Year |
Citation |
Score |
2020 |
Ciallella HL, Russo DP, Aleksunes LM, Grimm FA, Zhu H. Predictive modeling of estrogen receptor agonism, antagonism, and binding activities using machine- and deep-learning approaches. Laboratory Investigation; a Journal of Technical Methods and Pathology. PMID 32778734 DOI: 10.1038/S41374-020-00477-2 |
0.359 |
|
2020 |
Zhao L, Ciallella HL, Aleksunes LM, Zhu H. Advancing computer-aided drug discovery (CADD) by big data and data-driven machine learning modeling. Drug Discovery Today. PMID 32663517 DOI: 10.1016/J.Drudis.2020.07.005 |
0.329 |
|
2020 |
Chen Q, Zhou C, Shi W, Wang X, Xia P, Song M, Liu J, Zhu H, Zhang X, Wei S, Yu H. Mechanistic in silico modeling of bisphenols to predict estrogen and glucocorticoid disrupting potentials. The Science of the Total Environment. 728: 138854. PMID 32570315 DOI: 10.1016/J.Scitotenv.2020.138854 |
0.3 |
|
2020 |
Yan X, Sedykh A, Wang W, Yan B, Zhu H. Construction of a web-based nanomaterial database by big data curation and modeling friendly nanostructure annotations. Nature Communications. 11: 2519. PMID 32433469 DOI: 10.1038/S41467-020-16413-3 |
0.309 |
|
2020 |
Qi X, Li X, Yao H, Huang Y, Cai X, Chen J, Zhu H. Predicting plant cuticle-water partition coefficients for organic pollutants using pp-LFER model. The Science of the Total Environment. 725: 138455. PMID 32315909 DOI: 10.1016/J.Scitotenv.2020.138455 |
0.339 |
|
2020 |
Zhao L, Russo DP, Wang W, Aleksunes LM, Zhu H. Mechanism-driven Read-Across of Chemical Hepatotoxicants Based on Chemical Structures and Biological Data. Toxicological Sciences : An Official Journal of the Society of Toxicology. PMID 32073637 DOI: 10.1093/Toxsci/Kfaa005 |
0.385 |
|
2020 |
Liu G, Yan X, Sedykh A, Pan X, Zhao X, Yan B, Zhu H. Analysis of model PM-induced inflammation and cytotoxicity by the combination of a virtual carbon nanoparticle library and computational modeling. Ecotoxicology and Environmental Safety. 191: 110216. PMID 31972454 DOI: 10.1016/J.Ecoenv.2020.110216 |
0.318 |
|
2019 |
Guo Y, Zhao L, Zhang X, Zhu H. Using a hybrid read-across method to evaluate chemical toxicity based on chemical structure and biological data. Ecotoxicology and Environmental Safety. 178: 178-187. PMID 31004930 DOI: 10.1016/J.Ecoenv.2019.04.019 |
0.371 |
|
2019 |
Yan X, Sedykh A, Wang W, Zhao X, Yan B, Zhu H. In silico profiling nanoparticles: predictive nanomodeling using universal nanodescriptors and various machine learning approaches. Nanoscale. PMID 30984943 DOI: 10.1039/C9Nr00844F |
0.321 |
|
2019 |
Russo DP, Strickland J, Karmaus AL, Wang W, Shende S, Hartung T, Aleksunes LM, Zhu H. Nonanimal Models for Acute Toxicity Evaluations: Applying Data-Driven Profiling and Read-Across. Environmental Health Perspectives. 127: 47001. PMID 30933541 DOI: 10.1289/Ehp3614 |
0.342 |
|
2019 |
Ciallella HL, Zhu H. Advancing Computational Toxicology in the Big Data Era by Artificial Intelligence: Data-Driven and Mechanism-Driven Modeling for Chemical Toxicity. Chemical Research in Toxicology. PMID 30907586 DOI: 10.1021/Acs.Chemrestox.8B00393 |
0.382 |
|
2019 |
Wang W, Yan X, Zhao L, Russo DP, Wang S, Liu Y, Sedykh A, Zhao X, Yan B, Zhu H. Universal nanohydrophobicity predictions using virtual nanoparticle library. Journal of Cheminformatics. 11: 6. PMID 30659400 DOI: 10.1186/S13321-019-0329-8 |
0.305 |
|
2018 |
Russo DP, Zorn KM, Clark AM, Zhu H, Ekins S. Comparing Multiple Machine Learning Algorithms and Metrics for Estrogen Receptor Binding Prediction. Molecular Pharmaceutics. PMID 30114914 DOI: 10.1021/Acs.Molpharmaceut.8B00546 |
