1. 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. Safety 2019; (178)178-187.
  2. 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.
  3. 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.
  4. 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. (Editor invited perspective and Chemical Research in Toxicology cover paper)
  5. 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. Cheminformatics, 2019, (11) 6.
  6. Russo D P, Zorn K M, Clark A M, Zhu H, Ekins S Comparing Multiple Machine Learning Algorithms and Metrics for Estrogen Receptor Binding Prediction. Molecular Pharm, 2018; (15) 4361-4370.
  7. Wang W, Sedykh A, Sun H, Zhao L, Russo D P, Zhou H, Yan B, Zhu H* Predicting Nano-bio Interactions by Integrating Nanoparticle Libraries and Quantitative Nanostructure Activity Relationship Modeling. ACS Nano, 2017; (11) 12641-12649.
  8. Zhang L, Tan J, Han D, Zhu H* From machine learning to deep learning: progress in machine intelligence for rational drug discovery. Drug Discov Today, 2017; (22) 1680-1685.
  9. Liu Y, Su G, Wang F, Jia J, Li S, Zhao L, Shi Y, Cai Y, Zhu H, Zhao B, Jiang G, Zhou H, Yan B Elucidation of the Molecular Determinants for Optimal Perfluorooctanesulfonate Adsorption Using a Combinatorial Nanoparticle Library Approach. Environ. Sci. Technol., 2017; (51) 7120-7127.
  10. 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; (2) 2805-2812.
  11. 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. Toxicol. Appl. Pharmacol. 2017; (323) 66-73.
  12. Russo D P, Kim M, 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; (33) 464-466.
  13. Hamm J, Sullivan K, Clippinger A J, 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, Matheson J, Reaves E, Scarano L, Sprankle C, Tunkel J, Wilson D, Xia M, Zhu H, Allen D Alternative approaches for identifying acute systemic toxicity: Moving from research to regulatory testing. Toxicol. in Vitro, 2017; (41) 245-259.
  14. Xiang J, Zhang Z, Mu Y, Xu X, Guo S, Liu Y, Russo D, 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 Comb.Sci. 2016; (18) 230–235.
  15. Ribay K, Kim M, Wang W, Pinolini D, Zhu H* Hybrid Modeling of Estrogen Receptor Binding Agents Using Advanced Cheminformatics Tools and Massive Public Data. Environ. Sci. 2016; (4) 12.
  16. Kim M, Huang R, Sedykh A, Zhang J, Xia M, Zhu H* Mechanism profiling liver toxicants by using antioxidant response element assay data model and public big data. Health Perspect. 2016; (124) 634-641.
  17. Mu Y, Liu Y, Xiang J, Zhang Q, Zhai S, Russo D P, Zhu H, Bai X, Yan B From fighting depression to conquering tumors: a novel tricyclic thiazepine compound as a tubulin polymerization inhibitor Cell Death and Disease. 2016; (7) e2143.
  18. 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. 2016; (33) 167-182. (ALTEX cover paper. Featured together with following 5 ALTEX papers by Science Feb 12, 2016 “A crystal ball for chemical safety” and Nature Feb 11, 2016 “Legal tussle delays launch of huge toxicity database”)
  19. Ball N, Cronin M T, Shen J, Adenuga M D, Blackburn K, Booth E D, Bouhifd M, Donley E, Egnash L, Freeman J J, Hastings C, Juberg D R, Kleensang A, Kleinstreuer N, Kroese E D, Luechtefeld T, Maertens A, Marty S, Naciff J M, Palmer J, Pamies D, Penman M, Richarz A N, Russo D P, Stuard S B, Patlewicz G, van Ravenzwaay B, Wu S, Zhu H, Hartung T Toward Good Read-Across Practice (GRAP) guidance. ALTEX. 2016; (33) 149-166.
  20. Luechtefeld T, Maertens A, Russo D P, Rovida C, Zhu H, Hartung T Analysis of publically available skin sensitization data from REACH registrations 2008-2014. ALTEX. 2016; (33) 135-148.
  21. Luechtefeld T, Maertens A, Russo D P, Rovida C, Zhu H, Hartung T Analysis of Draize eye irritation testing and its prediction by mining publicly available 2008-2014 REACH data. ALTEX. 2016; (33) 123-134.
  22. Luechtefeld T, Maertens A, Russo D P, Rovida C, Zhu H, Hartung T Analysis of public oral toxicity data from REACH registrations 2008-2014. ALTEX. 2016; (33) 111-122.
