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

Chemical-induced blockade of the human ether-a-go-go-related gene (hERG) K+ channels may lead to fatal cardiac
arrhythmia. Given the ever-increasing number of chemicals, developing in silico 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.

化合物诱导的人类ether-à-go-go相关基因（human ether-a-go-go-related gene, hERG）钾离子通道阻断效应，可引发致命性心律失常。鉴于当前化合物数量持续攀升，开发计算机模拟（in silico）模型以替代耗时的实验检测，实现潜在hERG阻断剂的高效筛选，已成为更优方案。然而，现有模型主要基于结构多样性匮乏的数据集构建，且仅采用单一类型的分子表征手段，这限制了模型的预测精度与适用域（applicability domain, AD）的覆盖范围。本研究基于扩增后的hERG阻断数据集，构建了融合分子指纹（molecular fingerprint, MF）与分子图（molecular graph, MG）特征的双特征神经网络（dual feature-based neural network, DFNN）模型，用于hERG阻断剂的高通量筛选。最优DFNN模型的受试者工作特征曲线（receiver operating characteristic curve, ROC）下平均面积、灵敏度与特异度分别达0.950、0.844与0.909，其性能优于单一MF或MG驱动的神经网络模型、基于MF的机器学习模型以及既往同类模型。本研究采用先进的构效景观分析方法，对最优模型的适用域进行了表征。将该带有明确适用域的模型应用于超50万种工业化合物、药物及天然产物的筛选，共鉴定出超过26000种hERG阻断剂。DFNN模型的顶尖性能与稳健性，凸显了特征融合策略在建模流程中的有效性，该策略对其他研究终点亦具有重要借鉴价值。