Jubilant Biosys Limited, 96 Industrial Suburb, 2nd Stage, Yeshwanthpur, Bangalore, 560022, India.
Future Med Chem. 2020 Oct;12(20):1829-1843. doi: 10.4155/fmc-2020-0156. Epub 2020 Oct 9.
Conventional experimental approaches used for the evaluation of the proarrhythmic potential of compounds in the drug discovery process are expensive and time consuming but an integral element in the safety profile required for a new drug to be approved. The voltage-gated sodium ion channel 1.5 (Na 1.5), a target known for arrhythmic drugs, causes adverse cardiac complications when the channel is blocked. Machine learning classification and regression models were built to predict the possibility of blocking these channels by small molecules. The finalized models tested with balanced accuracies of 0.88, 0.93 and 0.94 at three thresholds (1, 10 and 30 µmol, respectively). The regression model built to predict the pIC of compounds had q of 0.84 (root-mean-square error = 0.46). The machine learning models that have been built can act as effective filters to screen out the potentially toxic compounds in the early stages of drug discovery.
传统的实验方法常用于评估药物发现过程中化合物的致心律失常潜力，虽然这些方法在新药获得批准所需的安全概况中不可或缺，但却既昂贵又耗时。电压门控钠离子通道 1.5 (Na 1.5) 是一种已知与心律失常药物有关的靶点，当通道被阻断时会引起心脏不良反应。机器学习分类和回归模型被用来预测小分子阻断这些通道的可能性。经过三个阈值（分别为 1、10 和 30µmol）的平衡准确率测试，最终模型的准确率分别为 0.88、0.93 和 0.94。用于预测化合物 pIC 的回归模型 q 值为 0.84（均方根误差 = 0.46）。已经构建的机器学习模型可以作为有效的筛选工具，在药物发现的早期阶段筛选出潜在的有毒化合物。