0.315 |
|
2017 |
Wang W, Sedykh A, Sun H, Zhao L, Russo DP, Zhou H, Yan B, Zhu H. Predicting Nano-bio Interactions by Integrating Nanoparticle Libraries and Quantitative Nanostructure Activity Relationship Modeling. Acs Nano. PMID 29149552 DOI: 10.1021/Acsnano.7B07093 |
0.318 |
|
2017 |
Bai X, Liu F, Liu Y, Li C, Wang S, Zhou H, Wang W, Zhu H, Winkler D, Yan B. Toward a systematic exploration of nano-bio interactions. Toxicology and Applied Pharmacology. PMID 28344110 DOI: 10.1016/J.Taap.2017.03.011 |
0.338 |
|
2017 |
Russo DP, Kim MT, Wang W, Pinolini D, Shende S, Strickland J, Hartung T, Zhu H. CIIPro: a new read-across portal to fill data gaps using public large-scale chemical and biological data. Bioinformatics (Oxford, England). 33: 464-466. PMID 28172359 DOI: 10.1093/Bioinformatics/Btw640 |
0.327 |
|
2017 |
Hamm J, Sullivan K, Clippinger AJ, Strickland J, Bell S, Bhhatarai B, Blaauboer B, Casey W, Dorman D, Forsby A, Garcia-Reyero N, Gehen S, Graepel R, Hotchkiss J, Lowit A, ... ... Zhu H, et al. Alternative approaches for identifying acute systemic toxicity: Moving from research to regulatory testing. Toxicology in Vitro : An International Journal Published in Association With Bibra. PMID 28069485 DOI: 10.1016/J.Tiv.2017.01.004 |
0.307 |
|
2016 |
Ribay K, Kim MT, Wang W, Pinolini D, Zhu H. Predictive Modeling of Estrogen Receptor Binding Agents Using Advanced Cheminformatics Tools and Massive Public Data. Frontiers in Environmental Science. 4. PMID 27642585 DOI: 10.3389/Fenvs.2016.00012 |
0.382 |
|
2016 |
Kim MT, Wang W, Sedykh A, Zhu H. Curating and Preparing High-Throughput Screening Data for Quantitative Structure-Activity Relationship Modeling. Methods in Molecular Biology (Clifton, N.J.). 1473: 161-72. PMID 27518634 DOI: 10.1007/978-1-4939-6346-1_17 |
0.313 |
|
2016 |
Russo DP, Zhu H. Accessing the High-Throughput Screening Data Landscape. Methods in Molecular Biology (Clifton, N.J.). 1473: 153-9. PMID 27518633 DOI: 10.1007/978-1-4939-6346-1_16 |
0.323 |
|
2016 |
Xiang J, Zhang Z, Mu Y, Xu X, Guo S, Liu Y, Russo DP, Zhu H, Yan B, Bai X. Discovery of Novel Tricyclic Thiazepine Derivatives as Anti-Drug-Resistant Cancer Agents by Combining Diversity-Oriented Synthesis and Converging Screening Approach. Acs Combinatorial Science. PMID 27082930 DOI: 10.1021/Acscombsci.6B00010 |
0.304 |
|
2016 |
Ball N, Cronin MT, Shen J, Adenuga MD, Blackburn K, Booth ED, Bouhifd M, Donley E, Egnash L, Freeman JJ, Hastings C, Juberg DR, Kleensang A, Kleinstreuer N, Kroese ED, ... ... Zhu H, et al. Toward Good Read-Across Practice (GRAP) guidance. Altex. PMID 26863606 DOI: 10.14573/Altex.1601251 |
0.303 |
|
2016 |
Zhu H, Bouhifd M, Kleinstreuer N, Kroese ED, Liu Z, Luechtefeld T, Pamies D, Shen J, Strauss V, Wu S, Hartung T. Supporting read-across using biological data. Altex. PMID 26863516 DOI: 10.14573/Altex.1601252 |
0.336 |
|
2016 |
Luechtefeld T, Maertens A, Russo DP, Rovida C, Zhu H, Hartung T. Analysis of public oral toxicity data from REACH registrations 2008-2014. Altex. PMID 26863198 DOI: 10.14573/Altex.1510054 |
0.307 |
|
2016 |
Luechtefeld T, Maertens A, Russo DP, Rovida C, Zhu H, Hartung T. Global analysis of publicly available safety data for 9,801 substances registered under REACH from 2008-2014. Altex. PMID 26863090 DOI: 10.14573/Altex.1510052 |
0.322 |
|
2015 |
Kim MT, Huang R, Sedykh A, Wang W, Xia M, Zhu H. Mechanism Profiling of Hepatotoxicity Caused by Oxidative Stress Using the Antioxidant Response Element Reporter Gene Assay Models and Big Data. Environmental Health Perspectives. PMID 26383846 DOI: 10.1289/Ehp.1509763 |
