| admetSAR 2.0: web-service for prediction and optimization of chemical ADMET properties H Yang, C Lou, L Sun, J Li, Y Cai, Z Wang, W Li, G Liu, Y Tang Bioinformatics 35 (6), 1067-1069, 2019 | 983 | 2019 |
| SDTNBI: an integrated network and chemoinformatics tool for systematic prediction of drug–target interactions and drug repositioning Z Wu, F Cheng, J Li, W Li, G Liu, Y Tang Briefings in bioinformatics 18 (2), 333-347, 2017 | 146 | 2017 |
| Prediction of chemical–protein interactions: multitarget-QSAR versus computational chemogenomic methods F Cheng, Y Zhou, J Li, W Li, G Liu, Y Tang Molecular BioSystems 8 (9), 2373-2384, 2012 | 132 | 2012 |
| Prediction of polypharmacological profiles of drugs by the integration of chemical, side effect, and therapeutic space F Cheng, W Li, Z Wu, X Wang, C Zhang, J Li, G Liu, Y Tang Journal of chemical information and modeling 53 (4), 753-762, 2013 | 103 | 2013 |
| Network-based identification of microRNAs as potential pharmacogenomic biomarkers for anticancer drugs J Li, K Lei, Z Wu, W Li, G Liu, J Liu, F Cheng, Y Tang Oncotarget 7 (29), 45584, 2016 | 90 | 2016 |
| In silico prediction of chemical mechanism of action via an improved network‐based inference method Z Wu, W Lu, D Wu, A Luo, H Bian, J Li, W Li, G Liu, J Huang, F Cheng, ... British journal of pharmacology 173 (23), 3372-3385, 2016 | 80 | 2016 |
| In silico prediction of compounds binding to human plasma proteins by QSAR models L Sun, H Yang, J Li, T Wang, W Li, G Liu, Y Tang ChemMedChem 13 (6), 572-581, 2018 | 78 | 2018 |
| Computational prediction of microRNA networks incorporating environmental toxicity and disease etiology J Li, Z Wu, F Cheng, W Li, G Liu, Y Tang Scientific reports 4 (1), 5576, 2014 | 68 | 2014 |
| Evaluation of different methods for identification of structural alerts using chemical ames mutagenicity data set as a benchmark H Yang, J Li, Z Wu, W Li, G Liu, Y Tang Chemical Research in Toxicology 30 (6), 1355-1364, 2017 | 65 | 2017 |
| Insights into the molecular mechanisms of Polygonum multiflorum Thunb-induced liver injury: a computational systems toxicology approach Y Wang, J Li, Z Wu, B Zhang, H Yang, Q Wang, Y Cai, G Liu, W Li, Y Tang Acta Pharmacologica Sinica 38 (5), 719-732, 2017 | 39 | 2017 |
| Prediction of human genes and diseases targeted by xenobiotics using predictive toxicogenomic-derived models (PTDMs) F Cheng, W Li, Y Zhou, J Li, J Shen, PW Lee, Y Tang Molecular BioSystems 9 (6), 1316-1325, 2013 | 32 | 2013 |
| Enhance information propagation for graph neural network by heterogeneous aggregations D Leng, J Guo, L Pan, J Li, X Wang arXiv preprint arXiv:2102.04064, 2021 | 10 | 2021 |
| Heterogeneous graph based deep learning for biomedical network link prediction J Guo, J Li, D Leng, L Pan arXiv preprint arXiv:2102.01649, 2021 | 5 | 2021 |
| Real-time tracking of COVID-19 and coronavirus research updates through text mining Y Jin, J Li, X Wang, P Li, J Guo, J Wu, D Leng, L Pan arXiv preprint arXiv:2102.07640, 2021 | 1 | 2021 |
| Predicting Molecule-Target Interaction by Learning Biomedical Network and Molecule Representations J Guo, J Li arXiv preprint arXiv:2302.00981, 2023 | | 2023 |
| Comparative pharmacophore modeling of human adenosine receptor A1 and A3 antagonists ZJ Xu, FX Cheng, J Li, YD Zhou, N Su, WH Li, GX Liu, Y Tang Science China Chemistry 55, 2407-2418, 2012 | | 2012 |
Feixiong ChengCleveland Clinic在 ccf.org 的电子邮件经过验证
Feixiong ChengCleveland Clinic在 ccf.org 的电子邮件经过验证
Zengrui WuSchool of Pharmacy, East China University of Science and Technology在 ecust.edu.cn 的电子邮件经过验证