April Surac
Department of Biomedical Data Science, Stanford University, California, USA
Publications
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Research Article
Targeting EZH2 in Cancer: AI-Driven Pipeline for Drug Discovery and Optimization
Author(s): April Surac*
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). These results highlight the pipeline’s strong predictive accuracy and reliability in identifying potent inhibitors. Unique to this approach is the focus on biologically interpretable descriptors, such as molecular weight and LogP, which enhance model transpare.. Read More»