Zhu
Zhu
Hao Zhu, Ph.D.
Professor
Biography
Education:
BS(Inorganic Chemistry), Jilin University, China
MS(Applied Chemistry), Peking University, China
PhD(Computational Chemistry), Case Western Reserve University
Postdoc(Cheminformatics), Case Western Reserve University
Research Expertise:
Computational toxicology, Nanoinformatics, Computer-aided drug discovery, Machine learning modeling of chemical and biological data
Award:
2022 NIEHS Extramural Paper of the Month (August, 2022)
2021 Society of Toxicology Computational Toxicology Best Paper of the Year
2021 Chancellor’s Award for Outstanding Research and Creative Activity at Rutgers
2020 NIEHS Extramural Paper of the Month (July, 2020)
2019 NIEHS Extramural Paper of the Month (June, 2019)
2019 Colgate-Palmolive Research Grant Recipient
2017 Big Data Initiative Grant Recipient
2017 Colgate-Palmolive Research Grant Recipient
2016 Early Tenure Promotion in Department of Chemistry at Rutgers-Camden
2016 Rutgers University's Research Council Grant Recipient
2016-2018 Johns Hopkins Center for Alternatives to Animal Testing Grant Recipient
2015 Rutgers University's Environmental Health Pilot Grant Recipient
2013 Colgate-Palmolive Research Grant Recipient
2013 Rutgers University's Research Council Grant Recipient
2011 Colgate-Palmolive Research Grant Recipient
2008-2011 Johns Hopkins Center for Alternatives to Animal Testing Grant Recipient
1997 Guanghua Graduate Student Award, Peking University
1991-1995 Undergraduate Fellowship, Jilin University
Member of:
Society of Toxicology
American Chemical Society
American Society for Cellular and Computational Toxicology
Recent Publications:
Jia X, Wen X, Russo D, Aleksunes L M, Zhu H* Mechanism-driven Modeling of Chemical Hepatotoxicity Using Structural Alerts and an In Vitro Screening Assay. J. Hazard. Mater., 2022, (436) 129193. (NIEHS Extramural Paper of the Month)
Ciallella H, Russo D, Sharma S, Li Y, Sloter E, Sweet L, Zhu H* Predicting Prenatal Developmental Toxicity Based On the Combination of Chemical Structures and Biological Data. Environ. Sci. Technol., 2022, (56) 5984–5998.
Marques E, Pfohl M, Wei W, Tarantola G, Ford L, Amaeze O, Alesio J, Ryu S, Jia X, Zhu H, Bothun G, Slitt A Replacement per- and polyfluoroalkyl substances (PFAS) are potent modulators of lipogenic and drug metabolizing gene expression signatures in primary human hepatocytes. Toxicol. Appl. Pharmacol.，2022; (442) 1115991.
Yan J, Yan X, Hu S, Zhu H, Yan B Comprehensive Interrogation on Acetylcholinesterase Inhibition by Ionic Liquids Using Machine Learning and Molecular Modeling. Environ. Sci. Technol., 2021, (55) 14720–14731.
Zhu H*, Chen J, Huang R, Hong H Sustainable Management of Synthetic Chemicals. ACS Sustainable Chem. Eng. 2021, (9) 13703-13704 (Invited Editorial)
Ciallella H, Russo D, Aleksunes L M, Grimm F, Zhu H* Revealing Adverse Outcome Pathways from Public High-Throughput Screening Data to Evaluate New Toxicants by a Knowledge-Based Deep Neural Network Approach. Environ. Sci. Technol., 2021, (55) 10875-10887. (Society of Toxicology Computational Toxicology Best Paper Award)
Mansouri K. and other 102 co-authors including Zhu H CATMoS: Collaborative Acute Toxicity Modeling Suite. Environ. Health Perspect. 2021, (129) 47013
Jia X; Ciallella H; Russo D; Zhao L; James M; Zhu H* Construction of a Virtual Opioid Bioprofile: a Data-driven QSAR Modeling Study to Identify New Analgesic Opioids. ACS Sustainable Chem. Eng. 2021, (9) 3909-3919
Ciallella H, Russo D, Aleksunes L M, Grimm F, Zhu H* Predictive Modeling of Estrogen Receptor Agonism, Antagonism, and Binding Activities Using Machine and Deep Learning Approaches. Lab Invest., 2021, (101) 490–502
Yan X, Zhang J, Russo D, Zhu H*, Yan B Prediction of Nano–Bio Interactions through Convolutional Neural Network Analysis of Nanostructure Images. ACS Sustainable Chem. Eng. 2020, (8) 19096–19104
Russo D, Yan X, Shende S, Huang H, Yan B, Zhu H* Virtual molecular projections and convolutional neural networks for end-to-end modeling of nanoparticle activities and properties. Anal. Chem., 2020; (92) 13971-13979.
