Supervised Screening of EGFR Inhibitors Validated through Computational Structural Biology Approaches

Abstract

One of the prominent challenges in breast cancer (BC) treatment is human epidermal growth factor receptor (EGFR) overexpression, which facilitates tumor proliferation and presents a viable target for anticancer therapies. This study integrates multiomics data to pinpoint promising therapeutic compounds and employs a machine learning (ML)-based similarity search to identify effective alternatives. We used BC cell line data from the Cancer Cell Line Encyclopedia (CCLE) and Genomics of Drug Sensitivity in Cancer (GDSC) databases and single-cell RNA sequencing (scRNA-seq) information that established afatinib as an efficacious candidate demonstrating superior IC₅₀ values. Next, ML models, including support vector machine (SVM), artificial neural networks (ANN), and random forest (RF), were trained on ChEMBL data to classify compounds with similar activity to the reference drug as active or inactive. The promising candidates underwent computational structural biology assessments for their molecular interactions and conformational dynamics. Our findings indicate that compounds ChEMBL233324, ChEMBL233325, ChEMBL234580, and ChEMBL372692 exhibit potent repressive action against EGFR, underscoring their potential as active antibreast cancer agents.