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  8. Huixiao Hong
  1. Science & Research (NCTR)

Huixiao Hong Ph.D.

Senior Biomedical Research and Biomedical Product Assessment Service Expert — Division of Bioinformatics and Biostatistics

Huixiao Hong
Huixiao Hong, Ph.D.

(870) 543-7121

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About  |  Publications  |  Lab Members


Dr. Huixiao Hong received a Ph.D. from Nanjing University in China where he was also an associate professor and the Director of the Laboratory of Computational Chemistry. He conducted post-doctoral research at the Maxwell Institute in Leeds University, England. Dr. Hong then became a visiting scientist at the National Cancer Institute at the National Institutes of Health and held a research scientist position at Sumitomo Chemical Company in Japan. He was a senior computational scientist at ROW Science and managed the Bioinformatics Division at Z-Tech, an ICFI company, at FDA’s National Center for Toxicological Research (NCTR) from 2002-2007. From 2007-2018, he was an NCTR research chemist and a supervisory research chemist. He served as the Bioinformatics Branch Chief in the Division of Bioinformatics and Biostatistics from 2018-2021 and is currently a Senior Biomedical Research and Biomedical Product Assessment Service (SBRBPAS) expert and Bioinformatics branch chief. He has more than 230 publications and has received many awards during his career.

Research Interests

Dr. Hong has a wide spectrum of research interests, including chemoinformatics, computational chemistry, next-generation sequencing data analysis, genome-wide association studies, proteomics, and systems biology. His current research goals are to generate safety signatures of drugs for the treatment of COVID-19 patients via big data analytics and artificial intelligence, and to identify candidate polypharmacy drugs for repurposing for COVID-19 treatment through computational chemistry approaches. Additionally, he is looking to construct an androgenic-activity database to enhance the endocrine disruptor knowledge base and to develop a knowledge base for management of opioid agonist and antagonist activities of chemicals. Dr. Hong is also working to develop predictive models using a variety of machine learning and deep learning algorithms to assist in the safety evaluation of FDA-regulated products.

National and International Groups

8th FDA Scientific Computing Days

Middle South Computational Biology & Bioinformatics
Board of Directors, Member
2016 – 2021

Steering Committee, Member
2018 – Present

OpenTox USA 2018 (July 11-12, Durham, NC)
Conference Chair

Select Publications

Assessing Reproducibility of Inherited Variants Detected with Short-Read Whole Genome Sequencing.
Pan B., Ren L., Onuchic V., Guan M., Kusko R., Bruinsma S., Trigg L., Scherer A., Ning B., Zhang C., Glidewell-Kenney C., Xiao C., Donaldson E., Sedlazeck F.J., Schroth G., Yavas G., Grunenwald H., Chen H., Meinholz H., Meehan J., Wang J., Yang J., Foox J., Shang J., Miclaus K., Dong L., Shi L., Mohiyuddin M., Pirooznia M., Gong P., Golshani R., Wolfinger R., Lababidi S., Sahraeian S.M.E., Sherry S., Han T., Chen T., Shi T., Hou W., Ge W., Zou W., Guo W., Bao W., Xiao W., Fan X., Gondo Y., Yu Y., Zhao Y., Su Z., Liu Z., Tong W., Xiao W., Zook J.M., Zheng Y., and Hong H.
Genome Biol. 2022, 23(1):2. doi: 10.1186/s13059-021-02569-8.

Achieving Robust Somatic Mutation Detection with Deep Learning Models Derived from Reference Data Sets of a Cancer Sample.
Sahraeian S.M.E., Fang L.T., Karagiannis K., Moos M., Smith S., Santana-Quintero L., Xiao C., Colgan M., Hong H., Mohiyuddin M., and Xiao W.
Genome Biol. 2022, 23(1):12. doi: 10.1186/s13059-021-02592-9.

