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Confirmed
Presence
Current · Arkansas
Last published
2026
Sources
OpenAlex · ORCID
Refreshed
2026-08-15
T
Tucker A. Patterson
Affiliation confirmed via AI analysis of OpenAlex, ORCID, and web sources.
High Impact
Researcher
Also affiliated: Boston University (2007); American Institute of Certified Public Accountants (2016); United States Food and Drug Administration (1999–2026); Center for Drug Evaluation and Research (2016); University of South Carolina (1989–1994); Columbia College - South Carolina (1992); American Association of Colleges of Pharmacy (1994–1996); University of Florida (1994–2007); Office of the Director (2018–2019); Office of the Director (2021–2025); Food and Drug Administration (2006–2026); Florida College (1994–1996)
Faculty Researcher
36 h-index
152 pubs
8,252 cited
Research Areas
Biomedical Subjects
Links
Biography and Research Information
OverviewAI-generated summary
Tucker A. Patterson's research centers on the application of machine learning and computational methods to toxicology and drug discovery. His work investigates the prediction of chemical and nanomaterial toxicity, the elucidation of protein-ligand interactions, and the development of models for predicting adverse biological effects, such as reproductive toxicity in rats.
Patterson also explores the use of advanced computational techniques for medical image analysis, including machine learning for brain tumor MRI segmentation. He has published on the use of machine learning and deep learning models for toxicity prediction and is involved in advancing alternative methods to reduce animal testing. His research network includes frequent collaborations with colleagues at the National Center for Toxicological Research, such as Fan Dong, Wenjing Guo, Zoe Li, and Sugunadevi Sakkiah.
With an h-index of 36 and over 8,200 citations across 151 publications, Patterson is recognized as a highly cited researcher. He leads a research group and maintains an active lab website.
Metrics
- h-index: 36
- Publications: 152
- Citations: 8,252
Selected Publications
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Machine Learning in Drug-induced Adverse Reaction Modeling: Case Studies of Drug-induced Cardiotoxicity Modeling (2026)
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Structural insights into cannabinoid receptors CB1 and CB2: determinants of ligand selectivity (2026)
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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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Identifying Sex Differences in Adverse Events Reported on Opioid Drugs in the FDA’s Adverse Event Reporting System (FAERS) (2026)
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Challenges and solutions in measuring commonly used biomarkers for drug-induced liver injury in a liver-on-a-chip platform (2025)
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Toxicity of ubiquitous tire rubber antiozonant N-(1,3-dimethylbutyl)-N′-phenyl-p-phenylenediamine (6PPD) and its transformation product 6PPD-quinone (6PPD-Q) in primary human hepatocytes and liver spheroids (2025)
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2024 international conference on neuroprotective agents conference proceedings (2025)
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Integrating Molecular Dynamics, Molecular Docking, and Machine Learning for Predicting SARS-CoV-2 Papain-like Protease Binders (2025)
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Assessing potential desflurane-induced neurotoxicity using nonhuman primate neural stem cell models (2025)
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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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Determining high priority disinfection byproducts based on experimental aquatic toxicity data and predictive models: Virtual screening and in vivo study (2024)
Collaboration Network
Top Collaborators
Huixiao Hong
National Center for Toxicological Research
30 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 30 shared publications
Wenjing Guo
National Center for Toxicological Research
24 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 24 shared publications
Jie Liu
United States Food and Drug Administration
24 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 24 shared publications
Fan Dong
United States Food and Drug Administration
17 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
- 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
Zoe Li
United States Food and Drug Administration
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
Sugunadevi Sakkiah
United States Food and Drug Administration
8 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
- 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
Showing 5 of 8 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
- Machine Learning Models for Predicting Liver Toxicity
- Informing selection of drugs for COVID-19 treatment through adverse events analysis
Showing 5 of 6 shared publications
William Slikker
5 shared publications
In database
- Neuroprotective Effects of Carnitine and Its Potential Application to Ameliorate Neurotoxicity
- Development of a primate model to evaluate the effects of ketamine and surgical stress on the neonatal brain
- Preface: 2022 International Conference on Neuroprotective Agents
- Phencyclidine (PCP)-induced neurotoxicity and behavioral deficits
- 2024 international conference on neuroprotective agents conference proceedings
Weida Tong
National Center for Toxicological Research
5 shared publications
In database
- Machine Learning Models for Predicting Liver Toxicity
- Informing selection of drugs for COVID-19 treatment through adverse events analysis
- Mold2 Descriptors Facilitate Development of Machine Learning and Deep Learning Models for Predicting Toxicity of Chemicals
- Decision forest—a machine learning algorithm for QSAR modeling
- Computational Toxicology
Weigong Ge
National Center for Toxicological Research
5 shared publications
In database
- 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
- Mold2 Descriptors Facilitate Development of Machine Learning and Deep Learning Models for Predicting Toxicity of Chemicals
- Developing predictive models for µ opioid receptor binding using machine learning and deep learning techniques
Gökhan Yavaş
United States Food and Drug Administration (US)
4 shared publications
- 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
- Mold2 Descriptors Facilitate Development of Machine Learning and Deep Learning Models for Predicting Toxicity of Chemicals
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
- 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
- Identification of Epidemiological Traits by Analysis of SARS−CoV−2 Sequences
Leah E. Latham
National Center for Toxicological Research
3 shared publications
In database
- Neuroprotective Effects of Carnitine and Its Potential Application to Ameliorate Neurotoxicity
- Development of a primate model to evaluate the effects of ketamine and surgical stress on the neonatal brain
- Phencyclidine (PCP)-induced neurotoxicity and behavioral deficits
Cheng Wang
National Center for Toxicological Research
3 shared publications
In database
- Neuroprotective Effects of Carnitine and Its Potential Application to Ameliorate Neurotoxicity
- Development of a primate model to evaluate the effects of ketamine and surgical stress on the neonatal brain
- Phencyclidine (PCP)-induced neurotoxicity and behavioral deficits
Similar Researchers
Based on overlapping research topics
Sugunadevi Sakkiah
National Center for Toxicological Research
Machine Learning Applications
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Wenjing Guo
National Center for Toxicological Research
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Fan Dong
National Center for Toxicological Research
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Fauzia Haque
University of Arkansas at Fayetteville
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C.M. White
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