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ORIGINAL RESEARCH article
Front. Pharmacol.
Sec. Predictive Toxicology
Volume 15 - 2024 |
doi: 10.3389/fphar.2024.1441587
This article is part of the Research Topic Shaping the Future of Predictive Toxicology: Addressing Challenges and New Approach Methodologies View all articles
Comprehensive Hepatotoxicity Prediction: Ensemble Model Integrating Machine Learning and Deep Learning
Provisionally accepted- Zhejiang University, Hangzhou, China
Background: Chemicals may lead to acute liver injuries, posing a serious threat to human health.Achieving the precise safety profile of a compound is challenging due to the complex and expensive testing procedures. In silico approaches will aid in identifying the potential risk of drug candidates in the initial stage of drug development and thus mitigating the developmental cost.In current studies, QSAR models were developed for hepatotoxicity predictions using the ensemble strategy to integrate machine learning (ML) and deep learning (DL) algorithms using various molecular features. A large dataset of 2588 chemicals and drugs was randomly divided into training (80%) and test (20%) sets, followed by the training of individual base models using diverse machine learning or deep learning based on three different kinds of descriptors and fingerprints. Feature selection approaches were employed to proceed with model optimizations based on the model performance. Hybrid ensemble approaches were further utilized to determine the method with the best performance.The voting ensemble classifier emerged as the optimal model, achieving an excellent prediction accuracy of 80.26%, AUC of 82.84%, and recall of over 93% followed by bagging and stacking ensemble classifiers method. The model was further verified by an external test set, internal 10-fold cross-validation, and rigorous benchmark training, exhibiting much better reliability than the published models.The proposed ensemble model offers a dependable assessment with a good performance for the prediction regarding the risk of chemicals and drugs to induce liver damage.
Keywords: Hepatotoxicity, Ensemble model, Molecular fingerprints, machine learning, deep learning
Received: 31 May 2024; Accepted: 24 Jul 2024.
Copyright: © 2024 Khan, Ren, Cao, Ye, Wang, Guo, Yang and Chen. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
JiaNan Ren, Zhejiang University, Hangzhou, China
Cheng Cao, Zhejiang University, Hangzhou, China
Hong-Yu-Xiang Ye, Zhejiang University, Hangzhou, China
Hao Wang, Zhejiang University, Hangzhou, China
Yamin Guo, Zhejiang University, Hangzhou, China
Jin-Rong Yang, Zhejiang University, Hangzhou, China
Jian-Zhong Chen, Zhejiang University, Hangzhou, China
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