摘要
目的利用人工智能算法辅助药物设计,实现拮抗乳腺癌候选药物的分子描述符筛选、ERα回归预测、ADMET分类预测。方法针对乳腺癌候选药物筛选问题,以化合物对抑制乳腺癌靶标的生物活性及其ADMET性质出发,基于获取的1974种化合物数据,分别利用稀疏贝叶斯学习与随机森林算法进行两阶段筛选,得到不具备强相关性的前20个对生物活性最具显著性影响的分子描述符;随后以筛选后的数据及其PIC 50值为基础建立了QSAR模型,基于灰狼优化的核极限学习机算法对新化合物的生物活性进行了预测,横向对比11种常见机器学习算法,同时利用GBDT算法构建了ADMET分类模型。结果GWO-KELM模型具有更高的拟合优度与更低的均方误差,而且药物性质识别的4个模型预测准确率均保持90%以上。结论所建模型能够有效分析并预测化合物性质,为抗乳腺癌候选药物的研发提供参考。
Objective This study aimed to achieve molecular descriptor screening,ERαregression prediction,and ADMET classification prediction of antagonistic breast cancer drug candidates by using artificial intelligence algorithms to assist in drug design.Methods To address the screening problem of breast cancer drug candidates,starting from the biological activity of the compounds to inhibit the target of breast cancer and their ADMET properties,a two-stage screening was performed based on the obtained data of 1974 compounds with sparse Bayesian learning and random forest algorithms,respectively,to obtain the top 20 molecular descriptors with the most significant effect on biological activity without strong correlation;subsequently,based on the screened data and its PIC 50 value,a QSAR model was established,and the biological activity of the new compound was predicted based on the nuclear extreme learning machine algorithm optimized by the gray wolf,and 11 common machine learning algorithms were compared horizontally.The ADMET classification model was constructed.Results The results show that the GWO-KELM model has higher goodness of fit and lower mean square error.The prediction accuracies of the four models were maintained above 90%.Conclusion The proposed models can effectively analyze and predict the properties of compounds,which can provide a reference for the development of anti-breast cancer drug candidates.
作者
王斯
张国浩
陈义安
WANG Si;ZHANG Guohao;CHEN Yian(School of Mathematics and Statistics,Chongqing Technology and Business University,Chongqing 400067,China;Chongqing Key Laboratory of Social Economic and Applied Statistics,Chongqing 400067,China)
出处
《重庆工商大学学报（自然科学版）》
2023年第6期93-104,共12页
Journal of Chongqing Technology and Business University:Natural Science Edition
基金
重庆市自然科学基金项目(CSTC2020JCYJ-MSXMX0316)
重庆市教委科学技术研究计划重大项目(KJZD-M202100801)
重庆工商大学研究生创新项目(YJSCXX2022-112-189).
