F
Fan Dong Data-verified
Affiliation confirmed via AI analysis of OpenAlex, ORCID, and web sources.
Senior Engineer
faculty
13 h-index
71 pubs
632 cited
Research Areas
Links
Biography and Research Information
OverviewAI-generated summary
Fan Dong's research centers on the application of machine learning and deep learning techniques to address challenges in toxicology and medical imaging. Dong has investigated the use of these computational approaches for predicting gas adsorption capacity in nanomaterials and for enhancing the segmentation of brain tumor MRI images. Further work has explored machine learning models for predicting rat multigeneration reproductive toxicity and for forecasting the potential for hERG channel blockade, a critical factor in drug safety assessments. Dong has also developed BERT-based language models to extract drug adverse events from social media data, contributing to pharmacovigilance practices. Collaborations at the National Center for Toxicological Research include extensive work with Tucker A. Patterson, Zoe Li, and Wenjing Guo.
Metrics
- h-index: 13
- Publications: 71
- Citations: 632
Selected Publications
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Pharmacovigilance in the digital age: gaining insight from social media data (2025)
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A refined set of RxNorm drug names for enhancing unstructured data analysis in drug safety surveillance (2025)
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Developing predictive models for µ opioid receptor binding using machine learning and deep learning techniques (2025)
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Analysis of Structures of SARS-CoV-2 Papain-like Protease Bound with Ligands Unveils Structural Features for Inhibiting the Enzyme (2025)
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Computational Toxicology (2024)
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Development of a comprehensive open access “molecules with androgenic activity resource (MAAR)” to facilitate risk assessment of chemicals (2024)
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Unlocking the potential of AI: Machine learning and deep learning models for predicting carcinogenicity of chemicals (2024)
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Machine learning and deep learning approaches for enhanced prediction of hERG blockade: a comprehensive QSAR modeling study (2024)
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Decoding the κ Opioid Receptor (KOR): Advancements in Structural Understanding and Implications for Opioid Analgesic Development (2024)
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BERT-based language model for accurate drug adverse event extraction from social media: implementation, evaluation, and contributions to pharmacovigilance practices (2024)
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Machine learning and deep learning for brain tumor MRI image segmentation (2023)
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Review of machine learning and deep learning models for toxicity prediction (2023)
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List of contributors (2023)
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QSAR models for predicting in vivo reproductive toxicity (2023)
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EADB—A database providing curated data for developing QSAR models to facilitate the assessment of endocrine activity (2023)
Collaboration Network
Top Collaborators
Huixiao Hong
United States Food and Drug Administration
20 shared publications
In database
- Review of machine learning and deep learning models for toxicity prediction
- Deep Learning Models for Predicting Gas Adsorption Capacity of Nanomaterials
- Machine learning and deep learning for brain tumor MRI image segmentation
- Machine learning models for rat multigeneration reproductive toxicity prediction
- Machine learning and deep learning approaches for enhanced prediction of hERG blockade: a comprehensive QSAR modeling study
Showing 5 of 20 shared publications
Jie Liu
United States Food and Drug Administration
17 shared publications
In database
- Review of machine learning and deep learning models for toxicity prediction
- Deep Learning Models for Predicting Gas Adsorption Capacity of Nanomaterials
- Machine learning and deep learning for brain tumor MRI image segmentation
- Machine learning models for rat multigeneration reproductive toxicity prediction
- BERT-based language model for accurate drug adverse event extraction from social media: implementation, evaluation, and contributions to pharmacovigilance practices
Showing 5 of 17 shared publications
Wenjing Guo
National Center for Toxicological Research
17 shared publications
In database
- Review of machine learning and deep learning models for toxicity prediction
- Deep Learning Models for Predicting Gas Adsorption Capacity of Nanomaterials
- Machine learning and deep learning for brain tumor MRI image segmentation
- Machine learning models for rat multigeneration reproductive toxicity prediction
- Machine learning and deep learning approaches for enhanced prediction of hERG blockade: a comprehensive QSAR modeling study
