文献类型：期刊文献

英文题名：admetSAR3.0: a comprehensive platform for exploration, prediction and optimization of chemical ADMET properties

作者：Gu, Yaxin[1];Yu, Zhuohang[1];Wang, Yimeng[1];Chen, Long[1];Lou, Chaofeng[1];Yang, Chen[1];Li, Weihua[1];Liu, Guixia[1];Tang, Yun[1]

机构：[1]East China Univ Sci & Technol, Shanghai Frontiers Sci Ctr Optogenet Tech Cell Met, Sch Pharm, Shanghai Key Lab New Drug Design, Shanghai 200237, Peoples R China

年份：2024

卷号：52

期号：W1

起止页码：W432

外文期刊名：NUCLEIC ACIDS RESEARCH

收录：;WOS:【SCI-EXPANDED(收录号:WOS:001206437000001)】；

基金：National Key Research and Development Program of China [2023YFF1204904]; National Natural Science Foundation of China [U23A20530 and 82173746]; Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism (Shanghai Municipal Education Commission). Funding for open access charge: National Key Research and Development Program of China [2023YFF1204904].

语种：英文

摘要：Absorption, distribution, metabolism, excretion and toxicity (ADMET) properties play a crucial role in drug discovery and chemical safety assessment. Built on the achievements of admetSAR and its successor, admetSAR2.0, this paper introduced the new version of the series, admetSAR3.0, as a comprehensive platform for chemical ADMET assessment, including search, prediction and optimization modules. In the search module, admetSAR3.0 hosted over 370 000 high-quality experimental ADMET data for 104 652 unique compounds, and supplemented chemical structure similarity search function to facilitate read-across. In the prediction module, we introduced comprehensive ADMET endpoints and two new sections for environmental and cosmetic risk assessments, empowering admetSAR3.0 to provide prediction for 119 endpoints, more than double numbers compared to the previous version. Furthermore, the advanced multi-task graph neural network framework offered robust and reliable support for ADMET prediction. In particular, a module named ADMETopt was added to automatically optimize the ADMET properties of query molecules through transformation rules or scaffold hopping. Finally, admetSAR3.0 provides user-friendly interfaces for multiple types of input data, such as SMILES string, chemical structure and batch molecule file, and supports various output types, including digital, chart displays and file downloads. In summary, admetSAR3.0 is anticipated to be a valuable and powerful tool in drug discovery and chemical safety assessment at http://lmmd.ecust.edu.cn/admetsar3/. Graphical Abstract