Machine Learning Models for Predicting Cytotoxicity of Nanomaterials.
Ji Z., Guo W., Wood E.L., Liu J., Sakkiah S., Xu X., Patterson T.A., and Hong H.
Chem Res Toxicol. 2022, 35(2):125-139. doi: 10.1021/acs.chemrestox.1c00310. Epub 2022 Jan 14.

Machine Learning Models for Predicting Liver Toxicity.
Liu J., Guo W., Sakkiah S., Ji Z., Yavas G., Zou W., Chen M., Tong W., Patterson T.A., and Hong H.
Methods Mol Biol. 2022, 2425:393-415. doi: 10.1007/978-1-0716-1960-5_15.

Machine Learning Models on Chemical Inhibitors of Mitochondrial Electron Transport Chain.
Tang W., Liu W., Wang Z., Hong H., and Chen J.
J Hazard Mater. 2022, 426:128067. doi: 10.1016/j.jhazmat.2021.128067. Epub 2021 Dec 10.

Integrative Approaches for Studying the Role of Noncoding RNAs in Influencing Drug Efficacy and Toxicity.
Li D., Chen M., Hong H., Tong W., and Ning B.
Expert Opin Drug Metab Toxicol. 2022, 1-13. doi: 10.1080/17425255.2022.2054802. Online ahead of print.

Establishing Community Reference Samples, Data and Call Sets for Benchmarking Cancer Mutation Detection Using Whole-Genome Sequencing.
Fang L.T., Zhu B., Zhao Y., Chen W., Yang Z., Kerrigan L., Langenbach K., de Mars M., Lu C., Idler K., Jacob H., Zheng Y., Ren L., Yu Y., Jaeger E., Schroth G.P., Abaan O.D., Talsania K., Lack J., Shen T.W., Chen Z., Stanbouly S., Tran B., Shetty J., Kriga Y., Meerzaman D., Nguyen C., Petitjean V., Sultan M., Cam M., Mehta M., Hung T., Peters E., Kalamegham R., Sahraeian S.M.E., Mohiyuddin M., Guo Y., Yao L., Song L., Lam H.Y.K., Drabek J., Vojta P., Maestro R., Gasparotto D., Kõks S., Reimann E., Scherer A., Nordlund J., Liljedahl U., Jensen R.V., Pirooznia M., Li Z., Xiao C., Sherry S.T., Kusko R., Moos M., Donaldson E., Tezak Z., Ning B., Tong W., Li J., Duerken-Hughes P., Catalanotti C., Maheshwari S., Shuga J., Liang W.S., Keats J., Adkins J., Tassone E., Zismann V., McDaniel T., Trent J., Foox J., Butler D., Mason C.E., Hong H., Shi L., Wang C., Xiao W., and the Somatic Mutation Working Group of Sequencing Quality Control Phase II Consortium. 
Nat Biotechnol. 2021, 39(9):1151-1160. doi: 10.1038/s41587-021-00993-6. Epub 2021 Sep 9. PMID: 34504347.

Toward Best Practice in Cancer Mutation Detection with Whole-Genome and Whole-Exome Sequencing.
Xiao W., Ren L., Chen Z., Fang L.T., Zhao Y., Lack J., Guan M., Zhu B., Jaeger E., Kerrigan L., Blomquist T.M., Hung T., Sultan M., Idler K., Lu C., Scherer A., Kusko R., Moos M., Xiao C., Sherry S.T., Abaan O.D., Chen W., Chen X., Nordlund J., Liljedahl U., Maestro R., Polano M., Drabek J., Vojta P., Kõks S., Reimann E., Madala B.S., Mercer T., Miller C., Jacob H., Truong T., Moshrefi A., Natarajan A., Granat A., Schroth G.P., Kalamegham R., Peters E., Petitjean V., Walton A., Shen T.W., Talsania K., Vera C.J., Langenbach K., de Mars M., Hipp J.A., Willey J.C., Wang J., Shetty J., Kriga Y., Raziuddin A., Tran B., Zheng Y., Yu Y., Cam M., Jailwala P., Nguyen C., Meerzaman D., Chen Q., Yan C., Ernest B., Mehra U., Jensen R.V., Jones W., Li J.L., Papas B.N., Pirooznia M., Chen Y.C., Seifuddin F., Li Z., Liu X., Resch W., Wang J., Wu L., Yavas G., Miles C., Ning B., Tong W., Mason C.E., Donaldson E., Lababidi S., Staudt L.M., Tezak Z., Hong H., Wang C., and Shi L. 
Nat Biotechnol. 2021, 39(9):1141-1150. doi: 10.1038/s41587-021-00994-5. Epub 2021 Sep 9. PMID: 34504346.

