J
Jie Liu
High Impact
Staff Fellow
NCTR
staff
46 h-index
292 pubs
7,849 cited
Research Areas
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Biography and Research Information
Metrics
- h-index: 46
- Publications: 292
- Citations: 7,849
Selected Publications
- Analysis of Structures of SARS-CoV-2 Papain-like Protease Bound with Ligands Unveils Structural Features for Inhibiting the Enzyme (2025) DOI
- BERT-based language model for accurate drug adverse event extraction from social media: implementation, evaluation, and contributions to pharmacovigilance practices (2024) DOI
- Machine learning and deep learning for brain tumor MRI image segmentation (2023) DOI
- Review of machine learning and deep learning models for toxicity prediction (2023) DOI
- Developing a SARS-CoV-2 main protease binding prediction random forest model for drug repurposing for COVID-19 treatment (2023) DOI
- Three-Dimensional Structural Insights Have Revealed the Distinct Binding Interactions of Agonists, Partial Agonists, and Antagonists with the µ Opioid Receptor (2023) DOI
- Deep Learning Models for Predicting Gas Adsorption Capacity of Nanomaterials (2022) DOI
- Machine learning models for rat multigeneration reproductive toxicity prediction (2022) DOI
- Machine Learning Models for Predicting Liver Toxicity (2022) DOI
- Machine Learning Models for Predicting Cytotoxicity of Nanomaterials (2022) DOI
- Elucidation of Agonist and Antagonist Dynamic Binding Patterns in ER-α by Integration of Molecular Docking, Molecular Dynamics Simulations and Quantum Mechanical Calculations (2021) DOI
- Nanomaterial Databases: Data Sources for Promoting Design and Risk Assessment of Nanomaterials (2021) DOI
- BPA Replacement Compounds: Current Status and Perspectives (2021) DOI
Collaboration Network
Top Collaborators
Huixiao Hong
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
- 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
Wenjing Guo
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
Tucker A. Patterson
National Center for Toxicological Research
11 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
- Machine learning and deep learning for brain tumor MRI image segmentation
- Machine learning models for rat multigeneration reproductive toxicity prediction
Showing 5 of 11 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
- BERT-based language model for accurate drug adverse event extraction from social media: implementation, evaluation, and contributions to pharmacovigilance practices
Showing 5 of 7 shared publications
Sugunadevi Sakkiah
National Center for Toxicological Research
5 shared publications
In database
- 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
- 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 Models for Predicting Liver Toxicity
Zuowei Ji
United States Food and Drug Administration (US)
4 shared publications
- 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
- Machine Learning Models for Predicting Liver Toxicity
Zoe Li
United States Food and Drug Administration
4 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 a SARS-CoV-2 main protease binding prediction random forest model for drug repurposing for COVID-19 treatment
Weigong Ge
National Center for Toxicological Research
3 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
- Developing a SARS-CoV-2 main protease binding prediction random forest model for drug repurposing for COVID-19 treatment