Literatur Review: Strategi dan Perkembangan Computer-Aided Drug Design (Cadd) untuk Identifikasi Lead Compound Antikanker dalam Konteks Kimia Medisinal dan Farmasi

Cancer remains the second leading cause of death worldwide, while conventional therapies are often limited by toxicity, resistance, and high costs. Advances in bioinformatics and artificial intelligence have established Computer-Aided Drug Design (CADD) as an efficient tool for modern drug discovery. Integrating medicinal chemistry, structural biology, and computational modeling, CADD accelerates the identification and optimization of anticancer candidates through in silico approaches such as molecular docking, molecular dynamics, QSAR, and pharmacophore modeling. This review systematically analyzed literature from PubMed, ScienceDirect, ResearchGate, and Google Scholar (2015–2025) focusing on in silico studies related to anticancer drug design. Selected articles were evaluated based on molecular targets, compound types, CADD techniques, and major findings.The results reveal that CADD effectively identifies natural and synthetic compounds targeting key cancer proteins including EGFR, CDK2, PI3K/Akt/mTOR, and p53. Integration with artificial intelligence enhances screening efficiency, prediction accuracy, and ADMET assessment. Overall, CADD represents a crucial strategy to accelerate the discovery of selective and clinically promising anticancer drugs.