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  1. Science & Research (NCTR)

Weida Tong Ph.D.
Leadership Role
Director, Division of Bioinformatics and Biostatistics - National Center for Toxicological Research

Director, Division of Bioinformatics and Biostatistics

Dr. Weida Tong

Weida Tong, Ph.D.
(870) 543-7121
NCTRResearch@fda.hhs.gov  

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 About  |  Publications

Background

Dr. Weida Tong is the director of the Division of Bioinformatics and Biostatistics at FDA’s National Center for Toxicological Research (NCTR). He has held the position of FDA Senior Biomedical Research and Biomedical Product Assessment Service (SBRBPAS) Expert, formerly known as Senior Biomedical Research Service, since 2011. Additionally, he has served as the chair of the Global Coalition for Regulatory Science Research (GCRSR)— an international coalition comprising approximately 20 regulatory agencies worldwide from over ten countries—since 2022. Dr. Tong holds the roles of founder and Chair Emeritus of the International MAQC (Massive Analysis and Quality Control) Society. He also lends his expertise to Science Advisory Boards for numerous multi-institutional projects in both Europe and the U.S. Furthermore, he maintains adjunct appointments at several universities. Dr. Tong's contributions to the field are evidenced by his publication of over 300 peer-reviewed papers and book chapters.

 

Research Interests

Dr. Tong's primary research focus revolves around the application of bioinformatics, Artificial Intelligence (AI), molecular modeling, and data analytics for various purposes including biomarker discovery, drug safety and repurposing, pharmacogenomics/toxicogenomics, and precision medicine. At present, he oversees several FDA mission-critical projects within his division, including:

  1. Developing AI for the Toxicology Program (AI4TOX), which encompasses four initiatives (AnimalGAN, SafetAI, BERTox, and PathologAI). This program leverages advanced AI and machine learning techniques to advance toxicological research and drug safety.
  2. Developing the Liver Toxicity Knowledge Base to address concerns regarding drug safety—particularly related to drug-induced liver injury (DILI).
  3. Designing and developing computer-based technologies to support FDA's efforts in bioinformatics and scientific computing.
  4. Supervising and leading the FDA-led community-wide MicroArray Quality Control and SEquencing Quality Control (MAQC/SEQC) consortium, which aims to analyze the technical performance and practical utility of emerging genomics technologies with a specific emphasis on regulatory application and precision medicine.


Professional Societies/National and International Groups

American Association of Pharmaceutical Scientists
Member
2018 – Present 

Global Coalition for Regulatory Science Research
Executive Committee Member
2013 – Present

Bioinformatics Working Group Chair
2018 – Present

Chair
2022 – Present

Intelligent Systems for Molecular Biology/Critical Assessment of Massive Data Analysis Society
Scientific Committee Member and Advisor 
2009 – Present

Massive Analysis and Quality Control Society
Member
2017 – Present 

Board Chairperson 
2017 – 2020

Chair Emeritus
2020 – Present

MidSouth Computational Biology & Bioinformatics Society
Member
2002 – Present 

President-Elect
2018

President
2019 – 2020

Past-President
2021 

Society of Toxicology
Member
2008 – Present

Board of Publication Chair
2019 – 2020

 

Select Publications

A Framework Enabling LLMs into Regulatory Environment for Transparency and Trustworthiness and Its Application to Drug Labeling Document.
Wu L., Xu J., Thakkar S., Gray M., Qu Y., Li D., and Tong W.
Regulatory Toxicology and Pharmacology. 2024, 149:105613.

Generation of a Drug-Induced Renal Injury List to Facilitate the Development of New Approach Methodologies for Nephrotoxicity.
Connor S., Li T., Qu Y., Roberts R.A., and Tong W.
Drug Discovery Today. 2024, 29(4):103938.

A Generative Adversarial Network Model Alternative to Animal Studies for Clinical Pathology Assessment.
Chen X., Liu Z., Roberts R., and Tong W.
Nature Communications. 2023, 14(1):7141.

Measurement and Mitigation of Bias in Artificial Intelligence: A Narrative Literature Review for Regulatory Science. 
Gray M., Samala R., Liu Q., Skiles D., Xu J., Tong W., and Wu L. 
Clinical Pharmacology and Therapeutics. 2023, 115(4): 687-697.