Identification of Epidemiological Traits by Analysis of SARS-CoV-2 Sequences.
Pan B., Ji Z., Sakkiah S., Guo W., Liu J., Patterson T.A., and Hong H. 
Viruses. 2021, 13(5):764. doi: 10.3390/v13050764. PMID: 33925388; PMCID: PMC8145049.

Nanomaterial Databases: Data Sources for Promoting Design and Risk Assessment of Nanomaterials.
Ji Z., Guo W., Sakkiah S., Liu J., Patterson T.A., and Hong H. 
Nanomaterials (Basel). 2021, 11(6):1599. doi: 10.3390/nano11061599. PMID: 34207026; PMCID: PMC8234318.

Elucidating Interactions Between SARS-CoV-2 Trimeric Spike Protein and ACE2 Using Homology Modeling and Molecular Dynamics Simulations.
Sakkiah S., Guo W., Pan B., Ji Z., Yavas G., Azevedo M., Hawes J., Patterson T.A., and Hong H. 
Front Chem. 2021, 8:622632. doi: 10.3389/fchem.2020.622632. PMID: 33469527; PMCID: PMC7813797.

Informing Selection of Drugs for COVID-19 Treatment Through Adverse Events Analysis.
Guo W., Pan B., Sakkiah S., Ji Z., Yavas G., Lu Y., Komatsu T.E., Lal-Nag M., Tong W., Patterson T.A., and Hong H. 
Sci Rep. 2021, 11(1):14022. doi: 10.1038/s41598-021-93500-5. PMID: 34234253; PMCID: PMC8263777.

Software-Assisted Pattern Recognition of Persistent Organic Pollutants in Contaminated Human and Animal Food.
Guo W., Archer J., Moore M., Shojaee S., Zou W., Ge W., Benjamin L., Adeuya A., Fairchild R., and Hong H. 
Molecules. 2021, 26(3):685. doi: 10.3390/molecules26030685. PMID: 33525602; PMCID: PMC7865765.

Elucidation of Agonist and Antagonist Dynamic Binding Patterns in ER-α by Integration of Molecular Docking, Molecular Dynamics Simulations and Quantum Mechanical Calculations.
Sakkiah S., Selvaraj C., Guo W., Liu J., Ge W., Patterson T.A., and Hong H. 
Int J Mol Sci. 2021, 22(17):9371. doi: 10.3390/ijms22179371. PMID: 34502280; PMCID: PMC8431471.

Developing QSAR Models with Defined Applicability Domains on PPARγ Binding Affinity Using Large Data Sets and Machine Learning Algorithms.
Wang Z., Chen J., and Hong H. 
Environ Sci Technol. 2021, 55(10):6857-6866. doi: 10.1021/acs.est.0c07040. Epub 2021 Apr 29. PMID: 33914508.

Predictive Models to Identify Small Molecule Activators and Inhibitors of Opioid Receptors.
Sakamuru S., Zhao J., Xia M., Hong H., Simeonov A., Vaisman I., and Huang R. 
J Chem Inf Model. 2021, 61(6):2675-2685. doi: 10.1021/acs.jcim.1c00439. Epub 2021 May 28. PMID: 34047186.

