Abstract:The human epidermal growth factor receptor 2 (HER2) is a critical oncogene implicated in the development of various aggressive cancers, particularly breast cancer. Discovering novel HER2 inhibitors is crucial for expanding therapeutic options for HER2‐related malignancies. In this study, we present a computational workflow that focuses on generating pharmacophores derived from docked poses of a selected list of 15 diverse, potent HER2 inhibitors, utilizing flexible docking. The resulting pharmacophores, along with other physicochemical molecular descriptors, were then evaluated in a machine learning‐quantitative structure‐activity relationship (ML‐QSAR) analysis against 1,272 HER2 inhibitors. Several machine learning methods were assessed, and a genetic function algorithm (GFA) was employed for feature selection. Ultimately, GFA combined with Bagging and J48Graft classifiers produced the best self‐consistent and predictive models. These models highlighted the significance of two pharmacophores, Hypo_1 and Hypo_2, in distinguishing potent from less active inhibitors. The successful ML‐QSAR models and their associated pharmacophores were used to screen the National Cancer Institute (NCI) database for novel HER2 inhibitors. Three promising anti‐HER2 leads were identified, with the top‐performing lead demonstrating an experimental anti‐HER2 IC50 value of 3.85 μM. Notably, the three inhibitors exhibited distinct chemical scaffolds compared to existing HER2 inhibitors, as indicated by principal component analysis.