Harnessing Machine Learning for the Virtual Screening of Natural Compounds as Both EGFR and HER2 Inhibitors in Colorectal Cancer: A Novel Therapeutic Approach.

Colorectal cancer (CRC) is a type of cancer that affects the colon and rectum, with overexpression of epidermal growth factor receptor (EGFR) and human epidermal growth factor receptor 2 (HER2) observed in up to 85% of colorectal cancer cases. Although CRC treatment has progressed with the introduction of targeted drugs, current approaches have primarily focused on a single blockage of EGFR or HER2 to combat colon cancer. However, monotherapies that target either the EGFR or HER2 receptor frequently have low efficacy due to mutations in downstream effectors such as Kirsten Rat Sarcoma 2 Viral Oncogene Homologue (KRAS) and the activation of compensatory signaling pathways that support tumor survival and proliferation. Hence, the discovery and development of a therapy with the capability to combat CRC by simultaneously inhibiting both EGFR and HER2 remain avenues for further exploration. This study introduces a novel machine learning (ML)-based stacking ensemble framework for rapidly and accurately identifying dual EGFR and HER2 inhibitors using SMILES notation. A benchmark data set comprising active and inactive compounds against EGFR and HER2 was curated from the ChEMBL database. Based on this data set, 40 baseline models were developed and optimized using a comprehensive set of well-known molecular descriptors and ML algorithms (Figure 1). These models generated probabilistic features integrated via a logistic regression model as a final estimate to construct the final stacking ensemble model. The model was applied to bioactive compounds identified through LC-MS/MS profiling of