Application of ensemble learning for predicting GABAA receptor agonists
文献类型：期刊论文
| 作者 | Xiao, Fu1,2; Ding, Xiaoyu2,3; Shi, Yan4; Wang, Dingyan2,3; Wang, Yitian2,3; Cui, Chen2,3; Zhu, Tingfei2,3; Chen, Kaixian1,2,3 ; Xiang, Ping4; Luo, Xiaomin1,2,3,5
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| 刊名 | COMPUTERS IN BIOLOGY AND MEDICINE
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| 出版日期 | 2024-02-01 |
| 卷号 | 169页码:13 |
| 关键词 | GABA(A )receptor Benzodiazepine Agonist Machine learning Classification |
| ISSN号 | 0010-4825 |
| DOI | 10.1016/j.compbiomed.2024.107958 |
| 通讯作者 | Xiang, Ping(xiangping2630@163.com) ; Luo, Xiaomin(xmluo@simm.ac.cn) |
| 英文摘要 | Background : Over the past few decades, agonists binding to the benzodiazepine site of the GABA(A) receptor have been successfully developed as clinical drugs. Different modulators (agonist, antagonist, and reverse agonist) bound to benzodiazepine sites exhibit different or even opposite pharmacological effects, however, their structures are so similar that it is difficult to distinguish them based solely on molecular skeleton. This study aims to develop classification models for predicting the agonists. Methods: 306 agonists or non-agonists were collected from literature. Six machine learning algorithms including RF, XGBoost, AdaBoost, GBoost, SVM, and ANN algorithms were employed for model development. Using six descriptors including 1D/2D Descriptors, ECFP4, 2D-Pharmacophore, MACCS, PubChem, and Estate fingerprint to characterize chemical structures. The model interpretability was explored by SHAP method. Results: The best model demonstrated an AUC value of 0.905 and an MCC value of 0.808 for the test set. The PubMac-based model (PubMac-GB) achieved best AUC values of 0.935 for test set. The SHAP analysis results emphasized that MaccsFP62, ECFP_624, ECFP_724, and PubchemFP213 were the crucial molecular features. Applicability domain analysis was also performed to determine reliable prediction boundaries for the model. The PubMac-GB model was applied to virtual screening for potential GABA(A) agonists and the top 100 compounds were given. Conclusion: Overall, our ensemble learning-based model (PubMac-GB) achieved comparable performance and would be helpful in effectively identifying agonists of GABA(A) receptors. |
| WOS关键词 | CLINICAL-PRACTICE GUIDELINE ; CHRONIC INSOMNIA ; DRUG DISCOVERY ; QSAR ; ADULTS ; BENZODIAZEPINES ; CLASSIFICATION ; PHARMACOPHORE ; PHARMACOLOGY ; MANAGEMENT |
| WOS研究方向 | Life Sciences & Biomedicine - Other Topics ; Computer Science ; Engineering ; Mathematical & Computational Biology |
| 语种 | 英语 |
| WOS记录号 | WOS:001158508100001 |
| 出版者 | PERGAMON-ELSEVIER SCIENCE LTD |
| 源URL | [http://119.78.100.183/handle/2S10ELR8/309548]  |
| 专题 | 新药研究国家重点实验室 |
| 通讯作者 | Xiang, Ping; Luo, Xiaomin |
| 作者单位 | 1.Nanjing Univ Chinese Med, Sch Chinese Mat Med, Nanjing 210023, Peoples R China 2.Chinese Acad Sci, Shanghai Inst Mat Med, Drug Discovery & Design Ctr, State Key Lab Drug Res, 555 Zuchongzhi Rd, Shanghai 201203, Peoples R China 3.Univ Chinese Acad Sci, 19A Yuquan Rd, Beijing 100049, Peoples R China 4.Minist Justice, Acad Forens Sci, Shanghai Key Lab Forens Med, Key Lab Forens Sci,Shanghai Forens Serv Platform, Shanghai 200063, Peoples R China 5.Chinese Acad Sci, Shanghai Inst Mat Med, 555 Zuchongzhi Rd, Shanghai 201203, Peoples R China |
| 推荐引用方式 GB/T 7714 | Xiao, Fu,Ding, Xiaoyu,Shi, Yan,et al. Application of ensemble learning for predicting GABAA receptor agonists[J]. COMPUTERS IN BIOLOGY AND MEDICINE,2024,169:13. |
| APA | Xiao, Fu.,Ding, Xiaoyu.,Shi, Yan.,Wang, Dingyan.,Wang, Yitian.,...&Luo, Xiaomin.(2024).Application of ensemble learning for predicting GABAA receptor agonists.COMPUTERS IN BIOLOGY AND MEDICINE,169,13. |
| MLA | Xiao, Fu,et al."Application of ensemble learning for predicting GABAA receptor agonists".COMPUTERS IN BIOLOGY AND MEDICINE 169(2024):13. |
入库方式： OAI收割
来源：上海药物研究所
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