Computer-aided drug design (CADD) denotes the integrated application of computational methods, algorithms, and molecular modeling techniques to the identification, formulation, and assessment of pharmaceutical and bioactive substances, operating within a virtual environment that precedes or complements laboratory synthesis and experimental validation ^[1]. Within the taxonomy of computational drug discovery methods, CADD is defined by its role as a rational, data-driven framework that predicts how a candidate molecule will interact with a disease-related biological target, thereby guiding the selection and optimization of compounds before they are physically produced or tested ^[2]. The methodological space of CADD is conventionally organized into two complementary families. Structure-based drug design (SBDD) exploits the experimentally resolved or computationally derived three-dimensional structure of a macromolecular target to evaluate the geometric and energetic complementarity of candidate ligands, typically through molecular docking and scoring, and to design or optimize chemical entities that bind with high affinity ^[3]. Ligand-based drug design (LBDD) proceeds in the absence of a target structure and instead derives predictive models from the known physicochemical and biological properties of a set of active and inactive ligands, using approaches such as quantitative structure–activity relationships (QSAR), pharmacophore modeling, and similarity searching to identify novel candidates with the desired activity profile ^[2][4]. A defining operational characteristic of CADD is that it enables the exploration of the vast drug-like chemical space at a scale and speed unattainable by experimental high-throughput screening, and it integrates molecular dynamics simulations, virtual screening, and absorption, distribution, metabolism, excretion, and toxicity (ADMET) prediction into a coherent in silico pipeline for hit identification, lead optimization, and drug repurposing ^[1][3]. The term thus designates the computational toolbox and the rational design logic—spanning target selection, candidate generation, binding assessment, and pharmacokinetic profiling—that collectively constitute the in silico component of modern drug discovery ^[2][3].