Machine Learning-Driven Anticancer Drug Discovery: From Virtual Screening to Preclinical Validation

Abstract

The escalating global cancer burden necessitates accelerated development of effective therapeutics, yet conventional drug discovery faces prohibitive challenges: cycles exceeding 10 years, costs surpassing $2 billion per drug, and clinical success rates below 10%. Traditional high-throughput screening paradigms remain resource-intensive and inefficient. This review systematically examines the transformative integration of bioinformatics, multi-omics data, and machine learning across the anticancer drug development pipeline. In virtual screening, AlphaFold-predicted protein structures have revolutionized structure-based approaches. When combined with graph neural networks (GNNs) and molecular dynamics simulations, these techniques enhance the accuracy of dynamic docking by over 30%. For ADMET optimization, GNN-based models significantly improve toxicity and pharmacokinetic prediction. Tumor organoid platforms now achieve 88% accuracy in replicating clinical chemotherapy responses for breast cancer, while interpretable AI tools like SHAP provide critical decision insights for molecular design. During preclinical validation, AI-driven automated synthesis systems enable closed-loop compound generation, accelerating design-to-synthesis workflows. Advanced bionic models—particularly 3D organoids and simulations of the tumor microenvironment—deliver physiologically relevant platforms for precise evaluation of efficacy and safety. Collectively, this synergistic framework of computational algorithms, experimental automation, and biomimetic models establishes a robust new paradigm for streamlining drug discovery and advancing personalized oncology therapeutics.