Wenjing Guo
Affiliation confirmed via AI analysis of OpenAlex, ORCID, and web sources.
Researcher
Also affiliated: Jiangxi University of Traditional Chinese Medicine (2020–2021); Shanghai University (2022); Hunan University of Science and Technology (2019); University of Science and Technology of China (2018–2023); United States Food and Drug Administration (2018–2026); Central Statistical Office (2021); Nanjing University of Finance and Economics (2005); Nanchang University (2025); Southwest University (2014); Xidian University (2003); Dalian University of Foreign Languages (2022); China Pharmaceutical University (2020–2024); Hefei University of Technology (2025); Southwest Petroleum University (2020); Huaibei Normal University (2013); City University of Hong Kong (2016–2021); Bengbu Medical College (2020); Henan University (2024); Donghua University (2022); South China Normal University (2010–2011); Chinese Academy of Sciences (2019–2025); Peking University (2022–2024); Dalian University of Technology (2021–2022); West China Medical Center of Sichuan University (2020–2023); Shandong Normal University (2022); Guangzhou Institutes of Biomedicine and Health (2019–2025); China University of Geosciences (Beijing) (2023); Nanjing University of Science and Technology (2025); Hefei University (2018); Health First (2021); Gansu University of Traditional Chinese Medicine (2021–2023); Dalian University (2022); State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation (2020); Zhejiang Cancer Hospital (2016–2020); Institute of Microbiology (2025); Shandong Xiehe University (2025–2026); Hefei National Center for Physical Sciences at Nanoscale (2018–2023); Guangzhou Regenerative Medicine and Health Guangdong Laboratory (2020); First Affiliated Hospital of Xinxiang Medical University (2023); Czech Academy of Sciences, Institute of Microbiology (2025); Institute of Microbiology (2024–2025); Xi’an University of Posts and Telecommunications (2023–2025); GlaxoSmithKline (China) (2011); Cancer Hospital of Chinese Academy of Medical Sciences (2020); First Affiliated Hospital of Bengbu Medical College (2020–2023); State Key Laboratory of Respiratory Disease (2025); Guangzhou Eighth People's Hospital (2021–2025); Tongji Hospital (2020–2022); University of Chinese Academy of Sciences (2024–2025); State Key Laboratory of Natural Medicine (2020–2022); Huazhong University of Science and Technology (2020–2022); University at Buffalo, State University of New York (2018–2019); Shanghai Institute of Technology (2019–2021); Zhejiang Gongshang University (2020); Zhejiang University (2012); Shandong University of Science and Technology (2018–2020); Fuzhou University (2015); Woodward (United States) (2016); University of Hong Kong (2013–2014); Northwest A&F University (2021); Guangzhou Medical University (2019–2025); University of Science and Technology Beijing (2024–2025); Xinjiang University (2025)
Faculty Researcher
Research Areas
Biomedical Subjects
Links
Biography and Research Information
OverviewAI-generated summary
Wenjing Guo's research focuses on the intersection of toxicology, machine learning, and molecular biology. Guo has investigated the prediction of cytotoxicity for nanomaterials using machine learning models and explored the application of machine learning and deep learning in toxicity prediction more broadly. In the realm of molecular mechanisms, Guo has studied the regulation of pluripotent-somatic transitions through phase separation and elucidated interactions between the SARS-CoV-2 trimeric spike protein and ACE2 using computational methods.
Further research interests include the fibrinolytic activity of cysteine-derived chiral carbon quantum dots for targeting human islet amyloid polypeptide in Type 2 Diabetes Mellitus and the photocatalytic activity of supercritical CO2-tailored 2D oxygen-doped amorphous carbon nitride. Guo has also contributed to studies on academic performance in the context of COVID-19 and the role of online learning readiness and emotional competence. With a substantial publication record, including 196 publications and over 3,500 citations, Guo holds an h-index of 30 and is recognized as a highly cited researcher. Guo leads a research group and collaborates with several colleagues at the National Center for Toxicological Research, including Tucker A. Patterson, Sugunadevi Sakkiah, Fan Dong, and Weigong Ge.
