Hong Fang
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Researcher
Also affiliated: Zhejiang A & F University (2025); Nanjing University of Chinese Medicine (2025); United States Food and Drug Administration (2001–2020); University of Health Sciences and Pharmacy (2025); Georgetown University (2017); Chinese Academy of Medical Sciences & Peking Union Medical College (2025); Washington University in St. Louis (2025); Agriculture and Forestry University (2025); Northrop Grumman (United States) (2003–2004); La Cité Collégiale (2012); Saint Louis College La Union (2025); National Cancer Center (2025); City College (2012); Nanjing Drum Tower Hospital (2025); Tianjin People's Hospital (2005); Hi-Z Technology (United States) (2005–2007); Food and Drug Administration (2010–2014); Center for Clinical Research (United States) (2025); Hangzhou Dianzi University (2025); ICF International (United States) (2007–2017); Procter & Gamble (United States) (2003); Fuzhou University (2023)
Faculty Researcher
Research Areas
Biomedical Subjects
Biography and Research Information
OverviewAI-generated summary
Hong Fang's research focuses on the application of artificial intelligence and advanced computational techniques to enhance drug and food safety, with a particular emphasis on regulatory science. Fang has developed and validated novel AI tools, such as RxBERT and LabelComp, designed to improve the analysis, annotation, and classification of drug labeling documents, especially concerning adverse events. These tools leverage large language models and real-world data to extract and interpret complex information from sources like FDA labeling documents, aiming to streamline regulatory processes and improve public health outcomes.
Fang's work also extends to areas such as text summarization for drug labeling and the functional characterization of specific enzymes, as seen in research on lycopene cyclases from microalgae. Collaborations within the National Center for Toxicological Research, including with Leihong Wu, Joshua Xu, and Weida Tong, support these research endeavors. Fang's scholarship is recognized by a high h-index of 51 and over 12,000 citations, indicating a significant impact in the field.
Metrics
- h-index: 50
- Publications: 122
- Citations: 11,840
Selected Publications
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Leveraging FDA Labeling Documents and Large Language Model to Enhance Annotation, Profiling, and Classification of Drug Adverse Events with AskFDALabel (2025)
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S02-03 FDALabel: enabling full text searching of drug labeling (2024)
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Description and Validation of a Novel AI Tool, LabelComp, for the Identification of Adverse Event Changes in FDA Labeling (2024)
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Text summarization with ChatGPT for drug labeling documents (2024)
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RxBERT: Enhancing drug labeling text mining and analysis with AI language modeling (2023)
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Artificial intelligence and real-world data for drug and food safety – A regulatory science perspective (2023)
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FDALabel for drug repurposing studies and beyond (2020)
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Study of pharmacogenomic information in FDA-approved drug labeling to facilitate application of precision medicine (2020)
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Study of serious adverse drug reactions using FDA-approved drug labeling and MedDRA (2019)
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Drug-Induced Liver Injury (DILI) Classification and Its Application on Human DILI Risk Prediction (2018)
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Multiple microRNAs function as self-protective modules in acetaminophen-induced hepatotoxicity in humans (2017)
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The Liver Toxicity Knowledge Base (LKTB) and drug-induced liver injury (DILI) classification for assessment of human liver injury (2017)
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Integrating Drug’s Mode of Action into Quantitative Structure–Activity Relationships for Improved Prediction of Drug-Induced Liver Injury (2017)
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Potential Reuse of Oncology Drugs in the Treatment of Rare Diseases (2016)
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FDA drug labeling: rich resources to facilitate precision medicine, drug safety, and regulatory science (2016)
Collaboration Network
Top Collaborators
- Text summarization with ChatGPT for drug labeling documents
- 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
- 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
- Text summarization with ChatGPT for drug labeling documents
- 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
- Artificial intelligence and real-world data for drug and food safety – A regulatory science perspective
- Text summarization with ChatGPT for drug labeling documents
- 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
- Artificial intelligence and real-world data for drug and food safety – A regulatory science perspective
- Artificial intelligence and real-world data for drug and food safety – A regulatory science perspective
- Artificial intelligence and real-world data for drug and food safety – A regulatory science perspective
- Artificial intelligence and real-world data for drug and food safety – A regulatory science perspective
- Artificial intelligence and real-world data for drug and food safety – A regulatory science perspective
- Artificial intelligence and real-world data for drug and food safety – A regulatory science perspective
- Artificial intelligence and real-world data for drug and food safety – A regulatory science perspective
- Artificial intelligence and real-world data for drug and food safety – A regulatory science perspective
- Artificial intelligence and real-world data for drug and food safety – A regulatory science perspective
- Artificial intelligence and real-world data for drug and food safety – A regulatory science perspective
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