy and algorithmic fairness. This review synthesizes findings from recent studies to provide a roadmap for the responsible and reproducible integration of AI into bioinformatics and drug discovery, while outlining critical gaps and future research priorities essential for clinical translation.