H
Hong Fang Data-verified
Affiliation confirmed via AI analysis of OpenAlex, ORCID, and web sources.
High Impact
Researcher
faculty
51 h-index
123 pubs
12,785 cited
Research Areas
Biography and Research Information
OverviewAI-generated summary
Hong Fang's research focuses on the application of artificial intelligence and large language models to enhance drug and food safety, particularly in regulatory science. Fang has investigated the use of AI for analyzing and summarizing drug labeling documents, developing tools like RxBERT for text mining and AskFDALabel for annotating and classifying drug adverse events. Recent work includes the description and validation of LabelComp, a novel AI tool for identifying adverse event changes in FDA labeling, and exploring AI and real-world data for regulatory science perspectives.
Fang's work also extends to the functional characterization of enzymes involved in lutein production in microalgae and the structural pharmacology of estrogen-related receptors. Earlier research involved the analysis of ingredients and metabolites of Gegen Decoction using UHPLC-Q-TOF-MS. Fang has a h-index of 51 with over 12,754 citations across 126 publications, and is recognized as a highly cited researcher. Collaborations include work with Leihong Wu, Joshua Xu, and Weida Tong, all from the National Center for Toxicological Research.
Metrics
- h-index: 51
- Publications: 123
- Citations: 12,785
Selected Publications
-
Leveraging FDA Labeling Documents and Large Language Model to Enhance Annotation, Profiling, and Classification of Drug Adverse Events with AskFDALabel (2025)
-
S02-03 FDALabel: enabling full text searching of drug labeling (2024)
-
Description and Validation of a Novel AI Tool, LabelComp, for the Identification of Adverse Event Changes in FDA Labeling (2024)
-
Text summarization with ChatGPT for drug labeling documents (2024)
-
RxBERT: Enhancing drug labeling text mining and analysis with AI language modeling (2023)
-
Artificial intelligence and real-world data for drug and food safety – A regulatory science perspective (2023)
Collaboration Network
Top Collaborators
Leihong Wu
National Center for Toxicological Research
4 shared publications
In database
- RxBERT: Enhancing drug labeling text mining and analysis with AI language modeling
- Text summarization with ChatGPT for drug labeling documents
- Leveraging FDA Labeling Documents and Large Language Model to Enhance Annotation, Profiling, and Classification of Drug Adverse Events with AskFDALabel
- Description and Validation of a Novel AI Tool, LabelComp, for the Identification of Adverse Event Changes in FDA Labeling
Joshua Xu
National Center for Toxicological Research
3 shared publications
In database
- RxBERT: Enhancing drug labeling text mining and analysis with AI language modeling
- Leveraging FDA Labeling Documents and Large Language Model to Enhance Annotation, Profiling, and Classification of Drug Adverse Events with AskFDALabel
- Description and Validation of a Novel AI Tool, LabelComp, for the Identification of Adverse Event Changes in FDA Labeling
Weida Tong
National Center for Toxicological Research (US)
3 shared publications
- RxBERT: Enhancing drug labeling text mining and analysis with AI language modeling
- Text summarization with ChatGPT for drug labeling documents
- Leveraging FDA Labeling Documents and Large Language Model to Enhance Annotation, Profiling, and Classification of Drug Adverse Events with AskFDALabel
Zhichao Liu
Boehringer Ingelheim (United States) (US)
2 shared publications
- Artificial intelligence and real-world data for drug and food safety – A regulatory science perspective
- Text summarization with ChatGPT for drug labeling documents
Oanh Dang
Center for Drug Evaluation and Research (US)
2 shared publications
- RxBERT: Enhancing drug labeling text mining and analysis with AI language modeling
- Description and Validation of a Novel AI Tool, LabelComp, for the Identification of Adverse Event Changes in FDA Labeling
Yan Yan
China Pharmaceutical University (CN)
1 shared publication
- [Analysis on ingredients and metabolites of Gegen Decoction absorbed into blood based on UHPLC-Q-TOF-MS].
Lei Zhao
China Pharmaceutical University (CN)
1 shared publication
- [Analysis on ingredients and metabolites of Gegen Decoction absorbed into blood based on UHPLC-Q-TOF-MS].
Boyang Yu
China Pharmaceutical University (CN)
1 shared publication
- [Analysis on ingredients and metabolites of Gegen Decoction absorbed into blood based on UHPLC-Q-TOF-MS].
Chengzhi Chai
China Pharmaceutical University (CN)
1 shared publication
- [Analysis on ingredients and metabolites of Gegen Decoction absorbed into blood based on UHPLC-Q-TOF-MS].
Shraddha Thakkar
Center for Drug Evaluation and Research (US)
1 shared publication
- Artificial intelligence and real-world data for drug and food safety – A regulatory science perspective
William Slikker
National Center for Toxicological Research
1 shared publication
In database
- Artificial intelligence and real-world data for drug and food safety – A regulatory science perspective
Frank Yiannas
United States Food and Drug Administration (US)
1 shared publication
- Artificial intelligence and real-world data for drug and food safety – A regulatory science perspective
Primal Silva
Canadian Food Inspection Agency (CA)
1 shared publication
- Artificial intelligence and real-world data for drug and food safety – A regulatory science perspective
Burton W. Blais
Canadian Food Inspection Agency (CA)
1 shared publication
- Artificial intelligence and real-world data for drug and food safety – A regulatory science perspective
Kern Rei Chng
1 shared publication
- Artificial intelligence and real-world data for drug and food safety – A regulatory science perspective
Similar Researchers
Based on overlapping research topics
Joseph VanScoy
University of Arkansas for Medical Sciences
Natural Language Processing Techniques
Advanced Neural Network Applications
Health Literacy and Information Accessibility
Jake Erwin
University of Arkansas for Medical Sciences
Natural Language Processing Techniques
Advanced Neural Network Applications
Health Literacy and Information Accessibility
Xingqiao Wang
University of Arkansas at Little Rock
Natural Language Processing Techniques
Advanced Neural Network Applications
Pharmacological Effects and Toxicity Studies
Magnus Gray
National Center for Toxicological Research
Natural Language Processing Techniques
Advanced Neural Network Applications
Pharmacological Effects and Toxicity Studies
Susan Gauch
University of Arkansas at Fayetteville
Natural Language Processing Techniques
Advanced Neural Network Applications
Health Literacy and Information Accessibility
Leihong Wu
National Center for Toxicological Research
Natural Language Processing Techniques
Advanced Neural Network Applications
Pharmacological Effects and Toxicity Studies