DICTrank: The Largest Reference List of 1318 Human Drugs Ranked by Risk of Drug-Induced Cardiotoxicity Using FDA Labeling.
Qu Y., Li T., Liu Z., Li D., and Tong W.
Drug Discovery Today. 2023, 28(11):103770.

RxBERT: Enhancing Drug Labeling Text Mining and Analysis with AI Language Modeling. 
Wu L., Gray M., Dang O., Xu J., Fang H., and Tong W. 
Experimental Biology and Medicine. 2023, 248(21):1937-1943.

A Weakly Supervised Deep Learning Framework for Whole Slide Classification to Facilitate Digital Pathology in Animal Study. 
Bussola N., Xu J., Wu L., Gorini L., Zhang Y., Furlanello C., and Tong W.
Chemical Research in Toxicology. 2023, 36(8):1321-1331.

Bidirectional Encoder Representations from Transformers-like Large Language Models in Patient Safety and Pharmacovigilance: A Comprehensive Assessment of Causal Inference Implications. 
Wang X., Xu X., Liu Z., and Tong W. 
Experimental Biology and Medicine. 2023, 248(21):1908-1917.

TransOrGAN: An Artificial Intelligence Mapping of Rat Transcriptomic Profiles between Organs, Ages, and Sexes.
Li T., Roberts R., Liu Z., and Tong W. 
Chemical Research in Toxicology. 2023, 36(6):916-925.

DeepAmes: A Deep Learning-Powered Ames Test Predictive Model with Potential for Regulatory Application. 
Li T., Liu Z., Thakkar S., Roberts R., and Tong W. 
Regulatory Toxicology and Pharmacology. 2023, 144:105486.

Tox-GAN: An Artificial Intelligence Approach Alternative to Animal Studies—A Case Study with Toxicogenomics.
Chen X., Roberts R., Tong W. et al.
Toxicological Sciences. 2022, 186:242-259.

NeuroCORD: A Language Model to Facilitate COVID-19-Associated Neurological Disorder Studies.
Wu L., Ali S., Ali H., Brock T., Xu J., and Tong W.
International Journal of Environmental Research and Public Health. 2022, 19:9974.
 
DeepCausality: A General AI-Powered Causal Inference Framework for Free Text: A Case Study of LiverTox. 
Wang X., Xu X., Tong W., Liu Q., and Liu Z.
Frontiers in Artificial Intelligence. 2022, 5:999289.
 
AI-Based Language Models Powering Drug Discovery and Development. 
Liu Z., Roberts R.A., Lal-Nag M., et al.
Drug Discovery Today. 2021, 26:2593-2607.
 
BERT-Based Natural Language Processing of Drug Labeling Documents: A Case Study for Classifying Drug-Induced Liver Injury Risk.
Wu Y., Liu Z., Wu L., et al.
Frontiers in Artificial Intelligence. 2021, 4:729834-729834.
 
DICE: A Drug Indication Classification and Encyclopedia for AI-Based Indication Extraction.
Bhatt A., Roberts R., Chen X., et al.
Frontiers in Artificial Intelligence. 2021, 4:711467.
 
InferBERT: A Transformer-Based Causal Inference Framework for Enhancing Pharmacovigilance.
Wang X., Xu X., Tong W., et al.
Frontiers in Artificial Intelligence. 2021, 4:659622-659622.

DeepDILI: Deep Learning-Powered Drug-Induced Liver Injury Prediction Using Model-Level Representation.
Li T., Tong W., Roberts R., et al.
Chemical Research in Toxicology. 2021, 34:550-565.

DeepCarc: Deep Learning-Powered Carcinogenicity Prediction Using Model-Level Representation.
Li T., Tong W., Roberts R., et al.
Frontiers in Artificial Intelligence. 2021, 4:757780.

Deep Learning on High-Throughput Transcriptomics to Predict Drug-Induced Liver Injury.
Li T., Tong W., Roberts R., et al. 
Frontiers in Bioengineering and Biotechnology. 2020, 8:562677.

Contact Information
Weida Tong
(870) 543-7121
Expertise
Expertise
Approach
Domain
Technology & Discipline
Toxicology
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