• In silico drug discovery and predictive pharmacology emerged as a central paradigm, leveraging PASS, QSAR, pharmacophore modeling, and virtual screening to forecast activities and guide experimental testing across numerous targets [4], [20], [6], [7], [15], [18], [11].

• Descriptor- and knowledge-based screening optimized early drug-likeness and target-fit via structure-based descriptors (MACCS/Daylight), pharmacophore point filters, and QSAR descriptors such as MEDV-13, informing selection and prioritization [2], [5], [12], [13].

• Structure- and receptor-oriented modeling approaches underpin mechanistic understanding of metabolism and drug–target interactions, exemplified by P450 modelling (COMPACT), CYP2D6 pharmacophore-protein models, and pseudoreceptor designs [13], [11], [18], [8].

• Data-driven machine learning and pattern discovery map genes, mechanisms, and drug responses, including SVM-based anticancer mechanism genes, inductive logic programming for drug design, and regression-augmented analyses of ligand data [14], [10], [19].

• Ligand design and pharmacophore and receptor surrogate frameworks enable targeted inhibitor development, such as selectin antagonists and peptidomimetic IL-1β converting enzyme inhibitors, illustrating pharmacophore-guided design in pharmacology [8], [9].