Recent Publications

Chung E, Wen X, Jia X, Ciallella HL, Aleksunes LM, Zhu H. Hybrid Non-Animal Modeling: A Mechanistic Approach to Predict Chemical Hepatotoxicity. J Hazard Mater. 2024 Jun 5;471:134297. DOI: 10.1016/j.jhazmat.2024.134297

Russo DP, Aleksunes LM, Goyak K, Qian H, Zhu H. Integrating Concentration-Dependent Toxicity Data and Toxicokinetics To Inform Hepatotoxicity Response Pathways. Environ Sci Technol. 2023 Aug 22;57(33):12291–301. DOI: 10.1021/acs.est.3c02792

Yan X, Yue T, Winkler DA, Yin Y, Zhu, H, Jiang G, Yan B. Converting Nanotoxicity Data to Information Using Artificial Intelligence and Simulation. Chem. Rev. 2023. DOI: 10.1021/acs.chemrev.3c00070

Jia X, Wang T, Zhu H. Advancing Computational Toxicology by Interpretable Machine Learning. Environ Sci Technol. 2023 May 24. DOI: 10.1021/acs.est.3c00653 

Chung E, Russo DP, Ciallella HL, Wang YT, Wu M, Aleksunes LM, Zhu H. Data-Driven Quantitative Structure–Activity Relationship Modeling for Human Carcinogenicity by Chronic Oral Exposure. Environ Sci Technol. 2023 Apr 11. DOI: 10.1021/acs.est.3c00648 

Wang T, Russo DP, Bitounis D, Demokritou P, Jia X, Huang H, Zhu H. Integrating structure annotation and machine learning approaches to develop graphene toxicity models. Carbon. 2023 Feb 1;204:484–94. DOI: 10.1016/j.carbon.2022.12.065

Jia X, Wen X, Russo DP, Aleksunes LM, Zhu H. Mechanism-driven modeling of chemical hepatotoxicity using structural alerts and an in vitro screening assay. J Hazard Mater. 2022 Aug 15;436:129193. DOI: 10.1016/j.jhazmat.2022.129193

Marques E, Pfohl M, Wei W, Tarantola G, Ford L, Amaeze O, Alesio J, Ryu S, Jia X, Zhu H, Bothun GD, 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 May 1;442:115991. DOI: 10.1016/j.taap.2022.115991

Ciallella HL, Russo DP, Sharma S, Li Y, Sloter E, Sweet L, Huang H, Zhu H. Predicting Prenatal Developmental Toxicity Based On the Combination of Chemical Structures and Biological Data. Environ Sci Technol. 2022 May 3;56(9):5984–98. DOI: 10.1021/acs.est.2c01040

Russo DP, Zhu H. High-Throughput Screening Assay Profiling for Large Chemical Databases. In: High-Throughput Screening Assays in Toxicology. New York, NY: Springer US; 2022; p. 125–32. DOI: 10.1007/978-1-0716-2213-1_12 

Liu MQ, Wang T, Wang QL, Zhou J, Wang BR, Zhang B, Wang KL, Zhu H, Zhang YH. Structure-guided discovery of food-derived GABA-T inhibitors as hunters for anti-anxiety compounds. Food Funct. 2022;13(24):12674–85. 

Yan X, Yue T, Zhu H, Yan B. Bridging the Gap Between Nanotoxicological Data and the Critical Structure–Activity Relationships. In: Advances in Toxicology and Risk Assessment of Nanomaterials and Emerging Contaminants. Singapore: Springer; 2022; p. 161–83. DOI: 10.1007/978-981-16-9116-4_7

Ciallella HL, Chung E, Russo DP, Zhu H. Automatic Quantitative Structure–Activity Relationship Modeling to Fill Data Gaps in High-Throughput Screening. In: High-Throughput Screening Assays in Toxicology. New York, NY: Springer US; 2022; p. 169–87. DOI: 10.1007/978-1-0716-2213-1_16 

Zhu H, Chen J, Huang R, Hong H. Sustainable Management of Synthetic Chemicals. ACS Sustain Chem Eng. 2021 Oct 18;9(41):13703–4. 

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 Nov 2;55(21):14720–31. 

Ciallella HL, Russo DP, Aleksunes LM, Grimm FA, 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 Aug 3;55(15):10875–87. 

Mansouri K, Karmaus AL, Fitzpatrick J, Patlewicz G, Pradeep P, Alberga D, Alepee N, Allen TEH, Allen D, Alves VM, Andrade CH, Auernhammer TR, Ballabio D, Bell S, Benfenati E, Bhattacharya S, Bastos JV, Boyd S, Brown JB, Capuzzi SJ, Chushak Y, Ciallella H, Clark AM, Consonni V, Daga PR, Ekins S, Farag S, Fedorov M, Fourches D, Gadaleta D, Gao F, Gearhart JM, Goh G, Goodman JM, Grisoni F, Grulke CM, Hartung T, Hirn M, Karpov P, Korotcov A, Lavado GJ, Lawless M, Li X, Luechtefeld T, Lunghini F, Mangiatordi GF, Marcou G, Marsh D, Martin T, Mauri A, Muratov EN, Myatt GJ, Nguyen DT, Nicolotti O, Note R, Pande P, Parks AK, Peryea T, Polash AH, Rallo R, Roncaglioni A, Rowlands C, Ruiz P, Russo DP, Sayed A, Sayre R, Sheils T, Siegel C, Silva AC, Simeonov A, Sosnin S, Southall N, Strickland J, Tang Y, Teppen B, Tetko IV, Thomas D, Tkachenko V, Todeschini R, Toma C, Tripodi I, Trisciuzzi D, Tropsha A, Varnek A, Vukovic K, Wang Z, Wang L, Waters KM, Wedlake AJ, Wijeyesakere SJ, Wilson D, Xiao Z, Yang H, Zahoranszky -Kohalmi Gergely, Zakharov AV, Zhang FF, Zhang Z, Zhao T, Zhu H, Zorn KM, Casey W, Kleinstreuer NC. CATMoS: Collaborative Acute Toxicity Modeling Suite. Environ Health Perspect. 129(4):047013.

