Hong Fang Source Confirmed

Affiliation confirmed via AI analysis of OpenAlex, ORCID, and web sources.

High Impact

Researcher

National Center for Toxicological Research

faculty

51 h-index 126 pubs 12,629 cited

Research Areas

Natural Language Processing Techniques Pharmacological Effects and Toxicity Studies Advanced Neural Network Applications Metabolomics and Mass Spectrometry Studies Health and Medical Research Impacts Plant Physiology and Cultivation Studies Computational Drug Discovery Methods

Links

ORCID Google Scholar Lab Website Research Group

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OverviewAI-generated summary

Hong Fang's research focuses on the application of artificial intelligence and advanced computational methods to enhance drug and food safety surveillance and analysis. Fang has published work utilizing large language models, such as ChatGPT and RxBERT, to improve the extraction, annotation, and classification of information from drug labeling documents, particularly concerning adverse events. This work includes the development of novel AI tools like AskFDALabel and LabelComp, designed to identify changes in adverse event reporting and enhance regulatory science perspectives.

Fang's scholarly output also extends to other areas, including the functional characterization of enzymes involved in lutein biosynthesis in microalgae and the analysis of ingredients and metabolites in traditional Chinese medicine. With a high-impact designation, Fang has accumulated 126 publications and over 12,600 citations, supported by collaborations with researchers at the National Center for Toxicological Research, including Leihong Wu and Joshua Xu.

Metrics

  • h-index: 51
  • Publications: 126
  • Citations: 12,629

Selected Publications

  • Leveraging FDA Labeling Documents and Large Language Model to Enhance Annotation, Profiling, and Classification of Drug Adverse Events with AskFDALabel (2025) DOI
  • S02-03 FDALabel: enabling full text searching of drug labeling (2024) DOI
  • Description and Validation of a Novel AI Tool, LabelComp, for the Identification of Adverse Event Changes in FDA Labeling (2024) DOI
  • Text summarization with ChatGPT for drug labeling documents (2024) DOI
  • RxBERT: Enhancing drug labeling text mining and analysis with AI language modeling (2023) DOI
  • Artificial intelligence and real-world data for drug and food safety – A regulatory science perspective (2023) DOI

Collaborators

Researchers in the database who share publications

Leihong Wu National Center for Toxicological Research 4 shared publications Joshua Xu National Center for Toxicological Research 3 shared publications Weida Tong National Center for Toxicological Research 1 shared publication William Slikker National Center for Toxicological Research 1 shared publication Magnus Gray National Center for Toxicological Research 1 shared publication

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