Weida Tong, Ph.D.
Dr. Weida Tong received his Ph.D. in polymer chemistry from Fudan University (Shanghai, China) in 1989 and completed his postdoctoral fellowship in computational chemistry at the University of Missouri-St. Louis in 1996. He then joined NCTR to develop a toxicological knowledge base for safety evaluation. In 2002, Dr. Tong became Director of a newly formed Center of Excellence for Bioinformatics (formerly Toxicoinformatics). In 2012, he was named the Director of the Division of Bioinformatics and Biostatistics. Dr. Tong is also a member of the Senior Biomedical Research Service (SBRS) at FDA. He has served on the science advisory boards for several multi-institutional projects in Europe and the US. He is the founder and board chairperson of international MAQC Society (through 2021), President of MCBIOS Society from 2018-2020, and Chair of SOT Board of Publication from 2019-2020. He also holds several adjunct positions at universities, including an associate professorship at Rutgers University in Princeton, New Jersey and a full professorship at the University of Arkansas at Little Rock. Dr. Tong has published over 300 peer-reviewed papers and book chapters, and has routinely been invited to present at national and international conferences.
Dr. Tong’s primary research interest is to apply bioinformatics and chemoinformatics, Artificial Intelligence (AI) and Machine Learning, and data analytics for biomarker discovery, drug safety and repurposing, pharmacogenomics/toxicogenomics, and precision medicine. Currently, he directs several FDA mission-critical projects in his division:
- Supervising and leading the FDA-led, community-ide MicroArray and Sequencing Quality Control (MAQC/SEQC) consortium to analyze technical performance and practical utility of emerging genomics technologies with emphasis on regulatory application and precision medicine.
- Development of Liver Toxicity Knowledge Base (LTKB) to address the drug safety concerns related to drug-induced liver injury (DILI).
- Designing and developing computer-based technology to support the FDA’s effort on bioinformatics and scientific computing (e.g., development of the FDA genomic tool, ArrayTrack, to support the pharmacogenomics data review in FDA).
- Developing machine learning and AI for digital health and drug repositioning.
- Conducting molecular modeling and QSARs on various toxicological endpoints such as endocrine disruptor and carcinogenicity.
Professional Societies/National and International Groups
American Association of Pharmaceutical Scientists (AAPS)
2019 – Present
MidSouth Computational Biology and Bioinformatics Society
2002 – Present
Society of Toxicology
2008 – Present
Publication titles are linked to text abstracts on PubMed.
Can Transcriptomic Profiles from Cancer Cell Lines be Used for Toxicity Assessment?
Liu Z., Zhu L., Thakkar S., Roberts R., and Tong W.
Chemical Research in Toxicology. 2020, 33(1):271-280.
Drug-Induced Liver Injury Severity and Toxicity (DILIst): Binary Classification of 1278 Drugs by Human Hepatotoxicity.
Thakkar S., Li T., Liu Z., Wu L., Roberts R., and Tong W.
Drug Discov Today. 2020, 25(1), 201-208.
Toward Clinical Implementation of Next-Generation Sequencing-Based Genetic Testing in Rare Diseases: Where Are We?
Liu Z., Zhu L., Roberts R., and Tong W.
Trends in Genetics. 2019, 35 (11), e1-e2, 783-882.
Toxicogenomics: A 2020 Vision.
Liu Z., Huang R., Roberts R., and Tong W.
Trends in Pharmacological Sciences. 2019, 40(2):92-103.
Liver Toxicity Knowledge Base (LKTB) and Drug-Induced Liver Injury (DILI) Classification for Assessment of Human Liver Injury.
Thakkar S., Chen M., Fang H., Liu Z., Roberts R., and Tong W.
The Expert Review of Gastroenterology & Hepatology. 2018, (12)1:31-38.
Lessons Learned From Two Decades of Anticancer Drugs.
Liu Z., Delavan B., Roberts R., and Tong W.
Trends in Pharmacological Sciences. 2017, 38, 10, 852-872.
Development of Decision Forest Models for Prediction of Drug-Induced Liver Injury in Humans Using a Large Set of FDA-approved Drugs.
Hong H. Thakkar S., Chen M., and Tong W.
Scientific Reports. 2017, 7, 1, 17311.
The International MAQC Society Launches to Enhance Reproducibility of High-Throughput Technologies.
Shi L., Kusko R., Wolfinger R.D., Haibe-Kains B., Fischer M., Sansone S.A., Mason C.E., Furlanello C., Jones W.D., Ning B., and Tong W.
Nature Biotechnology. 2017, 35, 12, 1127.
FDA Drug Labeling: Rich Resources to Facilitate Precision Medicine, Drug Safety, and Regulatory Science.
Fang H., Harris S.C., Liu Z., Zhou G.; Zhang G., Xu J., Rosario L., Howard P., and Tong W.
Drug Discov Today. 2016, 21(10), 1566.
A Model to Predict Severity of Drug-Induced Liver Injury in Humans.
Chen M., Borlak J., and Tong W.
Hepatology. 2016, 64(3):931-40.
DILIrank: The Largest Reference Drug List Ranked by the Risk for Developing Drug-Induced Liver Injury in Humans.
Chen M., Suzuki A., Thakkar S., Yu K., Hu C., and Tong W.
Drug Discov Today. 2016, 21(4):648-53. doi: 10.1016/j.drudis.2016.02.015. Epub 2016 Mar 3.
A Testing Strategy to Predict Risk for Drug-Induced Liver Injury in Humans Using High-Content Screen Assays and the 'Rule-of-Two' Model.
Chen M., Tung C.W., Shi Q., Guo L., Shi L., Fang H., Borlak J., and Tong W.
Arch Toxicol. 2014, 88(7):1439-49.
Quantitative Structure-Activity Relationship Models for Predicting Drug-Induced Liver Injury Based on FDA-Approved Drug Labeling Annotation and Using a Large Collection of Drugs.
Chen M., Hong H., Fang H., Kelly R., Zhou G., Borlak J., and Tong W.
Toxicol Sci. 2013, 136(1):242-9.
High Lipophilicity and High Daily Dose of Oral Medications are Associated with Significant Risk for Drug-Induced Liver Injury.
Chen M., Borlak J., and Tong W.
Hepatology. 2013, 58(1):388-96.
The Liver Toxicity Knowledge Base: A Systems Approach to a Complex End Point.
Chen M., Zhang J., Wang Y., Liu Z., Kelly R., Zhou G., Fang H., Borlak J., and Tong W.
Clin Pharmacol Ther. 2013, 93(5):409-12.
A Decade of Toxicogenomic Research and its Contribution to Toxicological Science.
Chen M., Zhang M., Borlak J., and Tong W.
Toxicol Sci. 2012, 130(2):217-28.
FDA-Approved Drug Labeling for the Study of Drug-Induced Liver Injury.
Chen M., Vijay V., Shi Q., Liu Z., Fang H., and Tong W.
Drug Discov Today. 2011, 16(15-16):697-703.
Contact information for all lab members:
Minjun Chen, Ph.D.
Weigong Ge, M.S.
Binsheng Gong, Ph.D.
Huixiao Hong, Ph.D.
Baitang Ning, Ph.D.
Dong Wang, Ph.D.
Senior Staff Fellow
Joshua Xu, Ph.D.
Gokhan Yavas, Ph.D.
Wei (Vivian) Zhuang, Ph.D.
Wen Zou, Ph.D.
- Contact Information
- Weida Tong
- (870) 543-7121
ExpertiseApproachDomainTechnology & DisciplineToxicology