|
Bioprocess 2025
基于机器学习的hERG心脏毒性预测模型:利用分子指纹特征提高药物安全性评估
|
Abstract:
本研究旨在探讨基于机器学习的心脏毒性预测模型,特别是针对hERG心脏毒性的预测。hERG心脏毒性是药物开发中的关键安全问题,可能导致QT间期延长综合征,增加心律失常的风险。通过机器学习方法,利用分子指纹特征对化合物的心脏毒性进行预测。本研究的主要发现包括:通过逻辑回归、随机森林、支持向量机和神经网络等算法建立的预测模型,能够准确预测hERG心脏毒性,为药物安全性评估提供了一种新的数据驱动方法。结果表明,随机森林模型的预测性能最佳,准确率达到85%,显示出其在药物安全性评估中的应用潜力。此外,SVM和MLP模型的准确率也较高,而逻辑回归模型的泛化能力相对较弱。本研究为心脏毒性预测提供了一种数据驱动的方法,有助于提高药物开发的安全性和效率。
This study aims to explore machine learning-based cardiotoxicity prediction models, particularly for predicting hERG cardiotoxicity. hERG cardiotoxicity is a critical safety issue in drug development, as it can lead to QT interval prolongation syndrome and increase the risk of arrhythmia. Using machine learning methods, we predict the cardiotoxicity of compounds based on molecular fingerprint features. The key findings of this study include the development of predictive models using logistic regression, random forest, support vector machines, and neural networks, which accurately predict hERG cardiotoxicity. This provides a novel data-driven approach for drug safety assessment. Results show that the RF model achieves the best predictive performance with an accuracy of 85%, demonstrating its potential application in drug safety assessment. Additionally, the SVM and MLP models also exhibit high accuracy, while the LR model has relatively poor generalization ability. This study provides a data-driven method for predicting cardiotoxicity, contributing to improving drug development safety and efficiency.
[1] | Koutsoukas, A., Simms, B., Kirchmair, J., Bond, P.J., Whitmore, A.V., Zimmer, S., et al. (2011) From in Silico Target Prediction to Multi-Target Drug Design: Current Databases, Methods and Applications. Journal of Proteomics, 74, 2554-2574. https://doi.org/10.1016/j.jprot.2011.05.011 |
[2] | Mayr, A., Klambauer, G., Unterthiner, T. and Hochreiter, S. (2018) Large-Scale Comparison of Machine Learning Methods for Drug Target Prediction on CheMBL. Bioinformatics, 34, 1127-1136. |
[3] | Wang, Y., Liu, D. and Hu, X. (2019) Predicting hERG Channel Inhibition Using a Combination of Molecular Fingerprints and Machine Learning. Journal of Chemical Information and Modeling, 59, 381-390. |
[4] | Chen, H., Engkvist, O., Wang, Y., Olivecrona, M. and Blaschke, T. (2018) The Rise of Deep Learning in Drug Discovery. Drug Discovery Today, 23, 1241-1250. https://doi.org/10.1016/j.drudis.2018.01.039 |
[5] | Goh, G.B., Hodas, N.O. and Vishnu, A. (2017) Deep Learning for Computational Chemistry. Journal of Computational Chemistry, 38, 1291-1307. https://doi.org/10.1002/jcc.24764 |
[6] | Ramsundar, B., Kearnes, S., Riley, P., Webster, D., Konerding, D. and Pande, V. (2016) Massively Multitask Networks for Drug Discovery. arXiv: 1502.02072. |
[7] | Wallach, I., Dzamba, M. and Heifets, A. (2015) AtomNet: A Deep Convolutional Neural Network for Bioactivity Prediction in Structure-Based Drug Discovery. arXiv: 1510.02855. |
[8] | Dahl, G.E., Jaitly, N. and Salakhutdinov, R. (2014) Multi-Task Neural Networks for QSAR Predictions. arXiv: 1406.1231. |
[9] | Ma, J., Sheridan, R.P., Liaw, A., Dahl, G.E. and Svetnik, V. (2015) Deep Neural Nets as a Method for Quantitative Structure–activity Relationships. Journal of Chemical Information and Modeling, 55, 263-274. https://doi.org/10.1021/ci500747n |
[10] | Unterthiner, T., Mayr, A., Klambauer, G., Steijaert, M., Ceulemans, H., Wegner, J.K. and Hochreiter, S. (2014) Deep Learning as an Opportunity in Virtual Screening. Deep Learning and Representation Learning Workshop, NIPS 2014, http://www.bioinf.jku.at/publications/2014/NIPS2014a.pdf |
[11] | Altae-Tran, H., Ramsundar, B., Pappu, A.S. and Pande, V. (2017) Low Data Drug Discovery with One-Shot Learning. ACS Central Science, 3, 283-293. https://doi.org/10.1021/acscentsci.6b00367 |
