| 引用本文: | 王 斯1,2, 张国浩1,2, 陈义安1,2.基于 GWO-KELM 与 GBDT 的抗乳腺癌药物性质预测(J/M/D/N,J:杂志，M：书，D：论文，N：报纸).期刊名称,2023，40（6）：93-104 |
| CHEN X. Adap tive slidingmode contr ol for discrete2ti me multi2inputmulti2 out put systems[ J ]. Aut omatica, 2006, 42(6): 4272-435 |
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| 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 1 974 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 PIC50 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。 |
| 关键词: 乳腺癌 ERα ADMET GWO-KELM GBDT 稀疏贝叶斯学习 |
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WANG Si1 2 ZHANG Guohao1 2 CHEN Yian1 2
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1. School of Mathematics and Statistics Chongqing Technology and Business University Chongqing 400067 China
2. Chongqing Key Laboratory of Social Economic and Applied Statistics Chongqing 400067 China
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| Abstract: |
| Objective This study aimed to achieve molecular descriptor screening ERα regression 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 1 974 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 PIC50 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. |
| Key words: breast cancer Erα ADMET GWO-KELM GBDT sparse Bayesian learning |
