Artificial Intelligence in Drug Discovery & Development

Abstract

Drug discovery and development is a complex, costly, and time-intensive process involving target identification, optimization, preclinical evaluation, clinical trials, regulatory approval, and post-market monitoring. Advances in artificial intelligence (AI) are transforming this landscape by enabling data-driven decision-making, reducing timelines, and improving predictive accuracy across the pharmaceutical pipeline. AI supports target identification through genomic and proteomic data analysis, enables de novo drug design with generative models, and aids property prediction and toxicity study using machine learning. In preclinical Trails, AI improves pharmacokinetic and pharmacodynamic modeling, predicts ADMET parameters, and supports the 3Rs principle by reducing animal use. Clinical research benefits from AI-driven patient recruitment, adaptive trial design, adherence tracking, and decentralized execution. Manufacturing and formulation are optimized with AI-based modeling, automation, and quality control. Regulatory review increasingly integrates AI for dossier evaluation, real-world evidence analysis, and safety monitoring, while post-marketing surveillance applies NLP and machine learning to detect adverse drug reactions and safety signals from diverse data sources. Despite challenges in data quality, interpretability, ethics, and regulatory acceptance, AI promises to reduce costs, improve efficiency, accelerate innovation in drug discovery and development. Future progress requires explainable, transparent, & globally harmonized AI solutions.

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