文献类型：期刊文献

英文题名：Profiling prediction of nuclear receptor modulators with multi-task deep learning methods: toward the virtual screening

作者：Wang, Jiye[1];Lou, Chaofeng[1];Liu, Guixia[1];Li, Weihua[1];Wu, Zengrui[1];Tang, Yun[1]

机构：[1]East China Univ Sci & Technol, Shanghai, Peoples R China

年份：2022

卷号：23

期号：5

外文期刊名：BRIEFINGS IN BIOINFORMATICS

收录：;WOS:【SCI-EXPANDED(收录号:WOS:000843871900001)】；

基金：This work was supported by the National Key Research and Development Program of China (Grant 2019YFA0904800), the National Natural Science Foundation of China (Grants 81872800, 82173746 and 82104066) and Shanghai Frontiers Science Center of Optogenetic Techniques for cell Metabolism (Shanghai Municipal Education Commission, Grant 2021 Sci & Tech 03-28).

语种：英文

外文关键词：multi-task deep learning; conventional machine learning; nuclear receptor modulator; multi-classification; virtual screening

摘要：Nuclear receptors (NRs) are ligand-activated transcription factors, which constitute one of the most important targets for drug discovery. Current computational strategies mainly focus on a single target, and the transfer of learned knowledge among NRs was not considered yet. Herein we proposed a novel computational framework named NR-Profiler for prediction of potential NR modulators with high affinity and specificity. First, we built a comprehensive NR data set including 42 684 interactions to connect 42 NRs and 31 033 compounds. Then, we used multi-task deep neural network and multi-task graph convolutional neural network architectures to construct multi-task multi-classification models. To improve the predictive capability and robustness, we built a consensus model with an area under the receiver operating characteristic curve (AUC) = 0.883. Compared with conventional machine learning and structure-based approaches, the consensus model showed better performance in external validation. Using this consensus model, we demonstrated the practical value of NR-Profiler in virtual screening for NRs. In addition, we designed a selectivity score to quantitatively measure the specificity of NR modulators. Finally, we developed a freely available standalone software for users to make profiling predictions for their compounds of interest. In summary, our NR-Profiler provides a useful tool for NR-profiling prediction and is expected to facilitate NR-based drug discovery.