Yu Myeong-Sang, Lee Jingyu, Lee Yunhyeok, Cho Daeahn, Oh Kwang-Seok, Jang Jidon, Nong Nuong Thi, Lee Hyang-Mi, Na Dokyun
Department of Biomedical Engineering, Chung-Ang University, Seoul, Republic of Korea.
Data Convergence Drug Research Center, Korea Research Institute of Chemical Technology, 141 Gajeong-ro, Yuseong-gu, Daejeon, 34114, Republic of Korea; Department of Medicinal and Pharmaceutical Chemistry, University of Science and Technology, Daejeon, 34129, Republic of Korea.
Comput Biol Med. 2025 Jan;184:109416. doi: 10.1016/j.compbiomed.2024.109416. Epub 2024 Nov 16.
The human ether-a-go-go-related gene (hERG) potassium channel is pivotal in drug discovery due to its susceptibility to blockage by drug candidate molecules, which can cause severe cardiotoxic effects. Consequently, identifying and excluding potential hERG channel blockers at the earliest stages of drug development is crucial. Most traditional machine learning models predict a molecule's cardiotoxicity or non-cardiotoxicity typically at 10 μM, which doesn't account for compounds with low IC values that are non-toxic at therapeutic levels due to their high effectiveness at lower concentrations. To address the need for more precise, quantitative predictions, we developed hERGBoost, a cutting-edge machine learning model employing a gradient-boosting algorithm. This model demonstrates superior accuracy in predicting the IC of drug candidates. Trained on a specially curated dataset for this study, hERGBoost not only exhibited excellent performance in external validation, achieving an R score of 0.394 and a low root mean square error of 0.616 but also significantly outstripped previous models in both qualitative and quantitative assessments. Representing a notable leap forward in the prediction of hERG channel blockers, the hERGBoost model and its datasets are freely available to the drug discovery community on our web server at. http://ssbio.cau.ac.kr/software/hergboost This resource promises to be invaluable in advancing safer pharmaceutical development.
人类醚 - 去极化相关基因（hERG）钾通道在药物发现中至关重要，因为它容易被候选药物分子阻断，这可能会导致严重的心脏毒性作用。因此，在药物开发的最早阶段识别并排除潜在的hERG通道阻滞剂至关重要。大多数传统的机器学习模型通常在10μM时预测分子的心脏毒性或非心脏毒性，这没有考虑到那些IC值低但在治疗水平无毒的化合物，因为它们在较低浓度下具有高效性。为了满足更精确、定量预测的需求，我们开发了hERGBoost，这是一种采用梯度提升算法的前沿机器学习模型。该模型在预测候选药物的IC方面表现出卓越的准确性。在为本研究专门策划的数据集上进行训练后，hERGBoost不仅在外部验证中表现出色，R分数达到0.394，均方根误差低至0.616，而且在定性和定量评估中均显著超过先前的模型。hERGBoost模型及其数据集代表了hERG通道阻滞剂预测方面的显著飞跃，可在我们的网络服务器上免费提供给药物发现社区。网址为：http://ssbio.cau.ac.kr/software/hergboost 。这一资源有望在推进更安全的药物开发方面发挥巨大价值。
