Match tier
Likely match
Presence
Current · Arkansas
Last published
2026
Sources
OpenAlex · ORCID
Refreshed
2026-10-05
F
Fan Dong
This is a likely match — the affiliation was inferred from OpenAlex, ORCID, and web sources but has not been fully confirmed. Treat with appropriate caution.
Senior Engineer
Also affiliated: University of Technology Sydney (2014–2019); Beijing Institute of Technology (2014–2018); United States Food and Drug Administration (2022–2026); Baycrest Hospital (2015); Peng Cheng Laboratory (2026); Indoc Research (2018–2023); Rotman Research Institute (2015–2018)
13 h-index
34 pubs
585 cited
Research Areas
Biomedical Subjects
Links
Biography and Research Information
OverviewAI-generated summary
Fan Dong's research focuses on the application of machine learning and deep learning techniques to address challenges in toxicology and neuroscience. Dong has investigated the use of these computational methods for predicting the toxicity of various substances, including gas adsorption capacity of nanomaterials and the potential for hERG blockade. Additionally, Dong's work has explored the application of deep learning for brain tumor MRI image segmentation and the development of adaptive frameworks for optimizing preprocessing pipelines in functional MRI. Other research interests include the detection of model drift in data-driven decision support systems and the development of secure neuroinformatics platforms. Dong has 71 publications and a h-index of 14, with key collaborators including Tucker A. Patterson, Zoe Li, Wenjing Guo, and Huixiao Hong, all from the National Center for Toxicological Research.
Metrics
- h-index: 13
- Publications: 34
- Citations: 585
Positions
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Senior Engineer 2022–presentInstitute of Computing Technology, Chinese Academy of Sciences ORCID
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Senior Engineer publications 2022–2026National Center for Toxicological Research ORCID
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Workforce Health Assessors Pty Ltd 2021–2022ORCID
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Research Fellow 2018–2020University of Technology Sydney School of Computer Science ORCID
Selected Publications
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BERT-Based Models for Normalization of Adverse Drug Event Expressions in Social Media to Standard Medical Terminology for Drug Safety Analysis (2026)
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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)
Collaboration Network
Top Collaborators
Huixiao Hong
National Center for Toxicological Research
21 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 21 shared publications
Wenjing Guo
National Center for Toxicological Research
18 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 18 shared publications
Tucker A. Patterson
National Center for Toxicological Research
18 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 18 shared publications
Jie Liu
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
- 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
National Center for Toxicological Research
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
- Developing predictive models for µ opioid receptor binding using machine learning and deep learning techniques
- QSAR models for predicting in vivo reproductive toxicity
Showing 5 of 9 shared publications
Jie Liu
National Center for Toxicological Research
4 shared publications
In database
- 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
- BERT-Based Models for Normalization of Adverse Drug Event Expressions in Social Media to Standard Medical Terminology for Drug Safety Analysis
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
3 shared publications
In database
- 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
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
Ann Varghese
National Center for Toxicological Research
3 shared publications
In database
- Analysis of Structures of SARS-CoV-2 Papain-like Protease Bound with Ligands Unveils Structural Features for Inhibiting the Enzyme
- Decoding the κ Opioid Receptor (KOR): Advancements in Structural Understanding and Implications for Opioid Analgesic Development
- BERT-Based Models for Normalization of Adverse Drug Event Expressions in Social Media to Standard Medical Terminology for Drug Safety Analysis
Nancy Chang
United States Food and Drug Administration (US)
2 shared publications
- Three-Dimensional Structural Insights Have Revealed the Distinct Binding Interactions of Agonists, Partial Agonists, and Antagonists with the µ Opioid Receptor
- Decoding the κ Opioid Receptor (KOR): Advancements in Structural Understanding and Implications for Opioid Analgesic Development
Ruili Huang
National Institutes of Health (US)
2 shared publications
- Three-Dimensional Structural Insights Have Revealed the Distinct Binding Interactions of Agonists, Partial Agonists, and Antagonists with the µ Opioid Receptor
- Decoding the κ Opioid Receptor (KOR): Advancements in Structural Understanding and Implications for Opioid Analgesic Development
Menghang Xia
National Institutes of Health (US)
2 shared publications
- Three-Dimensional Structural Insights Have Revealed the Distinct Binding Interactions of Agonists, Partial Agonists, and Antagonists with the µ Opioid Receptor
- Decoding the κ Opioid Receptor (KOR): Advancements in Structural Understanding and Implications for Opioid Analgesic Development
Myeongsang Lee
National Center for Toxicological Research (US)
2 shared publications
- Decision forest—a machine learning algorithm for QSAR modeling
- EADB—A database providing curated data for developing QSAR models to facilitate the assessment of endocrine activity
Meng Song
National Center for Toxicological Research (US)
2 shared publications
- Review of machine learning and deep learning models for toxicity prediction
- Decision forest—a machine learning algorithm for QSAR modeling
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