集成学习
肝损伤
集合预报
药品
接收机工作特性
计算机科学
肝毒性
药物毒性
毒性
训练集
机器学习
人工智能
医学
药理学
内科学
作者
Haixin Ai,Wen Chen,Li Zhang,Liangchao Huang,Zimo Yin,Huan Hu,Qi Zhao,Jian Zhao,Hongsheng Liu
标识
DOI:10.1093/toxsci/kfy121
摘要
Drug-induced liver injury (DILI) is a major safety concern in the drug-development process, and various methods have been proposed to predict the hepatotoxicity of compounds during the early stages of drug trials. In this study, we developed an ensemble model using 3 machine learning algorithms and 12 molecular fingerprints from a dataset containing 1241 diverse compounds. The ensemble model achieved an average accuracy of 71.1 ± 2.6%, sensitivity (SE) of 79.9 ± 3.6%, specificity (SP) of 60.3 ± 4.8%, and area under the receiver-operating characteristic curve (AUC) of 0.764 ± 0.026 in 5-fold cross-validation and an accuracy of 84.3%, SE of 86.9%, SP of 75.4%, and AUC of 0.904 in an external validation dataset of 286 compounds collected from the Liver Toxicity Knowledge Base. Compared with previous methods, the ensemble model achieved relatively high accuracy and SE. We also identified several substructures related to DILI. In addition, we provide a web server offering access to our models (http://ccsipb.lnu.edu.cn/toxicity/HepatoPred-EL/).
科研通智能强力驱动
Strongly Powered by AbleSci AI