Cross-Oncopanel Study Reveals High Sensitivity and Accuracy with Overall Analytical Performance Depending on Genomic Regions.
Gong B., Li D., Kusko R., Novoradovskaya N., Zhang Y., Wang S., Pabón-Peña C., Zhang Z., Lai K., Cai W., LoCoco J.S., Lader E., Richmond T.A., Mittal V.K., Liu L.C., Johann D.J. Jr., Willey J.C., Bushel P.R., Yu Y., Xu C., Chen G., Burgess D., Cawley S., Giorda K., Haseley N., Qiu F., Wilkins K., Arib H., Attwooll C., Babson K., Bao L., Bao W., Lucas A.B., Best H., Bhandari A., Bisgin H., Blackburn J., Blomquist T.M., Boardman L., Burgher B., Butler D.J., Chang C.J., Chaubey A., Chen T., Chierici M., Chin C.R., Close D., Conroy J., Cooley Coleman J., Craig D.J., Crawford E., Del Pozo A., Deveson I.W., Duncan D., Eterovic A.K., Fan X., Foox J., Furlanello C., Ghosal A., Glenn S., Guan M., Haag C., Hang X., Happe S., Hennigan B., Hipp J., Hong H., Horvath K., Hu J., Hung L.Y., Jarosz M., Kerkhof J., Kipp B., Kreil D.P., Łabaj P., Lapunzina P., Li P., Li Q.Z., Li W., Li Z., Liang Y., Liu S., Liu Z., Ma C., Marella N., Martín-Arenas R., Megherbi D.B., Meng Q., Mieczkowski P.A., Morrison T., Muzny D., Ning B., Parsons B.L., Paweletz C.P., Pirooznia M., Qu W., Raymond A., Rindler P., Ringler R., Sadikovic B., Scherer A., Schulze E., Sebra R., Shaknovich R., Shi Q., Shi T., Silla-Castro J.C., Smith M., López M.S., Song P., Stetson D., Strahl M., Stuart A., Supplee J., Szankasi P., Tan H., Tang L.Y., Tao Y., Thakkar S., Thierry-Mieg D., Thierry-Mieg J., Thodima V.J., Thomas D., Tichý B., Tom N., Garcia E.V., Verma S., Walker K., Wang C., Wang J., Wang Y., Wen Z., Wirta V., Wu L., Xiao C., Xiao W., Xu S., Yang M., Ying J., Yip S.H., Zhang G., Zhang S., Zhao M., Zheng Y., Zhou X., Mason C.E., Mercer T., Tong W., Shi L., Jones W., and Xu J. 
Genome Biol. 2021, 22(1):109. doi: 10.1186/s13059-021-02315-0. PMID: 33863344; PMCID: PMC8051090.

Human Transthyretin Binding Affinity of Halogenated Thiophenols and Halogenated Phenols: An In Vitro and In Silico Study.
Yang X., Ou W., Zhao S., Wang L., Chen J., Kusko R., Hong H., and Liu H. 
Chemosphere. 2021, 280:130627. doi: 10.1016/j.chemosphere.2021.130627. Epub 2021 Apr 30. PMID: 33964751.

Development of a Nicotinic Acetylcholine Receptor nAChR α7 Binding Activity Prediction Model.
Sakkiah S., Leggett C., Pan B., Guo W., Valerio L.G. Jr., and Hong H.
J Chem Inf Model. 2020, 60(4):2396-2404. doi: 10.1021/acs.jcim.0c00139.
Applicability Domains Enhance Application of PPARγ Agonist Classifiers Trained by Drug-like Compounds to Environmental Chemicals.
Wang Z., Chen J., and Hong H.
Chem Res Toxicol. 2020, 15;33(6):1382-1388. doi: 10.1021/acs.chemrestox.9b00498.

Lab Members

Contact information for all lab members:
(870) 543-7121

Minjun Chen, Ph.D.
Staff Fellow

Fan Dong, Ph.D.
Postdoctoral Fellow

Wenjing Guo, Ph.D.
Visiting Scientist

Jie Liu, Ph.D.
Staff Fellow

Wen Zou, Ph.D.
Visiting Scientist

Contact Information
Huixiao Hong
(870) 543-7121
Technology & Discipline
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