| 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 | 973 | 2019 |
| ADMET-score–a comprehensive scoring function for evaluation of chemical drug-likeness L Guan, H Yang, Y Cai, L Sun, P Di, W Li, G Liu, Y Tang Medchemcomm 10 (1), 148-157, 2019 | 413 | 2019 |
| In Silico Prediction of Chemical Toxicity for Drug Design Using Machine Learning Methods and Structural Alerts H Yang, L Sun, W Li, G Liu, Y Tang Frontiers in chemistry 6, 30, 2018 | 227 | 2018 |
| CATMoS: collaborative acute toxicity modeling suite K Mansouri, AL Karmaus, J Fitzpatrick, G Patlewicz, P Pradeep, D Alberga, ... Environmental health perspectives 129 (4), 047013, 2021 | 93 | 2021 |
| 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 | 77 | 2018 |
| ADMETopt: a web server for ADMET optimization in drug design via scaffold hopping H Yang, L Sun, Z Wang, W Li, G Liu, Y Tang Journal of chemical information and modeling 58 (10), 2051-2056, 2018 | 75 | 2018 |
| 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 | 63 | 2017 |
| Computational approaches to identify structural alerts and their applications in environmental toxicology and drug discovery H Yang, C Lou, W Li, G Liu, Y Tang Chemical Research in Toxicology 33 (6), 1312-1322, 2020 | 61 | 2020 |
| Insights into pesticide toxicity against aquatic organism: QSTR models on Daphnia Magna L He, K Xiao, C Zhou, G Li, H Yang, Z Li, J Cheng Ecotoxicology and environmental safety 173, 285-292, 2019 | 56 | 2019 |
| Integrating cell morphology with gene expression and chemical structure to aid mitochondrial toxicity detection S Seal, J Carreras-Puigvert, MA Trapotsi, H Yang, O Spjuth, A Bender Communications Biology 5 (1), 858, 2022 | 44 | 2022 |
| In silico prediction of chemical reproductive toxicity using machine learning C Jiang, H Yang, P Di, W Li, Y Tang, G Liu Journal of Applied Toxicology 39 (6), 844-854, 2019 | 41 | 2019 |
| In silico prediction of chemical genotoxicity using machine learning methods and structural alerts D Fan, H Yang, F Li, L Sun, P Di, W Li, Y Tang, G Liu Toxicology research 7 (2), 211-220, 2018 | 41 | 2018 |
| In silico prediction of pesticide aquatic toxicity with chemical category approaches F Li, D Fan, H Wang, H Yang, W Li, Y Tang, G Liu Toxicology research 6 (6), 831-842, 2017 | 39 | 2017 |
| Study of the solution behavior of β-cyclodextrin amphiphilic polymer inclusion complex and the stability of its O/W emulsion Y Ji, W Kang, L Meng, L Hu, H Yang Colloids and Surfaces A: Physicochemical and Engineering Aspects 453, 117-124, 2014 | 39 | 2014 |
| In silico prediction of chemicals binding to aromatase with machine learning methods H Du, Y Cai, H Yang, H Zhang, Y Xue, G Liu, Y Tang, W Li Chemical Research in Toxicology 30 (5), 1209-1218, 2017 | 38 | 2017 |
| Prediction and mechanistic analysis of drug-induced liver injury (DILI) based on chemical structure A Liu, M Walter, P Wright, A Bartosik, D Dolciami, A Elbasir, H Yang, ... Biology direct 16, 1-15, 2021 | 37 | 2021 |
| 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 | 37 | 2017 |
| In silico prediction of serious eye irritation or corrosion potential of chemicals Q Wang, X Li, H Yang, Y Cai, Y Wang, Z Wang, W Li, Y Tang, G Liu RSC advances 7 (11), 6697-6703, 2017 | 37 | 2017 |
| Comparison of cellular morphological descriptors and molecular fingerprints for the prediction of cytotoxicity-and proliferation-related assays S Seal, H Yang, L Vollmers, A Bender Chemical Research in Toxicology 34 (2), 422-437, 2021 | 32 | 2021 |
| In Silico Prediction of Endocrine Disrupting Chemicals Using Single-Label and Multilabel Models L Sun, H Yang, Y Cai, W Li, G Liu, Y Tang Journal of chemical information and modeling 59 (3), 973-982, 2019 | 30 | 2019 |
Jie LiPhD, Global Health Drug Discovery Institute在 ghddi.org 的电子邮件经过验证
Jie LiPhD, Global Health Drug Discovery Institute在 ghddi.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