The Evolving Role of Artificial Intelligence and Machine Learning in Drug Discovery and Development

Review Article

Abstract

The traditional pharmaceutical research and development timeline is seriously protracted, costly, and marked by high attrition rates. Artificial intelligence and machine learning (AI/ML) are catalyzing a paradigm shift across this entire pipeline. These computational methods process vast, high-dimensional datasets to uncover novel biological insights and expedite candidate selection. In early-stage discovery, AI models analyze 'omics' data and biological networks to identify and validate novel therapeutic targets. For lead discovery, ML-powered virtual screening and de novo design, utilizing generative models, are creating potent and selective molecules with optimized pharmacokinetic profiles. Predictive algorithms are substantially refining ADMET (absorption, distribution, metabolism, excretion, and toxicity) modeling, reducing late-stage attrition. This transformation extends into clinical development, where AI assists in optimizing trial design, stratifying patient cohorts, and analyzing real-world evidence for post-market surveillance. While significant challenges related to data quality, model interpretability, and regulatory guidelines persist, the integration of AI/ML is remarkably streamlining processes, from initial hypothesis to clinical application. This computational revolution promises to lower development costs and accelerate the delivery of novel, personalized therapies to patients