Counted Fingerprint-Enhanced Graph Neural Network Models Enable Accurate Screening of hERG Blockers from Diverse Categories of Chemicals

Chemical-induced blockade of the human <i>ether-a-go-go</i>-related gene (hERG) K⁺ channels may lead to fatal cardiac arrhythmia. Given the ever-increasing number of chemicals, developing <i>in silico</i> models is preferable to time-consuming experimental tests for efficiently screening potential hERG blockers. Nonetheless, the existing models were primarily constructed with structural nondiversity data sets and adopted one type of molecular representation, limiting their prediction accuracy and applicability domain (AD) coverage. Herein, dual feature-based neural network (DFNN) models with fused molecular fingerprint (MF) and molecular graph (MG) features were constructed based on an enlarged hERG blockade data set for high-throughput screening of hERG blockers. The optimal DFNN model achieved average area under the receiver operating characteristic curve, sensitivity, and specificity values of 0.950, 0.844, and 0.909, respectively, outperforming single MF- or MG-based neural network models, MF-based machine learning models, and previous models. ADs of the optimal model were characterized by an advanced structure-activity landscape analysis method. The model with defined ADs was applied to screen over 500,000 industrial chemicals, drugs, and natural products, yielding over 26,000 hERG blocker identifications. The state-of-the-art performance and robustness of the DFNN model underscore the effectiveness of the feature fusion strategy in modeling processes, holding significance for other end points.