Ether-a-go-go-related gene (hERG) channel blockade by small molecules is a big concern during drug development in the pharmaceutical industry. Blockade of hERG channels may cause prolonged QT intervals that potentially could lead to cardiotoxicity. Various in-silico techniques including deep learning models are widely used to screen out small molecules with potential hERG related toxicity. Most of the published deep learning methods utilize a single type of features which might restrict their performance. Methods based on more than one type of features such as DeepHIT struggle with the aggregation of extracted information. DeepHIT shows better performance when evaluated against one or two accuracy metrics such as negative predictive value (NPV) and sensitivity (SEN) but struggle when evaluated against others such as Matthew correlation coefficient (MCC), accuracy (ACC), positive predictive value (PPV) and specificity (SPE). Therefore, there is a need for a method that can efficiently aggregate information gathered from models based on different chemical representations and boost hERG toxicity prediction over a range of performance metrics. In this paper, we propose a deep learning framework based on step-wise training to predict hERG channel blocking activity of small molecules. Our approach utilizes five individual deep learning base models with their respective base features and a separate neural network to combine the outputs of the five base models. By using three external independent test sets with potency activity of IC50 at a threshold of 10 $$\upmu$$ m, our method achieves better performance for a combination of classification metrics. We also investigate the effective aggregation of chemical information extracted for robust hERG activity prediction. In summary, CardioTox net can serve as a robust tool for screening small molecules for hERG channel blockade in drug discovery pipelines and performs better than previously reported methods on a range of classification metrics.

中文翻译：

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CardioTox net：基于深度学习元特征集合的 hERG 通道封锁的稳健预测器

小分子对 Ether-a-go-go 相关基因 (hERG) 通道的阻断是制药行业药物开发过程中的一个大问题。hERG 通道的阻断可能会导致 QT 间期延长，从而可能导致心脏毒性。包括深度学习模型在内的各种计算机技术被广泛用于筛选具有潜在 hERG 相关毒性的小分子。大多数已发表的深度学习方法都使用单一类型的特征，这可能会限制其性能。基于不止一种特征的方法（例如 DeepHIT）很难聚合提取的信息。当针对阴性预测值 (NPV) 和灵敏度 (SEN) 等一两个准确度指标进行评估时，DeepHIT 表现出更好的性能，但当针对马太相关系数 (MCC)、准确度 (ACC)、阳性预测值 (PPV) 等其他指标进行评估时，DeepHIT 表现不佳）和特异性（SPE）。因此，需要一种方法能够有效地聚合从基于不同化学表示的模型中收集的信息，并在一系列性能指标上增强 hERG 毒性预测。在本文中，我们提出了一种基于逐步训练的深度学习框架来预测小分子的 hERG 通道阻断活性。我们的方法利用五个单独的深度学习基本模型及其各自的基本特征和一个单独的神经网络来组合五个基本模型的输出。通过使用三个外部独立测试集，在阈值为 10 $$\upmu$$ m 时，其效力活性为 IC50，我们的方法在分类指标组合方面获得了更好的性能。我们还研究了为稳健的 hERG 活性预测而提取的化学信息的有效聚合。总之，CardioTox net 可以作为一种强大的工具，用于筛选药物发现管道中 hERG 通道阻断的小分子，并且在一系列分类指标上比之前报道的方法表现更好。