Artificial Intelligence as a Catalyst for Structure Based Drug Design

Abstract

Drug discovery remains one of the most expensive and time-consuming endeavors in modern science, with the development of a single approved therapeutic typically requiring more than a decade of research and several billion dollars in investment. Over the past five years, artificial intelligence (AI) and machine learning (ML) have moved from peripheral computational aids to central drivers of the medicinal chemistry pipeline, reshaping how targets are identified, how hits are generated, and how lead compounds are optimized. This review synthesizes recent advances in AI-enabled drug discovery, with particular emphasis on structure-based and generative molecular design. We examine the principal categories of AI methodology in current use, including deep neural networks for bioactivity prediction, graph-based generative models for de novo molecule design, and transformer architectures for retrosynthesis planning. We further discuss landmark case studies demonstrating translational impact, most notably the discovery of the antibiotic halicin through deep learning-guided screening, and describe how structure-based generative frameworks are being benchmarked against traditional docking approaches. Finally, we critically evaluate persistent challenges relating to data quality, model interpretability, and regulatory acceptance, and outline directions likely to define the next decade of AI-augmented medicinal chemistry.