[12] | Duvenaud, D.K., Maclaurin, D., Aguilera-Iparraguirre, J., Gomez-Bombarelli, R., Hirzel, T., Aspuru-Guzik, A. and Adams, R.P. (2015) Convolutional Networks on Graphs for Learning Molecular Fingerprints. Proceedings of the 29th International Conference on Neural Information Processing Systems, Montreal, 7-12 December 2015, 2224-2232. |
[13] | Gilmer, J., Schoenholz, S.S., Riley, P.F., Vinyals, O. and Dahl, G.E. (2017) Neural Message Passing for Quantum Chemistry. Proceedings of the 34th International Conference on Machine Learning, Sydney, 6-11 August 2017, 1263-1272. |
[14] | Kearnes, S., McCloskey, K., Berndl, M., Pande, V. and Riley, P. (2016) Molecular Graph Convolutions: Moving beyond Fingerprints. Journal of Computer-Aided Molecular Design, 30, 595-608. https://doi.org/10.1007/s10822-016-9938-8 |
[15] | Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C. and Yu, P.S. (2018) A Comprehensive Survey on Graph Neural Networks. IEEE Transactions on Neural Networks and Learning Systems, 29, 434-445. |
[16] | Yang, K., Swanson, K., Jin, W., Coley, C., Eiden, P., Gao, H., et al. (2019) Analyzing Learned Molecular Representations for Property Prediction. Journal of Chemical Information and Modeling, 59, 3370-3388. https://doi.org/10.1021/acs.jcim.9b00237 |
[17] | Zhang, L., Han, X., Wang, Z., Zhao, Y., Liu, S. and Li, J. (2018) End-to-End Attention-Based Recurrent Neural Network for Predicting Drug-Target Interactions from Heterogeneous Information. Scientific Reports, 8, 1-14. |
[18] | Zhu, H. and Kong, X. (2019) Graph Neural Networks for Drug Discovery. In: Liu, W.B., Hao, H.Q., Wang, H., Zou, Z.Y. and Xing, W.W., Eds., Graph Neural Networks: Methods and Applications, Springer, 175-196. |
[19] | Zhou, Z., Li, X. and Zare, R.N. (2018) Optimizing Chemical Reactions with Deep Reinforcement Learning. ACS Central Science, 4, 1129-1136. |
[20] | Zhavoronkov, A., Ivanenkov, Y.A., Aliper, A., Veselov, M.S., Aladinskiy, V.A., Aladinskaya, A.V., et al. (2019) Deep Learning Enables Rapid Identification of Potent DDR1 Kinase Inhibitors. Nature Biotechnology, 37, 1038-1040. https://doi.org/10.1038/s41587-019-0224-x |
[21] | Cortes, C. and Vapnik, V. (1995) Support-Vector Networks. Machine Learning, 20, 273-297. https://doi.org/10.1007/bf00994018 |
[22] | Breiman, L. (2001) Random Forests. Machine Learning, 45, 5-32. https://doi.org/10.1023/a:1010933404324 |
[23] | Bishop, C.M. (2006) Pattern Recognition and Machine Learning. Springer. |
[24] | Goodfellow, I., Bengio, Y. and Courville, A. (2016) Deep Learning. MIT Press. |
[25] | Kuhn, M. and Johnson, K. (2013) Applied Predictive Modeling. Springer. |
[26] | Hastie, T., Tibshirani, R. and Friedman, J. (2009) The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer. |
[27] | James, G., Witten, D., Hastie, T. and Tibshirani, R. (2013) An Introduction to Statistical Learning. Springer. |
[28] | Murphy, K.P. (2012) Machine Learning: A Probabilistic Perspective. MIT Press. |
[29] | Schmidhuber, J. (2015) Deep learning in neural networks: An overview. Neural Networks, 61, 85-117. https://doi.org/10.1016/j.neunet.2014.09.003 |
[30] | LeCun, Y., Bengio, Y. and Hinton, G. (2015) Deep learning. Nature, 521, 436-444. https://doi.org/10.1038/nature14539 |
[31] | Silver, D., Huang, A., Maddison, C.J., Guez, A., Sifre, L., van den Driessche, G., et al. (2016) Mastering the Game of Go with Deep Neural Networks and Tree Search. Nature, 529, 484-489. https://doi.org/10.1038/nature16961 |
[32] | Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., et al. (2015) Human-Level Control through Deep Reinforcement Learning. Nature, 518, 529-533. https://doi.org/10.1038/nature14236 |