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: Shanghai University (2025); University of Technology Sydney (2014–2021); Inner Mongolia Agricultural University (2025); Beijing Institute of Technology (2014–2018); United States Food and Drug Administration (2022–2026); Qingdao University of Science and Technology (2008); Northeast Agricultural University (2009–2022); Chinese Academy of Sciences (2010–2025); Hainan University (2019–2020); Beijing Normal University (2014–2017); Xi'an Polytechnic University (2025); Baycrest Hospital (2015–2018); Wuhan University (2014); Institute of Computing Technology (2023–2025); Shandong Institute of Automation (2010); Centre for Quantum Computation and Communication Technology (2014); Peng Cheng Laboratory (2026); Indoc Research (2018–2023); University of Chinese Academy of Sciences (2025); Rotman Research Institute (2015–2018); University of Southern Mississippi (2012–2013); Nanjing University (2008–2009)
Faculty Researcher
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 drug safety. Dr. Dong has investigated the use of these computational methods for predicting the toxicity of chemical compounds, including gas adsorption capacity of nanomaterials and rat multigeneration reproductive toxicity. Additionally, Dr. Dong's work extends to predicting the hERG blockade potential of drug candidates, a critical aspect of cardiovascular safety assessment. The researcher has also explored the use of BERT-based language models for extracting drug adverse events from social media data, contributing to enhanced pharmacovigilance practices. Dr. Dong's publications include a review of machine learning and deep learning models for toxicity prediction and work on brain tumor MRI image segmentation. Collaborations include extensive shared publications with Tucker A. Patterson, Zoe Li, and Wenjing Guo at the National Center for Toxicological Research.
Metrics
- h-index: 13
- Publications: 71
- Citations: 677
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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- Review of machine learning and deep learning models for toxicity prediction
- Decision forest—a machine learning algorithm for QSAR modeling
- Decision forest—a machine learning algorithm for QSAR modeling
- List of contributors
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