Dr. Zhu is a Professor of Biomedical Informatics and Genomics at the Tulane University Medical School. His major research interest is to use cheminformatics tools to develop predictive models. All resulted models can be used to directly predict the chemical efficacy and toxicity based on the public big data and molecular structure information. His current research interests also include data-driven modeling, artificial intelligence algorithm development and computer-aided nanomedicine design. He is the Principal Investigator (PI) of several prestigious research grants (NIH R01, NIH U02, NSF, NIH R15 and etc). Dr. Zhu is author/co-author of near 100 peer-reviewed journal articles and 10 book chapters with over 6600 citations. His research was recognized with different awards, such as Rutgers Chancellor’s Award for Outstanding Research and Creative Activity, Society of Toxicology Best Paper of the Year (two times, 2021 and 2023), National Institute of Environmental Health Sciences (NIEHS) Extramural Paper of the Month (three times, 2019, 2020 and 2022) and Drug Discovery Today top citation paper of the year (2018).
1. 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. PMC9262097.
2. 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 May 20;11(1):2519. PMC7239871.
3. Zhu H. Big Data and Artificial Intelligence Modeling for Drug Discovery. Annu Rev Pharmacol Toxicol. 2020 Jan 6;60:573-589. PMC7010403.
4. 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. Environ Health Perspect. 2019 Apr;127(4):47001. PMC6785238.