CADD-Driven Molecular Design: Computational Strategies for the Discovery and Optimization of Bioactive Molecules

Abstract

Computer-Aided Drug Design (CADD) has become a widely adopted and, in many documented cases, demonstrably valuable component of pharmaceutical chemistry, enabling the rational, in silico modeling, prediction, and optimization of bioactive molecules before the commitment of synthetic and biological resources. Over the past four decades, CADD has evolved from simple two-dimensional structure-activity correlations into a mature, multidisciplinary science integrating structural biology, statistical mechanics, medicinal chemistry, pharmacokinetics, and, most recently, artificial intelligence. This review provides a comprehensive treatment of the theoretical foundations, computational methodologies, and practical applications that define modern CADD, beginning with its historical evolution and foundational concepts-structure-activity relationships, molecular interactions, descriptors, and energy minimization-before examining the two principal design philosophies: structure-based drug design (SBDD) and ligand-based drug design (LBDD). Core techniques including molecular docking, molecular dynamics simulation, binding free-energy calculation, pharmacophore modeling, and QSAR analysis are described with attention to their algorithms, assumptions, and practical execution. The review further explores computational ADME and toxicity prediction, lead identification and optimization, applications across anticancer, antimicrobial, antiviral, central nervous system, and cardiovascular drug discovery, and computational drug repurposing. A dedicated discussion addresses integration of CADD with experimental pipelines through the design-make-test-analyze cycle, and a substantial section is devoted to artificial intelligence and machine learning, including deep learning-based property prediction, AI-augmented virtual screening, and generative de novo molecular design. Emerging frontiers-quantum chemical calculations, multi-parameter optimization, digital twins, and integration of omics data-are surveyed alongside a critical appraisal of scoring-function accuracy, computational cost, and reproducibility challenges that continue to constrain the field. Throughout, established, well-replicated methodology is distinguished from emerging approaches whose evidence base remains preliminary, and a comparative summary of method-level advantages, limitations, computational cost, and validation status is provided to support critical, rather than purely descriptive, evaluation. Collectively, this review aims to give researchers, students, and practitioners an integrated understanding of how computational strategies are discovering and optimizing the bioactive molecules of today and tomorrow.