Published Paper Details:

Pharmaceutical development has undergone revolutionary transformation through the integration of computational approaches into traditional discovery protocols. Computer-Aided Drug Design (CADD) represents a systematic framework employing mathematical modeling and in-silico simulations to predict therapeutic efficacy and optimize chemical structures prior to laboratory synthesis and validation. This methodology encompasses multiple complementary computational strategies: target structure exploitation, ligand-receptor interaction modeling, similarity-based analysis, and activity prediction through statistical correlation. Contemporary pharmaceutical challenges including protracted development timelines (typically 10-15 years) and escalating costs (approaching $2-3 billion per approved agent) can be substantially mitigated through rational computational prescreening of candidate compounds. This comprehensive literature review synthesizes current understanding of CADD fundamental concepts, evaluates principal computational techniques including receptorligand positioning algorithms and molecular property prediction models, examines available computational platforms, explores therapeutic discovery applications, and discusses emerging technologies including artificial intelligence integration. The intersection of advanced computational techniques with experimental verification methodologies signifies a fundamental paradigm shift in pharmaceutical research strategy.