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Current · Arkansas
Last published
2026
Sources
OpenAlex · ORCID
Refreshed
2026-10-05
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Yanyan Qu
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Also affiliated: United States Food and Drug Administration (2026)
9 h-index
24 pubs
230 cited
Research Areas
Links
Biography and Research Information
OverviewAI-generated summary
Yanyan Qu's research focuses on integrating in vitro and in silico methods, specifically New Approach Methodologies (NAMs), to improve the prediction of drug-induced liver injury. This work aims to enhance the accuracy and efficiency of toxicological assessments. Qu has published one study in this area, with a 2026 publication date, indicating recent activity in the field. Qu collaborates with Ting Li at the National Center for Toxicological Research, with one shared publication, suggesting a collaborative research environment.
Metrics
- h-index: 9
- Publications: 24
- Citations: 230
Selected Publications
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Integrating in vitro and in silico NAMs for enhanced prediction of drug-induced liver injury (2026)
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Beyond QSARs: Quantitative Knowledge–Activity Relationships (QKARs) for enhanced drug toxicity prediction (2025)
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DILIrank 2.0: An updated and expanded database for drug-induced liver injury risk based on FDA labeling and a literature review (2025)
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DICTrank Is a Reliable Dataset for Cardiotoxicity Prediction Using Machine Learning Methods (2025)
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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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A framework enabling LLMs into regulatory environment for transparency and trustworthiness and its application to drug labeling document (2024)
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Generation of a drug-induced renal injury list to facilitate the development of new approach methodologies for nephrotoxicity (2024)
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DICTrank: The largest reference list of 1318 human drugs ranked by risk of drug-induced cardiotoxicity using FDA labeling (2023)
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DICTrank: The largest reference list of 1318 human drugs ranked by risk of drug-induced cardiotoxicity using FDA labeling (2023)
Collaboration Network
Top Collaborators
Weida Tong
National Center for Toxicological Research
9 shared publications
In database
- DICTrank: The largest reference list of 1318 human drugs ranked by risk of drug-induced cardiotoxicity using FDA labeling
- Generation of a drug-induced renal injury list to facilitate the development of new approach methodologies for nephrotoxicity
- Leveraging FDA Labeling Documents and Large Language Model to Enhance Annotation, Profiling, and Classification of Drug Adverse Events with AskFDALabel
- A framework enabling LLMs into regulatory environment for transparency and trustworthiness and its application to drug labeling document
- DICTrank Is a Reliable Dataset for Cardiotoxicity Prediction Using Machine Learning Methods
Showing 5 of 9 shared publications
Dongying Li
National Center for Toxicological Research
6 shared publications
In database
- DICTrank: The largest reference list of 1318 human drugs ranked by risk of drug-induced cardiotoxicity using FDA labeling
- A framework enabling LLMs into regulatory environment for transparency and trustworthiness and its application to drug labeling document
- DICTrank Is a Reliable Dataset for Cardiotoxicity Prediction Using Machine Learning Methods
- DILIrank 2.0: An updated and expanded database for drug-induced liver injury risk based on FDA labeling and a literature review
- Beyond QSARs: Quantitative Knowledge–Activity Relationships (QKARs) for enhanced drug toxicity prediction
Showing 5 of 6 shared publications
Ting Li
National Center for Toxicological Research
4 shared publications
In database
- DICTrank: The largest reference list of 1318 human drugs ranked by risk of drug-induced cardiotoxicity using FDA labeling
- Generation of a drug-induced renal injury list to facilitate the development of new approach methodologies for nephrotoxicity
- DICTrank Is a Reliable Dataset for Cardiotoxicity Prediction Using Machine Learning Methods
- Beyond QSARs: Quantitative Knowledge–Activity Relationships (QKARs) for enhanced drug toxicity prediction
Zhichao Liu
BioClinica (United States) (US)
2 shared publications
- DICTrank: The largest reference list of 1318 human drugs ranked by risk of drug-induced cardiotoxicity using FDA labeling
- DICTrank Is a Reliable Dataset for Cardiotoxicity Prediction Using Machine Learning Methods
Skylar Connor
National Center for Toxicological Research
2 shared publications
In database
- Generation of a drug-induced renal injury list to facilitate the development of new approach methodologies for nephrotoxicity
- DILIrank 2.0: An updated and expanded database for drug-induced liver injury risk based on FDA labeling and a literature review
Leihong Wu
National Center for Toxicological Research
2 shared publications
In database
- Leveraging FDA Labeling Documents and Large Language Model to Enhance Annotation, Profiling, and Classification of Drug Adverse Events with AskFDALabel
- A framework enabling LLMs into regulatory environment for transparency and trustworthiness and its application to drug labeling document
Joshua Xu
National Center for Toxicological Research
2 shared publications
In database
- Leveraging FDA Labeling Documents and Large Language Model to Enhance Annotation, Profiling, and Classification of Drug Adverse Events with AskFDALabel
- A framework enabling LLMs into regulatory environment for transparency and trustworthiness and its application to drug labeling document
Shraddha H. Thakkar
United States Food and Drug Administration (US)
2 shared publications
- A framework enabling LLMs into regulatory environment for transparency and trustworthiness and its application to drug labeling document
- Beyond QSARs: Quantitative Knowledge–Activity Relationships (QKARs) for enhanced drug toxicity prediction
Ting Li
National Center for Toxicological Research
1 shared publication
In database
- DICTrank: The largest reference list of 1318 human drugs ranked by risk of drug-induced cardiotoxicity using FDA labeling
Zhichao Liu
National Center for Toxicological Research (US)
1 shared publication
- DICTrank: The largest reference list of 1318 human drugs ranked by risk of drug-induced cardiotoxicity using FDA labeling
Ruth Angela Roberts
University of Birmingham (GB)
1 shared publication
- Generation of a drug-induced renal injury list to facilitate the development of new approach methodologies for nephrotoxicity
Magnus Gray
National Center for Toxicological Research
1 shared publication
In database
- A framework enabling LLMs into regulatory environment for transparency and trustworthiness and its application to drug labeling document
Hong Fang
National Center for Toxicological Research
1 shared publication
In database
- Leveraging FDA Labeling Documents and Large Language Model to Enhance Annotation, Profiling, and Classification of Drug Adverse Events with AskFDALabel
AyoOluwa O. Olubamiwa
National Center for Toxicological Research
1 shared publication
In database
- DILIrank 2.0: An updated and expanded database for drug-induced liver injury risk based on FDA labeling and a literature review
Minjun Chen
National Center for Toxicological Research
1 shared publication
In database
- DILIrank 2.0: An updated and expanded database for drug-induced liver injury risk based on FDA labeling and a literature review
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