ADMET evaluation in drug discovery. 12. Development of binary classification models for prediction of hERG potassium channel blockage.

Inhibition of the human Ether-a-go-go Related Gene (hERG) potassium channel may result in QT interval prolongation, which causes severe cardiac side effects and is a major problem in clinical studies of drug candidates. The development of in silico tools to filter out potential hERG potassium channel blockers in early stages of the drug discovery process is of considerable interest. Here, a diverse set of 806 compounds with hERG inhibition data was assembled, and the binary hERG classification models using naïve Bayesian classification and recursive partitioning (RP) techniques were established and evaluated. The naïve Bayesian classifier based on molecular properties and the ECFP_8 fingerprints yielded 84.8% accuracy for the training set using the leave-one-out (LOO) cross-validation procedure and 85% accuracy for the test set of 120 molecules. For the two additional test sets, the model achieved 89.4% accuracy for the WOMBAT-PK test set, and 86.1% accuracy for the PubChem test set. The naïve Bayesian classifiers gave better predictions than the PR classifiers. Moreover, the Bayesian classifier, employing molecular fingerprints, highlights the important structural fragments favorable or unfavorable for hERG potassium channel blockage, which offers extra valuable information for the design of compounds avoiding undesirable hERG activity.

人源醚 - 相关基因（hERG）钾通道的抑制可能导致QT间期延长，这会引起严重的心脏副作用，并且是药物候选物临床研究中的一个主要问题。在药物研发过程的早期阶段开发计算机模拟工具以筛选出潜在的hERG钾通道阻滞剂具有相当大的意义。在此，收集了一组具有hERG抑制数据的806种不同化合物，并建立和评估了使用朴素贝叶斯分类和递归划分（RP）技术的二元hERG分类模型。基于分子性质和ECFP_8指纹的朴素贝叶斯分类器在使用留一法（LOO）交叉验证程序时对训练集的准确率为84.8%，对120个分子的测试集准确率为85%。对于另外两个测试集，该模型对WOMBAT - PK测试集的准确率为89.4%，对PubChem测试集的准确率为86.1%。朴素贝叶斯分类器比递归划分分类器给出了更好的预测结果。此外，使用分子指纹的贝叶斯分类器突出了对hERG钾通道阻断有利或不利的重要结构片段，这为设计避免不良hERG活性的化合物提供了额外的有价值信息。