Wang Y, Russo D, Liu C, Zhou Q, Zhu H*, Zhang Y Predictive modeling of angiotensin I-converting enzyme (ACE) inhibitory peptides using various machine learning approaches. J. Agric. Food Chem., 2020; (68):12132-12140.
Zhao L, Ciallella H, Aleksunes L M, Zhu H* Advancing computer-aided drug discovery (CADD) by big data and data-driven machine learning modeling. Drug Discov Today, 2020 (25) 1624-1638. PMCID: PMC7572559 (Editor Invited Keynote Review)
Gao R, Guan N, Huang M, Foreman J, Kung M, Rong Z, Su Y, Sweet L, Zhu B, Zhu H, Zou H, Li B, Wang Y, Yin H, Yin Z, Zhang X Read-across: Principle, case study and its potential regulatory application in China. Regul. Toxicol. Pharmacol., 2020; (116) 104728.
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. Nat. Commun, 2020; (11) 2519. (NIEHS Extramural Paper of the Month)
Zhou C, Shi W, Zhu H, Yu H, Liu J, Song M, Xia P, Chen Q, Wei S, Zhang X, Wang X Mechanistic in silico modeling of bisphenols to predict estrogen and glucocorticoid disrupting potentials. Sci. Total Environ., 2020, (728) 138854.
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. Sci. Total Environ., 2020, (725) 138455.
Liu G, Yan X, Sedykh A, Pan X, Zhao X, Yan B, Zhu H* Analysis of model PM2.5-induced inflammation and cytotoxicity by the combination of a virtual carbon nanoparticle library and computational modeling. Ecotoxicol. Environ. Saf., 2020; (191) 110216. PMCID: PMC7018436.
Liu Y, Wei Y, Zhang S, Yan X, Zhu H, Xu L, Zhao B, Xie H, Yan B Regulation of aryl hydrocarbon receptor signaling pathway and dioxin toxicity by novel agonists and antagonists. Chem. Res. Toxicol., 2020; (33) 614-624.
Bai X, Wang S, Yan X, Zhou H, Zhan J, Liu S, Sharma V, Jiang G, Zhu H, Yan B Regulation of Cell Uptake and Cytotoxicity by Nanoparticle Core under the Controlled Shape, Size, and Surface Chemistries. ACS Nano, 2020; (14):289-302.
Zhao L, Russo D P, Wang W, Aleksunes L M, Zhu H* Mechanism-driven Read-Across of Chemical Hepatotoxicants Based on Chemical Structures and Biological Data. Toxicol. Sci., 2020; (174) 178-188. PMCID: PMC7098374. (Featured by Society of Toxicology as monthly highlighted paper)
Zhu H* Big Data and Artificial Intelligence Modeling for Drug Discovery. Annual Rev. Pharm. Tox., 2020; (20) 573-589. PMCID: PMC7010403. (Editor invited)
Wang Y, Li B, Xu X, Ren H, Yin J, Zhu H, Zhang Y FTIR spectroscopy coupled with machine learning approaches as a rapid tool for identification and quantification of artificial sweeteners. Food Chem., 2020; (303) 125404.
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. Ecotox. Environ. Saf., 2019; (178) 178-187. PMCID: PMC6508079.
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, 2019; (11) 8352–8362.
Russo D P, Strickland, J, Karmaus A L, Wang W, Shende S, Hartung T, Aleksunes L M, Zhu H* Non-animal models for acute toxicity evaluations: applying data-driven profiling and read-across. Environ. Health Perspect., 2019; (127) 47001. PMCID: PMC6785238. (NIEHS Extramural Paper of the Month)
Ciallella, H, Zhu H* Advancing Computational Toxicology in the Big Data Era by Artificial Intelligence: Data-Driven and Mechanism-Driven Modeling for Chemical Toxicity. Chem. Res. Tox., 2019; (32) 536−547. PMCID: PMC6688471. (Editor invited perspective and featured as Chemical Research in Toxicology cover paper)
Wang W, Yan X, Zhao L, Russo D P, Wang S, Liu Y, Sedykh A, Zhao X, Yan B, Zhu H* Universal nanohydrophobicity predictions using virtual nanoparticle library. J. Cheminform., 2019, (11) 6. PMCID: PMC668988
