Artificial Intelligence in Drug Discovery

Abstract

AI and ML in drug discovery have revolutionized medication development speed and optimization. Data-centric and machine learning technologies are revolutionizing drug development from target discovery to clinical trial projections. AI uses genomic, proteomic, and cheminformatics data to uncover novel drug candidates and predict their efficacy and safety better than traditional approaches. Machine learning, particularly deep learning, can find complex patterns in these datasets to discover new drug-target interactions and predict pharmacokinetics and pharmacodynamics. We use AI technologies including reinforcement learning, supervised and unsupervised learning, and natural language processing to drug development. Examples include deep neural networks for virtual screening, generative adversarial networks for chemical development, and adverse drug reaction ensembles. Data integration, model interpretability, and rigorous validation frameworks for AI-driven prediction reliability are also covered in the article.