Targeting EZH2 in Cancer: AI-Driven Pipeline for Drug Discovery and Optimization

Traditional drug discovery is time-intensive and costly, often spanning over a decade and incurring billions in expenses. This study introduces a novel machine learning pipeline tailored to predict and optimize inhibitors for Enhancer of Zeste Homolog 2 (EZH2), a critical epigenetic target implicated in cancer progression. Leveraging curated datasets from repositories like the Protein Data Bank, PubChem, and ChEMBL, the pipeline integrates feature selection using Lipinski’s Rule of Five with advanced regression algorithms, achieving predictive metrics of R² = 0.75 and RMSE = 0.8 for inhibitory potency (pIC50 values). Unique to this approach is the focus on biologically interpretable descriptors such as molecular weight and LogP, which enhance model transparency and relevance to pharmacokinetics. Validation through molecular docking (SwissDock) and RDKit reinforced robustness, demonstrating the model’s ability to narrow down chemical libraries with a threefold improvement in efficiency compared to traditional computational methods. By combining machine learning with pharmacological insights, this study addresses key bottlenecks in early-stage drug discovery, offering a scalable and adaptable framework for EZH2-targeted cancer therapeutics. While experimental validation remains indispensable, this computational approach significantly accelerates the prioritization of candidate compounds, paving the way for cost-effective and efficient oncological drug development.