0.301 |
|
2015 |
Wang W, Kim MT, Sedykh A, Zhu H. Developing Enhanced Blood-Brain Barrier Permeability Models: Integrating External Bio-Assay Data in QSAR Modeling. Pharmaceutical Research. PMID 25862462 DOI: 10.1007/S11095-015-1687-1 |
0.325 |
|
2014 |
Zhu H, Zhang J, Kim MT, Boison A, Sedykh A, Moran K. Big data in chemical toxicity research: the use of high-throughput screening assays to identify potential toxicants. Chemical Research in Toxicology. 27: 1643-51. PMID 25195622 DOI: 10.1021/Tx500145H |
0.335 |
|
2014 |
Zhang J, Hsieh JH, Zhu H. Profiling animal toxicants by automatically mining public bioassay data: a big data approach for computational toxicology. Plos One. 9: e99863. PMID 24950175 DOI: 10.1371/Journal.Pone.0099863 |
0.357 |
|
2014 |
Sprague B, Shi Q, Kim MT, Zhang L, Sedykh A, Ichiishi E, Tokuda H, Lee KH, Zhu H. Design, synthesis and experimental validation of novel potential chemopreventive agents using random forest and support vector machine binary classifiers. Journal of Computer-Aided Molecular Design. 28: 631-46. PMID 24840854 DOI: 10.1007/S10822-014-9748-9 |
0.369 |
|
2013 |
Zhu XW, Sedykh A, Zhu H, Liu SS, Tropsha A. The use of pseudo-equilibrium constant affords improved QSAR models of human plasma protein binding. Pharmaceutical Research. 30: 1790-8. PMID 23568522 DOI: 10.1007/S11095-013-1023-6 |
0.345 |
|
2013 |
Sedykh A, Fourches D, Duan J, Hucke O, Garneau M, Zhu H, Bonneau P, Tropsha A. Human intestinal transporter database: QSAR modeling and virtual profiling of drug uptake, efflux and interactions. Pharmaceutical Research. 30: 996-1007. PMID 23269503 DOI: 10.1007/S11095-012-0935-X |
0.335 |
|
2013 |
Zhang L, Fourches D, Sedykh A, Zhu H, Golbraikh A, Ekins S, Clark J, Connelly MC, Sigal M, Hodges D, Guiguemde A, Guy RK, Tropsha A. Discovery of novel antimalarial compounds enabled by QSAR-based virtual screening. Journal of Chemical Information and Modeling. 53: 475-92. PMID 23252936 DOI: 10.1021/Ci300421N |
0.367 |
|
2013 |
Zhu H. From QSAR to QSIIR: Searching for enhanced computational toxicology models Methods in Molecular Biology. 930: 53-65. PMID 23086837 DOI: 10.1007/978-1-62703-059-5_3 |
0.39 |
|
2012 |
Solimeo R, Zhang J, Kim M, Sedykh A, Zhu H. Predicting chemical ocular toxicity using a combinatorial QSAR approach. Chemical Research in Toxicology. 25: 2763-2769. PMID 23148656 DOI: 10.1021/Tx300393V |
0.374 |
|
2012 |
Martin TM, Harten P, Young DM, Muratov EN, Golbraikh A, Zhu H, Tropsha A. Does rational selection of training and test sets improve the outcome of QSAR modeling? Journal of Chemical Information and Modeling. 52: 2570-8. PMID 23030316 DOI: 10.1021/Ci300338W |
0.314 |
|
2011 |
Low Y, Uehara T, Minowa Y, Yamada H, Ohno Y, Urushidani T, Sedykh A, Muratov E, Kuz'min V, Fourches D, Zhu H, Rusyn I, Tropsha A. Predicting drug-induced hepatotoxicity using QSAR and toxicogenomics approaches. Chemical Research in Toxicology. 24: 1251-62. PMID 21699217 DOI: 10.1021/Tx200148A |
0.373 |
|
2011 |
Sedykh A, Zhu H, Tang H, Zhang L, Richard A, Rusyn I, Tropsha A. Use of in vitro HTS-derived concentration-response data as biological descriptors improves the accuracy of QSAR models of in vivo toxicity. Environmental Health Perspectives. 119: 364-70. PMID 20980217 DOI: 10.1289/Ehp.1002476 |
0.386 |
|
2010 |
Rodgers AD, Zhu H, Fourches D, Rusyn I, Tropsha A. Modeling liver-related adverse effects of drugs using knearest neighbor quantitative structure-activity relationship method. Chemical Research in Toxicology. 23: 724-32. PMID 20192250 DOI: 10.1021/Tx900451R |
0.329 |
|