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. Lab Invest. 2021 Apr;101(4):490–502. 

Jia X, Ciallella HL, Russo DP, Zhao L, James MH, Zhu H. Construction of a Virtual Opioid Bioprofile: A Data-Driven QSAR Modeling Study to Identify New Analgesic Opioids. ACS Sustain Chem Eng. 2021 Mar 15;9(10):3909–19. 

Yan X, Zhang J, Russo DP, Zhu H, Yan B. Prediction of Nano–Bio Interactions through Convolutional Neural Network Analysis of Nanostructure Images. ACS Sustain Chem Eng. 2020 Dec 28;8(51):19096–104. 

Wang YT, Russo DP, Liu C, Zhou Q, Zhu H, Zhang YH. Predictive Modeling of Angiotensin I-Converting Enzyme Inhibitory Peptides Using Various Machine Learning Approaches. J Agric Food Chem. 2020 Oct 28;68(43):12132–40.

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 Oct 1;116:104728. 

Russo DP, Yan X, Shende S, Huang H, Yan B, Zhu H. Virtual Molecular Projections and Convolutional Neural Networks for the End-to-End Modeling of Nanoparticle Activities and Properties. Anal Chem. 2020 Oct 20;92(20):13971–9. 

Zhao L, Ciallella HL, Aleksunes LM, Zhu H. Advancing computer-aided drug discovery (CADD) by big data and data-driven machine learning modeling. Drug Discov Today. 2020 Sep 1;25(9):1624–38. 

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. Sci Total Environ. 2020 Aug 1;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 Jul 10;725:138455. 

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. 

Zhao L, Russo DP, Wang W, Aleksunes LM, Zhu H. Mechanism-Driven Read-Across of Chemical Hepatotoxicants Based on Chemical Structures and Biological Data. Toxicol Sci. 2020 Apr 1;174(2):178–88. 

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 Mar 15;191:110216. 

Wang YT, Li B, Xu XJ, Ren HB, Yin JY, Zhu H, Zhang YH. FTIR spectroscopy coupled with machine learning approaches as a rapid tool for identification and quantification of artificial sweeteners. Food Chem. 2020 Jan 15;303:125404. 

Zhu H. Big Data and Artificial Intelligence Modeling for Drug Discovery. Annu Rev Pharmacol Toxicol. 2020 Jan 6;60:573–89. 

Liu Y, Wei Y, Zhang S, Yan X, Zhu H, Xu L, Zhao B, Xie HQ, Yan B. Regulation of Aryl Hydrocarbon Receptor Signaling Pathway and Dioxin Toxicity by Novel Agonists and Antagonists. Chem Res Toxicol. 2019 Dec 27;33(2):614–624.

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. 127(4):047001. 

Ciallella HL, Zhu H. Advancing Computational Toxicology in the Big Data Era by Artificial Intelligence: Data-Driven and Mechanism-Driven Modeling for Chemical Toxicity. Chem Res Toxicol. 2019 Apr 15;32(4):536–47. 

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 Apr 25;11(17):8352–62. 

Russo DP, Zorn KM, Clark AM, Zhu H, Ekins S. Comparing Multiple Machine Learning Algorithms and Metrics for Estrogen Receptor Binding Prediction. Mol Pharm. 2018 Oct 1;15(10):4361–70. 

Zhao L, Zhu H. Big Data in Computational Toxicology: Challenges and Opportunities. In: Computational Toxicology. John Wiley & Sons, Ltd; 2018; p. 291–312. DOI: 10.1002/9781119282594.ch11

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. 2017 Dec 26;11(12):12641–9. 

Zhao L, Wang W, Sedykh A, Zhu H. Experimental Errors in QSAR Modeling Sets: What We Can Do and What We Cannot Do. ACS Omega. 2017 Jun 30;2(6):2805–12. 

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. 2017 Feb 1;33(3):464–6. 

Kim MT, Huang R, Sedykh A, Wang W, Xia M, Zhu H. Mechanism Profiling of Hepatotoxicity Caused by Oxidative Stress Using Antioxidant Response Element Reporter Gene Assay Models and Big Data. Environ Health Perspect. 2016 May;124(5):634–41. 

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. Front Environ Sci. 2016;4. 

Kim MT, Wang W, Sedykh A, Zhu H. Curating and Preparing High-Throughput Screening Data for Quantitative Structure-Activity Relationship Modeling. In: High-Throughput Screening Assays in Toxicology. New York, NY: Springer; 2016; p. 161–72. DOI: 10.1007/978-1-4939-6346-1_17 

Wang W, Kim MT, Sedykh A, Zhu H. Developing Enhanced Blood–Brain Barrier Permeability Models: Integrating External Bio-Assay Data in QSAR Modeling. Pharm Res. 2015 Sep 1;32(9):3055–65. 

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. Chem Res Toxicol. 2014 Oct 20;27(10):1643–51. 

Kim MT, Sedykh A, Chakravarti SK, Saiakhov RD, Zhu H. Critical Evaluation of Human Oral Bioavailability for Pharmaceutical Drugs by Using Various Cheminformatics Approaches. Pharm Res. 2014 Apr 1;31(4):1002–14.