Enlarged Data Sets and Innovative Applicability Domain Characterization Empower ML Models to Reliably Bridge hERG Binding Data Gaps in Diverse Chemicals

Chemicals may cause cardiotoxicity by binding to the K⁺ channel encoded by the human <i>ether-à-go-go</i>-related gene (hERG). Given the ever-increasing number of chemicals, developing <i>in silico</i> models to efficiently fill the hERG binding affinity data gap is more desirable than conducting time-consuming experimental tests. However, previous data sets with limited chemical space hindered the development of models with high prediction accuracy and broad applicability domains (ADs). Herein, an expanded hERG binding affinity data set containing diverse categories of chemicals was constructed and subsequently employed to develop machine learning models. ADs of the constructed models were defined by an innovative structure-activity landscape (SAL)-based AD characterization (AD_SAL), which considers activity cliffs within SALs formed by molecules with similar structures but inconsistent bioactivities. The optimal model constrained by the AD_SAL achieved a coefficient of determination up to 0.89 on the external-validation set, which significantly outperformed previous models. The model coupled with the AD_SAL constraint was applied to predict hERG binding affinities for more than 100,000 chemicals from multiple inventories, identifying over 5,000 potential hERG blockers. The model with AD_SAL can serve as an efficient and reliable tool for bridging the hERG-mediated cardiotoxicity data vacancy to support sound chemical management.