SepPCNET: Deeping Learning on a 3D Surface Electrostatic Potential Point Cloud for Enhanced Toxicity Classification and Its Application to Suspected Environmental Estrogens
文献类型：期刊论文
| 作者 | Wang, Liguo; Zhao, Lu; Liu, Xian; Fu, Jianjie ; Zhang, Aiqian
|
| 刊名 | ENVIRONMENTAL SCIENCE & TECHNOLOGY
 |
| 出版日期 | 2021-07-20 |
| 卷号 | 55期号:14页码:9958-9967 |
| ISSN号 | 0013-936X |
| 关键词 | deep learning 3D molecular surface electrostatic potential point cloud chemical toxicity classification model visualization data imbalance estrogen receptor agonist activity |
| 英文摘要 | Deep learning (DL) offers an unprecedented opportunity to revolutionize the landscape of toxicity prediction based on quantitative structure-activity relationship (QSAR) studies in the big data era. However, the structural description in the reported DL-QSAR models is still restricted to the two-dimensional level. Inspired by point clouds, a type of geometric data structure, a novel three-dimensional (3D) molecular surface point cloud with electrostatic potential (SepPC) was proposed to describe chemical structures. Each surface point of a chemical is assigned its 3D coordinate and molecular electrostatic potential. A novel DL architecture SepPCNET was then introduced to directly consume unordered SepPC data for toxicity classification. The SepPCNET model was trained on 1317 chemicals tested in a battery of 18 estrogen receptor-related assays of the ToxCast program. The obtained model recognized the active and inactive chemicals at accuracies of 82.8 and 88.9%, respectively, with a total accuracy of 88.3% on the internal test set and 92.5% on the external test set, which outperformed other up-to-date machine learning models and succeeded in recognizing the difference in the activity of isomers. Additional insights into the toxicity mechanism were also gained by visualizing critical points and extracting data-driven point features of active chemicals. |
| WOS研究方向 | Engineering, Environmental ; Environmental Sciences |
| 源URL | [http://ir.rcees.ac.cn/handle/311016/45831]  |
| 专题 | 生态环境研究中心_环境化学与生态毒理学国家重点实验室 |
| 作者单位 | 1.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 2.Univ Chinese Acad Sci, Hangzhou Inst Adv Study, Sch Environm, Hangzhou 310012, Peoples R China 3.Chinese Acad Sci, Res Ctr Ecoenvironm Sci, State Key Lab Environm Chem & Ecotoxicol, Beijing 100085, Peoples R China |
| 推荐引用方式 GB/T 7714 | Wang, Liguo,Zhao, Lu,Liu, Xian,et al. SepPCNET: Deeping Learning on a 3D Surface Electrostatic Potential Point Cloud for Enhanced Toxicity Classification and Its Application to Suspected Environmental Estrogens[J]. ENVIRONMENTAL SCIENCE & TECHNOLOGY,2021,55(14):9958-9967. |
| APA | Wang, Liguo,Zhao, Lu,Liu, Xian,Fu, Jianjie,&Zhang, Aiqian.(2021).SepPCNET: Deeping Learning on a 3D Surface Electrostatic Potential Point Cloud for Enhanced Toxicity Classification and Its Application to Suspected Environmental Estrogens.ENVIRONMENTAL SCIENCE & TECHNOLOGY,55(14),9958-9967. |
| MLA | Wang, Liguo,et al."SepPCNET: Deeping Learning on a 3D Surface Electrostatic Potential Point Cloud for Enhanced Toxicity Classification and Its Application to Suspected Environmental Estrogens".ENVIRONMENTAL SCIENCE & TECHNOLOGY 55.14(2021):9958-9967. |
入库方式： OAI收割
来源：生态环境研究中心
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