World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
43
Citations
7406
World Ranking
8015
National Ranking
3446

Overview

Hao Zhu is affiliated with Rutgers, The State University of New Jersey in the United States. Their research activities primarily focus on the field of Medicine, with specific contributions extending into several subfields including Computational Theory and Mathematics, Materials Chemistry, Cognitive Neuroscience, Molecular Biology, and Pharmacology.

The scientist's main topics of work encompass Computational Drug Discovery Methods, Maternal and Fetal Healthcare, Machine Learning in Materials Science, Metabolomics and Mass Spectrometry Studies, Pregnancy and Preeclampsia Studies, Pharmacogenetics and Drug Metabolism, and Animal Testing and Alternatives.

Hao Zhu has published multiple research papers in various scientific venues. Notable recent papers include:

  • Advancing computer-aided drug discovery (CADD) by big data and data-driven machine learning modeling, 2020, Drug Discovery Today
  • Construction of a web-based nanomaterial database by big data curation and modeling friendly nanostructure annotations, 2020, Nature Communications
  • CATMoS: Collaborative Acute Toxicity Modeling Suite, 2021, Environmental Health Perspectives
  • Advancing Computational Toxicology by Interpretable Machine Learning, 2023, Environmental Science & Technology
  • Converting Nanotoxicity Data to Information Using Artificial Intelligence and Simulation, 2023, Chemical Reviews

Frequent publication venues where Hao Zhu's work appears include Environmental Science & Technology, SSRN Electronic Journal, UNC Libraries, Environmental Health Perspectives, and ACS Sustainable Chemistry & Engineering.

Among coauthors who have collaborated with Hao Zhu frequently are Daniel P. Russo, Lauren M. Aleksunes, Heather L. Ciallella, Xuelian Jia, and Xiliang Yan.

Hao Zhu has contributed to book literature as well, with a publication titled High-Throughput Screening Assays in Toxicology, released in 2022 by Springer Science+Business Media.

Best Publications

  • From machine learning to deep learning: progress in machine intelligence for rational drug discovery.

    Lu Zhang;Jianjun Tan;Dan Han;Hao Zhu;Hao Zhu

  • Critical Assessment of QSAR Models of Environmental Toxicity against Tetrahymena pyriformis: Focusing on Applicability Domain and Overfitting by Variable Selection

    Igor V. Tetko;Iurii Sushko;Anil Kumar Pandey;Hao Zhu

  • Big Data and Artificial Intelligence Modeling for Drug Discovery.

    Hao Zhu

  • Does rational selection of training and test sets improve the outcome of QSAR modeling

    Todd M. Martin;Paul Harten;Douglas M. Young;Eugene N. Muratov;Eugene N. Muratov

  • Combinatorial QSAR Modeling of Chemical Toxicants Tested against Tetrahymena pyriformis

    Hao Zhu;Alexander Tropsha;Denis Fourches;Alexandre Varnek

  • Quantitative structure-activity relationship modeling of rat acute toxicity by oral exposure.

    Hao Zhu;Todd M. Martin;Lin Ye;Alexander Sedykh

  • Predicting Drug-induced Hepatotoxicity Using QSAR and Toxicogenomics Approaches

    Yen Low;Takeki Uehara;Yohsuke Minowa;Hiroshi Yamada

  • QSAR Modeling of the Blood–Brain Barrier Permeability for Diverse Organic Compounds

    Liying Zhang;Hao Zhu;Tudor I. Oprea;Alexander Golbraikh

  • Advancing Computational Toxicology in the Big Data Era by Artificial Intelligence: Data-Driven and Mechanism-Driven Modeling for Chemical Toxicity

    Heather L. Ciallella;Hao Zhu

  • Advancing computer-aided drug discovery (CADD) by big data and data-driven machine learning modeling.

    Linlin Zhao;Heather L. Ciallella;Lauren M. Aleksunes;Hao Zhu

  • Toward Good Read-Across Practice (GRAP) guidance

    Nicholas Ball;Mark T.D. Cronin;Jie Shen;Karen Blackburn

  • Estimation of the aqueous solubility of organic molecules by the group contribution approach.

    Gilles Klopman;Hao Zhu

  • Big Data in Chemical Toxicity Research: The Use of High-Throughput Screening Assays To Identify Potential Toxicants

    Hao Zhu;Jun Zhang;Marlene T. Kim;Abena Boison

  • Comparing Multiple Machine Learning Algorithms and Metrics for Estrogen Receptor Binding Prediction

    Daniel P. Russo;Kimberley M. Zorn;Alex Michael Clark;Hao Zhu

  • Modeling Liver-Related Adverse Effects of Drugs Using kNearest Neighbor Quantitative Structure−Activity Relationship Method

    Amie D. Rodgers;Hao Zhu;Dennis Fourches;Ivan Rusyn

  • CATMoS: Collaborative Acute Toxicity Modeling Suite.

    Kamel Mansouri;Agnes L. Karmaus;Jeremy Fitzpatrick;Grace Patlewicz

  • Use of in vitro HTS-derived concentration-response data as biological descriptors improves the accuracy of QSAR models of in vivo toxicity.

    Alexander Sedykh;Hao Zhu;Hao Tang;Liying Zhang

  • Construction of a web-based nanomaterial database by big data curation and modeling friendly nanostructure annotations

    Xiliang Yan;Xiliang Yan;Alexander Sedykh;Alexander Sedykh;Wenyi Wang;Bing Yan;Bing Yan

  • Discovery of novel antimalarial compounds enabled by QSAR-based virtual screening.

    Liying Zhang;Denis Fourches;Alexander Sedykh;Hao Zhu

  • Analysis of Draize eye irritation testing and its prediction by mining publicly available 2008-2014 REACH data.

    Thomas Luechtefeld;Alexandra Maertens;Daniel P. Russo;Costanza Rovida

  • Identification of putative estrogen receptor-mediated endocrine disrupting chemicals using QSAR- and structure-based virtual screening approaches.

    Liying Zhang;Alexander Sedykh;Ashutosh Tripathi;Hao Zhu

  • Supporting read-across using biological data.

    Hao Zhu;Mounir Bouhifd;Elizabeth Donley;Laura Egnash

Frequent Co-Authors

Alexander Tropsha
Alexander Tropsha University of North Carolina at Chapel Hill
Thomas Hartung
Thomas Hartung Johns Hopkins University
Denis Fourches
Denis Fourches North Carolina State University
Eugene N. Muratov
Eugene N. Muratov University of North Carolina at Chapel Hill
Igor V. Tetko
Igor V. Tetko Helmholtz Zentrum München
Lauren M. Aleksunes
Lauren M. Aleksunes Rutgers, The State University of New Jersey
Sean Ekins
Sean Ekins University of Arizona
Fred A. Wright
Fred A. Wright North Carolina State University
Miao Zhang
Miao Zhang Beijing Jiaotong University
Guotong Du
Guotong Du Jilin University

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