Match tier
Confirmed
Presence
Current · Arkansas
Last published
2025
Sources
OpenAlex · ORCID
Refreshed
2026-08-17
Z
Zoe Li
Affiliation confirmed via AI analysis of OpenAlex, ORCID, and web sources.
Researcher
Also affiliated: United States Food and Drug Administration (2023–2025); Douglas Mental Health University Institute (2025); The Ohio State University (2018–2022); Universitatea Națională de Știință și Tehnologie Politehnica București (2023); University of Glasgow (2024–2025); McMaster University (2017–2025)
Faculty Researcher
13 h-index
38 pubs
544 cited
Research Areas
Biomedical Subjects
Links
Biography and Research Information
OverviewAI-generated summary
Zoe Li's research focuses on the application of machine learning and deep learning techniques to address complex problems in toxicology, medical imaging, and infrastructure resilience. She has investigated the efficacy of these computational methods for predicting toxicity, segmenting brain tumor MRI images, and understanding recovery disparities following extreme weather events. Li's work also extends to environmental science, with publications examining the effects of reservoir systems on river basins in China and the impact of climate change on hydrological processes. Her research network includes significant collaboration with Tucker A. Patterson and Fan Dong at the National Center for Toxicological Research, with whom she has co-authored numerous publications. Li's scholarship metrics include an h-index of 13, with 38 total publications and 523 citations.
Metrics
- h-index: 13
- Publications: 38
- Citations: 544
Selected Publications
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Developing predictive models for µ opioid receptor binding using machine learning and deep learning techniques (2025)
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Computational Toxicology (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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Fingerprinting Interactions between Proteins and Ligands for Facilitating Machine Learning in Drug Discovery (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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QSAR models for predicting in vivo reproductive toxicity (2023)
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EADB—A database providing curated data for developing QSAR models to facilitate the assessment of endocrine activity (2023)
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Decision forest—a machine learning algorithm for QSAR modeling (2023)
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Three-Dimensional Structural Insights Have Revealed the Distinct Binding Interactions of Agonists, Partial Agonists, and Antagonists with the µ Opioid Receptor (2023)
Collaboration Network
Top Collaborators
Tucker A. Patterson
National Center for Toxicological Research
11 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
- Fingerprinting Interactions between Proteins and Ligands for Facilitating Machine Learning in Drug Discovery
- 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
Showing 5 of 11 shared publications
Huixiao Hong
National Center for Toxicological Research
11 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
- Fingerprinting Interactions between Proteins and Ligands for Facilitating Machine Learning in Drug Discovery
- 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
Showing 5 of 11 shared publications
Jie Liu
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 a SARS-CoV-2 main protease binding prediction random forest model for drug repurposing for COVID-19 treatment
- QSAR models for predicting in vivo reproductive toxicity
Showing 5 of 9 shared publications
Fan Dong
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
- 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
Wenjing Guo
National Center for Toxicological Research
8 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
- 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 8 shared publications
Ruili Huang
National Institutes of Health (US)
3 shared publications
- Fingerprinting Interactions between Proteins and Ligands for Facilitating Machine Learning in Drug Discovery
- 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)
3 shared publications
- Fingerprinting Interactions between Proteins and Ligands for Facilitating Machine Learning in Drug Discovery
- 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
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
- Developing a SARS-CoV-2 main protease binding prediction random forest model for drug repurposing for COVID-19 treatment
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
Myeongsang Lee
United States Food and Drug Administration (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
Liang Xu
United States Food and Drug Administration (US)
2 shared publications
- Developing a SARS-CoV-2 main protease binding prediction random forest model for drug repurposing for COVID-19 treatment
- Decision forest—a machine learning algorithm for QSAR modeling
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
Weida Tong
National Center for Toxicological Research
2 shared publications
In database
- Decision forest—a machine learning algorithm for QSAR modeling
- Computational Toxicology
Weigong Ge
National Center for Toxicological Research
2 shared publications
In database
- 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
Minjun Chen
United States Food and Drug Administration
1 shared publication
In database
- Decision forest—a machine learning algorithm for QSAR modeling
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