  23. Luechtefeld T, Maertens A, Russo D P, Rovida C, Zhu H, Hartung T Global analysis of publicly available safety data for 9,801 substances registered under REACH from 2008-2014. ALTEX. 2016; (33) 95-109.
  24. Zhang Y, Wang Y, Liu A, Xu S L, Zhao B, Zhang Y, Zou H, Wang W, Zhu H, Yan B Modulation of Carbon Nanotube’s Perturbation to the Metabolic Activity of CYP3A4 in the Liver. Advanced Functional Materials. 2016; (26) 841-850. (Advanced Functional Materials cover paper)
  25. Wang W, Kim M, Sedykh A, Zhu H* Developing enhanced blood-brain barrier permeability models: Integrating external bio-assay data in QSAR modeling. Res. 2015; (32) 3055-3065.
  26. Liu Y, Li F, Wu L, Wang W, Zhu H, Zhang Q, Zhou H, Yan B Improving both aqueous solubility and anti-cancer activity by assessing progressive lead optimization libraries. Med. Chem. Letter 2015; (25) 1971-1975.
  27. Li S, Zhai S, Liu Y, Zhou H, Wu J, Jiao Q, Zhang B, Zhu H*, Yan B Experimental modulation and computational model of nano-hydrophobicity. 2015; (52) 312-317.
  28. Zhu H*, Zhang J, Kim M, Boison A, Sedykh A, Moran K Big data in chemical toxicity research: The use of high-throughput screening assays to identify potential toxicants. Res. Tox. 2014; (27) 1643-1651. (Featured together with the following PLoS One paper by Chemical Watch Sept 18, 2014 “Trial data mining project flags up Qsar issues”)
  29. Zhang J, Hsieh J-H, Zhu H* Profiling animal toxicants by automatically mining public bioassay data: A big data approach for computational toxicology. PLoS One 2014; (9) e99863.
  30. Sprague B, Shi Q, Kim M, Zhang L, Sedykh A, Ichiishi E, Tokuda H, Lee K H, Zhu H* Design, synthesis and experimental validation of novel potential chemopreventive agents using random forest and support vector machine binary classifiers. Comput. Aided Mol. Des. 2014; (28) 631-646.
  31. Wu L, Zhang Y, Zhang C, Cui X, Zhai S, Liu Y, Li C, Zhu H, Qu G, Jiang G, Yan B Tuning cell autophagy by diversifying carbon nanotube surface chemistry. ACS Nano 2014, (8), 2087–2099.
  32. Kim M, Sedykh A, Chakravarti S K, Saiakhov R D, Zhu H* Critical evaluation of human oral bioavailability for pharmaceutical drugs by using various cheminformatics approaches. Res. 2014; (31) 1002-1014.
  33. Zhang L, Sedykh A, Tripathi A, Zhu H, Afantitis A, Mouchlis VD, Melagraki G, Rusyn I, Tropsha A Identification of putative estrogen receptor-mediated endocrine disrupting chemicals using QSAR- and structure-based virtual screening approaches. Appl. Pharmacol. 2013; (272) 67-76.
  34. 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. Res. 2013; (30) 1790-1798.
  35. 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. Res. 2013; (30) 996-1007.
  36. Zhang L, Fourches D, Sedykh A, Zhu H, Golbraikh A, Ekins S, Clark J, Connelly M C, Sigal M, Hodges D, Guiguemde W A, Guy R K, Tropsha A The discovery of novel antimalarial compounds enabled by QSAR-based virtual screening. Chem. Inf. Model. 2013; (53) 475-492.
  37. Solimeo R, Zhang J, Kim M, Sedykh A, Zhu H* Predicting chemical ocular toxicity using a combinatorial QSAR approach. Res. Tox. 2012; (25) 2763?2769.
  38. Martin T M, Harten P, Young D M, Muratov E N, Golbraikh A, Zhu H, Tropsha A Does rational selection of training and test sets improve the outcome of QSAR modeling? Chem. Inf. Model. 2012; (52):2570-2578.
  39. Kang C, Zhu H, Wright F A, Zou F, Kosorok M R The interactive decision committee for chemical toxicity analysis. J Stat Res. 2012; (46) 157-186.
  40. Ye D, Shi Q, Leung C H, Kim S W, Park S Y, Gullen E A, Jiang ZL, Zhu H, Morris-Natschke S L, Cheng Y C, Lee K H Antitumor agents 294. Novel E-ring-modified camptothecin-4?-anilino-4′-O-demethyl-epipodophyllotoxin conjugates as DNA topoisomerase I inhibitors and cytotoxic agents. Med. Chem. 2012; (20): 4489-4494.

* indicates that I served as the corresponding author