Metrics
- h-index: 30
- Publications: 198
- Citations: 3,599
Selected Publications
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Identifying Sex Differences in Adverse Events Reported on Opioid Drugs in the FDA’s Adverse Event Reporting System (FAERS) (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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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)
Collaboration Network
Top Collaborators
- Review of machine learning and deep learning models for toxicity prediction
- Machine Learning Models for Predicting Cytotoxicity of Nanomaterials
- Elucidating Interactions Between SARS-CoV-2 Trimeric Spike Protein and ACE2 Using Homology Modeling and Molecular Dynamics Simulations
- Nanomaterial Databases: Data Sources for Promoting Design and Risk Assessment of Nanomaterials
- BPA Replacement Compounds: Current Status and Perspectives
Showing 5 of 18 shared publications
- Review of machine learning and deep learning models for toxicity prediction
- Machine Learning Models for Predicting Cytotoxicity of Nanomaterials
- Elucidating Interactions Between SARS-CoV-2 Trimeric Spike Protein and ACE2 Using Homology Modeling and Molecular Dynamics Simulations
- Nanomaterial Databases: Data Sources for Promoting Design and Risk Assessment of Nanomaterials
- Machine learning and deep learning for brain tumor MRI image segmentation
Showing 5 of 15 shared publications
- Review of machine learning and deep learning models for toxicity prediction
- Machine Learning Models for Predicting Cytotoxicity of Nanomaterials
- Nanomaterial Databases: Data Sources for Promoting Design and Risk Assessment of Nanomaterials
- BPA Replacement Compounds: Current Status and Perspectives
- Deep Learning Models for Predicting Gas Adsorption Capacity of Nanomaterials
Showing 5 of 13 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 9 shared publications
- Machine Learning Models for Predicting Cytotoxicity of Nanomaterials
- Elucidating Interactions Between SARS-CoV-2 Trimeric Spike Protein and ACE2 Using Homology Modeling and Molecular Dynamics Simulations
- Nanomaterial Databases: Data Sources for Promoting Design and Risk Assessment of Nanomaterials
- BPA Replacement Compounds: Current Status and Perspectives
- Elucidation of Agonist and Antagonist Dynamic Binding Patterns in ER-α by Integration of Molecular Docking, Molecular Dynamics Simulations and Quantum Mechanical Calculations
Showing 5 of 7 shared publications
- Machine Learning Models for Predicting Cytotoxicity of Nanomaterials
- Elucidating Interactions Between SARS-CoV-2 Trimeric Spike Protein and ACE2 Using Homology Modeling and Molecular Dynamics Simulations
- Nanomaterial Databases: Data Sources for Promoting Design and Risk Assessment of Nanomaterials
- BPA Replacement Compounds: Current Status and Perspectives
- Machine Learning Models for Predicting Liver Toxicity
Showing 5 of 6 shared publications
- Deep Learning Models for Predicting Gas Adsorption Capacity of Nanomaterials
- Elucidation of Agonist and Antagonist Dynamic Binding Patterns in ER-α by Integration of Molecular Docking, Molecular Dynamics Simulations and Quantum Mechanical Calculations
- Machine learning and deep learning approaches for enhanced prediction of hERG blockade: a comprehensive QSAR modeling study
- Developing a SARS-CoV-2 main protease binding prediction random forest model for drug repurposing for COVID-19 treatment
- Developing predictive models for µ opioid receptor binding using machine learning and deep learning techniques
- Assessing reproducibility of inherited variants detected with short-read whole genome sequencing
- Elucidating Interactions Between SARS-CoV-2 Trimeric Spike Protein and ACE2 Using Homology Modeling and Molecular Dynamics Simulations
- Machine Learning Models for Predicting Liver Toxicity
- Informing selection of drugs for COVID-19 treatment through adverse events analysis
- Review of machine learning and deep learning models for toxicity prediction
- Machine learning and deep learning for brain tumor MRI image segmentation
- Developing a SARS-CoV-2 main protease binding prediction random forest model for drug repurposing for COVID-19 treatment
- Developing predictive models for µ opioid receptor binding using machine learning and deep learning techniques
- 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
- Developing a SARS-CoV-2 main protease binding prediction random forest model for drug repurposing for COVID-19 treatment
- Assessing reproducibility of inherited variants detected with short-read whole genome sequencing
- Elucidating Interactions Between SARS-CoV-2 Trimeric Spike Protein and ACE2 Using Homology Modeling and Molecular Dynamics Simulations
- Informing selection of drugs for COVID-19 treatment through adverse events analysis
- Machine Learning Models for Predicting Liver Toxicity
- Informing selection of drugs for COVID-19 treatment through adverse events analysis
- Machine Learning Models for Predicting Cytotoxicity of Nanomaterials
- 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
- Elucidating Interactions Between SARS-CoV-2 Trimeric Spike Protein and ACE2 Using Homology Modeling and Molecular Dynamics Simulations
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