Computational Approaches for Anticancer Drug Design

Computational approaches have transformed anticancer drug discovery from an empirical, trial-and-error screening process into a rational, data-driven discipline. This approach targets the molecular complexity of tumors across the entire drug development pipeline. This chapter summarizes the advances in structure-based and ligand-based design, artificial intelligence, and systems pharmacology. It demonstrates how in silico strategies accelerate target identification, lead discovery, optimization, and clinical translation while reducing cost and attrition. Beginning with the molecular basis of cancer, it outlines how genetic and epigenetic alterations, signaling network rewiring, and pathway crosstalk inform the selection and validation of druggable targets. Furthermore, we discussed the details of structure-based drug design, covering experimental and predicted protein structures, as well as molecular docking for rational hit-to-lead refinement. Complementary ligand-based approaches, including pharmacophore modeling, QSAR, chemical similarity, and virtual screening against large libraries, are discussed as powerful alternatives when structural information is limited. We highlighted the integration of AI and machine learning for de novo molecule generation, activity prediction, and ADMET prediction, as well as molecular optimization. Subsequent sections examine computational drug repurposing and integrative pipelines that combine databases, cloud/HPC resources, and multimodal algorithms into end-to-end workflows. Finally, the chapter addresses key challenges, such as data heterogeneity and model interpretability, and surveys emerging frontiers that promise truly personalized, on-demand anticancer therapeutics. Overall, the chapter provides a comprehensive, practice-oriented framework for leveraging computational tools in precision oncology.