2009 |
Zhu H, Martin TM, Ye L, Sedykh A, Young DM, Tropsha A. Quantitative structure-activity relationship modeling of rat acute toxicity by oral exposure. Chemical Research in Toxicology. 22: 1913-21. PMID 19845371 DOI: 10.1021/Tx900189P |
0.378 |
|
2009 |
Zhu H, Ye L, Richard A, Golbraikh A, Wright FA, Rusyn I, Tropsha A. A novel two-step hierarchical quantitative structure-activity relationship modeling work flow for predicting acute toxicity of chemicals in rodents. Environmental Health Perspectives. 117: 1257-64. PMID 19672406 DOI: 10.1289/Ehp.0800471 |
0.387 |
|
2008 |
Tetko IV, Sushko I, Pandey AK, Zhu H, Tropsha A, Papa E, Oberg T, Todeschini R, Fourches D, Varnek A. Critical assessment of QSAR models of environmental toxicity against Tetrahymena pyriformis: focusing on applicability domain and overfitting by variable selection. Journal of Chemical Information and Modeling. 48: 1733-46. PMID 18729318 DOI: 10.1021/Ci800151M |
0.367 |
|
2008 |
Zhang L, Zhu H, Oprea TI, Golbraikh A, Tropsha A. QSAR modeling of the blood-brain barrier permeability for diverse organic compounds. Pharmaceutical Research. 25: 1902-14. PMID 18553217 DOI: 10.1007/S11095-008-9609-0 |
0.373 |
|
2008 |
Zhu H, Rusyn I, Richard A, Tropsha A. Use of cell viability assay data improves the prediction accuracy of conventional quantitative structure-activity relationship models of animal carcinogenicity. Environmental Health Perspectives. 116: 506-13. PMID 18414635 DOI: 10.1289/Ehp.10573 |
0.341 |
|
2008 |
Zhu H, Tropsha A, Fourches D, Varnek A, Papa E, Gramatica P, Oberg T, Dao P, Cherkasov A, Tetko IV. Combinatorial QSAR modeling of chemical toxicants tested against Tetrahymena pyriformis. Journal of Chemical Information and Modeling. 48: 766-84. PMID 18311912 DOI: 10.1021/Ci700443V |
0.382 |
|
2005 |
Klopman G, Zhu H. Recent methodologies for the estimation of n-octanol/water partition coefficients and their use in the prediction of membrane transport properties of drugs. Mini Reviews in Medicinal Chemistry. 5: 127-33. PMID 15720283 DOI: 10.2174/1389557053402765 |
0.505 |
|
2005 |
Zhu H, Sedykh A, Chakravarti S, Klopman G. A New Group Contribution Approach to the Calculation of LogP Current Computer Aided-Drug Design. 1: 3-9. DOI: 10.2174/1573409052952323 |
0.53 |
|
2004 |
Klopman G, Zhu H, Fuller MA, Saiakhov RD. Searching for an enhanced predictive tool for mutagenicity. Sar and Qsar in Environmental Research. 15: 251-63. PMID 15370416 DOI: 10.1080/10629360410001724897 |
0.524 |
|
2004 |
Klopman G, Chakravarti SK, Zhu H, Ivanov JM, Saiakhov RD. ESP: a method to predict toxicity and pharmacological properties of chemicals using multiple MCASE databases. Journal of Chemical Information and Computer Sciences. 44: 704-15. PMID 15032553 DOI: 10.1021/Ci030298N |
0.513 |
|
2003 |
Klopman G, Zhu H, Ecker G, Chiba P. MCASE study of the multidrug resistance reversal activity of propafenone analogs. Journal of Computer-Aided Molecular Design. 17: 291-7. PMID 14635722 DOI: 10.1023/A:1026124505322 |
0.479 |
|
2001 |
Klopman G, Zhu H. Estimation of the aqueous solubility of organic molecules by the group contribution approach. Journal of Chemical Information and Computer Sciences. 41: 439-45. PMID 11277734 DOI: 10.1021/Ci000152D |
0.541 |
|
2001 |
Klopman G, Zhu H. Estimation of the Aqueous Solubility of Organic Molecules by the Group Contribution Approach J. Chem. Inf. Comput. Sci. 41, 439−455 (2001) Journal of Chemical Information and Computer Sciences. 41: 1096-1097. DOI: 10.1021/Ci010048B |
0.476 |
|
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