| 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 | 935 | 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 | 399 | 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 | 226 | 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 | 83 | 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 | 75 | 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 | 74 | 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 | 61 | 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 | 56 | 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 | 51 | 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 |
| 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 | 39 | 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 | 39 | 2019 |
| 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 |
| 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 | 38 | 2014 |
| 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 | 36 | 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 | 36 | 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 | 36 | 2017 |
| 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 | 35 | 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 | 29 | 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 | 28 | 2019 |
Jie LiPhD, Global Health Drug Discovery InstituteVerified email at ghddi.org
