Transforming Molecules, Transforming Medicine: A Comprehensive Review of Bioisosterism in Drug Design

Bioisosterism has transformed from empirical group replacement into a precision-guided cornerstone of drug discovery, integrating computational intelligence with chemical intuition. This review critically examines the evolution from classical monovalent through tetravalent substitutions to AI-driven, context-aware bioisosteric design. Modern computational frameworks—molecular electrostatic potential mapping, density functional theory, fragment molecular orbital analysis, and molecular dynamics—now predict electronic and structural equivalence with unprecedented accuracy. Graph neural networks, generative models, and reinforcement learning algorithms have revolutionized bioisostere identification, enabling rapid exploration of previously inaccessible chemical space. Contemporary successes—venetoclax's BCL-2 selectivity, elexacaftor's CFTR modulation, sotorasib's covalent KRAS targeting—exemplify bioisosterism's power in addressing selectivity, metabolic vulnerability, and undruggable targets. Yet critical challenges persist: predictive model limitations, synthetic tractability barriers, and extension to peptide/biologic modalities demand continued innovation. As personalized medicine and multi-target therapeutics reshape pharmaceutical landscapes, bioisosterism stands uniquely positioned at the convergence of computational prediction and medicinal chemistry creativity, driving next-generation therapeutic breakthroughs.