Showing 5 of 17 shared publications
Tucker A. Patterson
National Center for Toxicological Research
17 shared publications
In database
- Review of machine learning and deep learning models for toxicity prediction
- Machine learning and deep learning for brain tumor MRI image segmentation
- Machine learning models for rat multigeneration reproductive toxicity prediction
- Machine learning and deep learning approaches for enhanced prediction of hERG blockade: a comprehensive QSAR modeling study
- BERT-based language model for accurate drug adverse event extraction from social media: implementation, evaluation, and contributions to pharmacovigilance practices
Showing 5 of 17 shared publications
Zoe Li
United States Food and Drug Administration
9 shared publications
In database
- Review of machine learning and deep learning models for toxicity prediction
- Machine learning and deep learning for brain tumor MRI image segmentation
- Three-Dimensional Structural Insights Have Revealed the Distinct Binding Interactions of Agonists, Partial Agonists, and Antagonists with the µ Opioid Receptor
- QSAR models for predicting in vivo reproductive toxicity
- Decoding the κ Opioid Receptor (KOR): Advancements in Structural Understanding and Implications for Opioid Analgesic Development
Showing 5 of 9 shared publications
Tom Gee
3 shared publications
- Resting state fMRI scanner instabilities revealed by longitudinal phantom scans in a multi-center study
- FAIR in action: Brain-CODE - A neuroscience data sharing platform to accelerate brain research
- Pilot: Making research data FAIR
Weigong Ge
National Center for Toxicological Research
3 shared publications
In database
- Deep Learning Models for Predicting Gas Adsorption Capacity of Nanomaterials
- Machine learning and deep learning approaches for enhanced prediction of hERG blockade: a comprehensive QSAR modeling study
- Developing predictive models for µ opioid receptor binding using machine learning and deep learning techniques
Weida Tong
National Center for Toxicological Research (US)
3 shared publications
- Decision forest—a machine learning algorithm for QSAR modeling
- Development of a comprehensive open access “molecules with androgenic activity resource (MAAR)” to facilitate risk assessment of chemicals
- Computational Toxicology
Yiqiang Chen
Institute of Computing Technology (CN)
3 shared publications
- FewShotBP
- EyeGraphGPT: Knowledge Graph Enhanced Multimodal Large Language Model for Ophthalmic Report Generation
- DDIR: Domain-Disentangled Invariant Representation learning for tailored predictions
Md Kamrul Hasan Khan
National Center for Toxicological Research (US)
3 shared publications
- Review of machine learning and deep learning models for toxicity prediction
- Machine learning and deep learning for brain tumor MRI image segmentation
- Machine learning and deep learning approaches for enhanced prediction of hERG blockade: a comprehensive QSAR modeling study
Jie Liu
National Center for Toxicological Research (US)
3 shared publications
- Machine learning and deep learning approaches for enhanced prediction of hERG blockade: a comprehensive QSAR modeling study
- Developing predictive models for µ opioid receptor binding using machine learning and deep learning techniques
- Development of a comprehensive open access “molecules with androgenic activity resource (MAAR)” to facilitate risk assessment of chemicals
Stephen R. Arnott
Baycrest Hospital (CA)
2 shared publications
- Resting state fMRI scanner instabilities revealed by longitudinal phantom scans in a multi-center study
- FAIR in action: Brain-CODE - A neuroscience data sharing platform to accelerate brain research
Stephen C. Strother
Baycrest Hospital (CA)
2 shared publications
- Resting state fMRI scanner instabilities revealed by longitudinal phantom scans in a multi-center study
- FAIR in action: Brain-CODE - A neuroscience data sharing platform to accelerate brain research
Xueli Liu
State Ethnic Affairs Commission (CN)
2 shared publications
- Comment on acp-2021-866
- Comment on acp-2021-866
Liang Ran
Institute of Atmospheric Physics (CN)
2 shared publications
- Comment on acp-2021-866
- Comment on acp-2021-866
Similar Researchers
Based on overlapping research topics
Zoe Li
National Center for Toxicological Research
Machine Learning Applications
Advanced Neural Network Applications
Pharmacological Effects and Toxicity Studies
Minjun Chen
National Center for Toxicological Research
Machine Learning Applications
Advanced Neural Network Applications
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Tucker A. Patterson
National Center for Toxicological Research
Machine Learning Applications
Advanced Neural Network Applications
Pharmacological Effects and Toxicity Studies
Kamel Mansouri
National Center for Toxicological Research
Machine Learning Applications
Advanced Neural Network Applications
Pharmacological Effects and Toxicity Studies
Daniel Irabor
University of Arkansas at Pine Bluff
Advanced Neural Network Applications
Pharmacological Effects and Toxicity Studies
Medical Imaging Techniques and Applications
M. Fazlul Hoq
University of Arkansas for Medical Sciences
Machine Learning Applications
Advanced Neural Network Applications
Medical Imaging Techniques and Applications