W
Wenjing Guo Data-verified
Affiliation confirmed via AI analysis of OpenAlex, ORCID, and web sources.
High Impact
Researcher
faculty
30 h-index
195 pubs
3,435 cited
Research Areas
Links
Biography and Research Information
OverviewAI-generated summary
Wenjing Guo's research focuses on the application of machine learning and computational methods to toxicology and biological processes. Guo has investigated the use of machine learning models for predicting the cytotoxicity of nanomaterials and reviewed machine learning and deep learning models for toxicity prediction. Their work also includes elucidating molecular interactions, such as those between the SARS-CoV-2 trimeric spike protein and ACE2, using computational simulation techniques like homology modeling and molecular dynamics.
Further research extends to areas like regulating pluripotent-somatic transitions through phase separation and the fibrinolytic activity of cysteine-derived chiral carbon quantum dots in relation to Type 2 Diabetes Mellitus. Guo also explores data sources for promoting the design and risk assessment of nanomaterials. Guo leads a research group and collaborates with several researchers at the National Center for Toxicological Research, including Tucker A. Patterson, Sugunadevi Sakkiah, Fan Dong, and Weigong Ge, with whom they have co-authored multiple publications.
Metrics
- h-index: 30
- Publications: 195
- Citations: 3,435
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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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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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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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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Developing a SARS-CoV-2 main protease binding prediction random forest model for drug repurposing for COVID-19 treatment (2023)
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Deep Learning Models for Predicting Gas Adsorption Capacity of Nanomaterials (2022)
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Machine learning models for rat multigeneration reproductive toxicity prediction (2022)
Collaboration Network
Top Collaborators
Huixiao Hong
National Center for Toxicological Research
15 shared publications
In database
- Machine Learning Models for Predicting Cytotoxicity of Nanomaterials
- Review of machine learning and deep learning models for toxicity prediction
- 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 15 shared publications
Tucker A. Patterson
National Center for Toxicological Research
13 shared publications
In database
- Machine Learning Models for Predicting Cytotoxicity of Nanomaterials
- Review of machine learning and deep learning models for toxicity prediction
- 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 13 shared publications
Jie Liu
United States Food and Drug Administration
12 shared publications
In database
- Machine Learning Models for Predicting Cytotoxicity of Nanomaterials
- Review of machine learning and deep learning models for toxicity prediction
- 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 12 shared publications
Sugunadevi Sakkiah
National Center for Toxicological Research
7 shared publications
In database
- 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
Fan Dong
United States Food and Drug Administration
7 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 7 shared publications
Zuowei Ji
United States Food and Drug Administration (US)
6 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
Gökhan Yavaş
United States Food and Drug Administration (US)
4 shared publications
- 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
Weigong Ge
National Center for Toxicological Research
4 shared publications
In database
- 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
Md Kamrul Hasan Khan
National Center for Toxicological Research (US)
4 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
- Developing a SARS-CoV-2 main protease binding prediction random forest model for drug repurposing for COVID-19 treatment
Bohu Pan
National Center for Toxicological Research
3 shared publications
In database
- 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
Qizheng Wang
Chinese Academy of Sciences (CN)
3 shared publications
- The Dynamics of Metabolic Characterization in iPSC-Derived Kidney Organoid Differentiation via a Comparative Omics Approach
- Decellularised spinal cord matrix manipulates glial niche into repairing phase via serglycin‐mediated signalling pathway
- The ERK1/2–ATG13–FIP200 signaling cascade is required for autophagy induction to protect renal cells from hypoglycemia‐induced cell death
Heying Li
Chinese Academy of Sciences (CN)
3 shared publications
- The Dynamics of Metabolic Characterization in iPSC-Derived Kidney Organoid Differentiation via a Comparative Omics Approach
- Decellularised spinal cord matrix manipulates glial niche into repairing phase via serglycin‐mediated signalling pathway
- MYOCD is Required for Cardiomyocyte-like Cells Induction from Human Urine Cells and Fibroblasts Through Remodeling Chromatin
Xiao Zhang
Guangzhou Institutes of Biomedicine and Health (CN)
3 shared publications
- The Dynamics of Metabolic Characterization in iPSC-Derived Kidney Organoid Differentiation via a Comparative Omics Approach
- Decellularised spinal cord matrix manipulates glial niche into repairing phase via serglycin‐mediated signalling pathway
- The ERK1/2–ATG13–FIP200 signaling cascade is required for autophagy induction to protect renal cells from hypoglycemia‐induced cell death
Zoe Li
United States Food and Drug Administration
3 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
- Developing a SARS-CoV-2 main protease binding prediction random forest model for drug repurposing for COVID-19 treatment
Yucui Xiong
Guangzhou Institutes of Biomedicine and Health (CN)
2 shared publications
- The Dynamics of Metabolic Characterization in iPSC-Derived Kidney Organoid Differentiation via a Comparative Omics Approach
- Decellularised spinal cord matrix manipulates glial niche into repairing phase via serglycin‐mediated signalling pathway
Similar Researchers
Based on overlapping research topics
Tucker A. Patterson
National Center for Toxicological Research
Pharmacological Effects and Toxicity Studies
Machine Learning Applications
SARS-CoV-2 and COVID-19 Research
Sugunadevi Sakkiah
National Center for Toxicological Research
Pharmacological Effects and Toxicity Studies
Machine Learning Applications
SARS-CoV-2 and COVID-19 Research
Fauzia Haque
University of Arkansas at Fayetteville
Machine Learning Applications
SARS-CoV-2 and COVID-19 Research
Nanoparticles: synthesis and applications
Weigong Ge
National Center for Toxicological Research
Pharmacological Effects and Toxicity Studies
Machine Learning Applications
SARS-CoV-2 and COVID-19 Research
Manuel Alejandro Ramirez-Lee
National Center for Toxicological Research
Pharmacological Effects and Toxicity Studies
SARS-CoV-2 and COVID-19 Research
Nanoparticles: synthesis and applications
Zoe Li
National Center for Toxicological Research
Pharmacological Effects and Toxicity Studies
Machine Learning Applications
Computational Drug Discovery Methods