Compound Library–Based Strategies for Anticancer Drug Discovery: Design, Screening, and Translational Approaches

Cancer remains a major global health challenge owing to its molecular complexity, heterogeneity, therapeutic resistance, and the dose-limiting toxicities associated with conventional chemotherapeutic agents. These limitations necessitate the development of innovative, systematic, and efficient strategies for anticancer drug discovery. In this context, compound library–based approaches have emerged as a cornerstone of modern drug development, enabling the rapid identification and optimization of bioactive molecules through high-throughput, rational, and integrative methodologies. This chapter provides a comprehensive overview of the principles, design strategies, and applications of compound libraries in anticancer drug discovery. The chapter systematically examines the concept, classification, and significance of compound libraries, including small-molecule, natural product, peptide, peptidomimetic, fragment-based, and focused libraries, highlighting their relevance in targeting diverse oncogenic pathways. Key library design strategies, such as rational drug design, diversity-oriented synthesis, and target-focused approaches, are discussed alongside contemporary synthetic methodologies, including combinatorial chemistry, solid-phase synthesis, parallel synthesis, and green chemistry techniques. Particular emphasis is placed on the integration of in silico tools, including molecular docking, virtual screening, pharmacophore modeling, QSAR analysis, molecular dynamics simulations, and ADMET prediction, which collectively improve hit identification, reduce attrition rates, and facilitate lead optimization. Furthermore, the chapter highlights the importance of high-throughput and high-content screening technologies, in vitro biological assays, and in vivo validation models in translating computational predictions into biologically and therapeutically relevant outcomes. Critical challenges such as limited chemical space coverage, translational gaps between in vitro and in vivo efficacy, toxicity concerns, and data complexity are critically evaluated. Finally, emerging trends including artificial intelligence–assisted library design, nanotechnology-based drug delivery systems, and precision oncology approaches are discussed as transformative advances shaping the future of anticancer therapeutics.