基于分子对接和靶标特异性打分函数的化合物hERG心脏毒性预测  

Prediction of the hERG Cardiotoxicity of Compounds Using Molecular Docking and Target-Specific Scoring Functions

作　　者：孟金蕙 张力[1,2,3] 王廉馨 刘黎黎 刘苗 刘宏生 MENG Jin-hui;ZHANG Li;WANG Lian-xin;LIU Li-lil;LIU Miao;LIU Hong-sheng(School of Life Science,Liaoning University,Shenyang 110036,China;Liaoning Province Research Center for Computer Simulating and Information Processing of Bio-macromolecules,Shenyang 110036,China;Technology Innovation Center for Computer Simulating and Information Processing of Bio-macromolecules of Shenyang,Shenyang 110036,China;School of Pharmacy,Liaoning University,Shenyang 110036,China)

机构地区：[1]辽宁大学生命科学院,沈阳110036 [2]辽宁省生物大分子计算模拟与信息处理工程技术研究中心,沈阳110036 [3]沈阳市生物大分子计算模拟与信息处理技术创新中心,沈阳110036 [4]辽宁大学药学院,沈阳110036

出　　处：《中国药学杂志》2022年第19期1645-1650,共6页Chinese Pharmaceutical Journal

基　　金：国家自然科学基金资助项目资助(82003655);辽宁省科技厅重点研发计划项目资助(2019JH2/10300041);辽宁省教育厅科学研究经费项目资助(LQN201906)。

摘　　要：目的开发基于机器学习的人类快速延迟整流性钾通道基因(the human Etherh-à-go-go-Reloted Gene,hERG)特异性打分函数(RF-hERG-Score),用于预测化合物对hERG的抑制活性(pIC_(50))。方法采用随机森林算法,以AutoDock Vina(传统打分函数)分子对接生成的1847个化合物-hERG复合体结构和实验测定的半抑制浓度(pIC_(50))数据作为训练集进行训练。结果在十倍交叉验证中,RF-hERG-Score比RF-Score(通用打分函数)和AutoDock Vina更准确,其预测的pIC_(50)与实验值的皮尔逊相关系数(Rp)为0.603、斯皮尔曼等级相关系数(Rs)为0.623、均方根误差(RMSE)为0.849。在两组外部测试集中,RF-hERG-Score的Rp、Rs和RMSE也高于其他2种方法,并且优于相应文献报道模型的预测性能。结论RF-hERG-Score提高了hERG抑制剂结合亲和力的预测准确度,为利用计算模拟方法实现药物心脏毒性的较准确预测提供一种新的方案。OBJECTIVE To develop a machine learning-based hERG(the human Ether-à-go-go-Related Gene)target-specific scoring function(RF-hERG-Score)to predict the inhibitory activity of drugs on hERG(pIC_(50)).METHODS The random forest algorithm was used,and the structures of 1847 compound-hERG complexes generated by AutoDock Vina molecular docking and experimental binding affinity(pIC_(50))data were used as the training set.RESULTS In ten-fold cross-validation,RF-hERG-Score was more accurate than RF-Score(generic scoring function)and AutoDock Vina(empirical scoring function).Between the pIC_(50)predicted by RF-hERG-Score and the experimental pIC_(50),the Pearson correlation coefficient(Rp)was 0.603,the Spearman rank correlation coefficient(Rs)was 0.623,and the root mean square error(RMSE)was 0.849.In the two external test sets,the Rp,Rs,and RMSE of RF-hERG-Score were also higher than the other two methods and better than the prediction performance of the model reported in the corresponding research.CONCLUSION RF-hERG-Score improves the prediction accuracy of the binding affinity of hERG inhibitors and provides a new solution for using computational simulation methods to achieve accurate prediction of drug cardiotoxicity.

关 键 词：人类快速延迟整流性钾通道基因 毒性预测 分子对接 机器学习 打分函数 

分 类 号：R911[医药卫生—药学]