T
Tucker A. Patterson Data-verified
Affiliation confirmed via AI analysis of OpenAlex, ORCID, and web sources.
High Impact
Researcher
faculty
36 h-index
150 pubs
8,148 cited
Research Areas
Links
Biography and Research Information
OverviewAI-generated summary
Tucker A. Patterson's research focuses on the application of machine learning and deep learning methodologies to predict toxicity and understand biological interactions. His work has involved developing models to forecast the cytotoxicity of nanomaterials and predict multigeneration reproductive toxicity in rats. Patterson has also investigated protein-ligand interactions using computational methods, including homology modeling and molecular dynamics simulations, particularly in the context of SARS-CoV-2 spike protein and ACE2 interactions.
Further research areas include the development of machine learning models for brain tumor MRI image segmentation and the broader advancement of alternative methods to reduce animal testing. Patterson has a significant publication record, with 147 total publications and over 8,000 citations, reflected in his h-index of 35. He is recognized as a highly cited researcher. His research group includes key collaborators such as Fan Dong, Wenjing Guo, Zoe Li, and Sugunadevi Sakkiah, all from the National Center for Toxicological Research, with whom he has numerous shared publications.
Metrics
- h-index: 36
- Publications: 150
- Citations: 8,148
Selected Publications
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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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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)
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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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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)
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
18 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 18 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
William Slikker
7 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
- The NMDA Receptor System and Developmental Neurotoxicity
- Preface: 2022 International Conference on Neuroprotective Agents
- The NMDA Receptor System and Developmental Neurotoxicity
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
- 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
Cheng Wang
Neurolixis (United States) (US)
6 shared publications
- 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
- Assessing potential desflurane-induced neurotoxicity using nonhuman primate neural stem cell models
- The NMDA Receptor System and Developmental Neurotoxicity
- The NMDA Receptor System and Developmental Neurotoxicity
Showing 5 of 6 shared publications
Fang Liu
Neurolixis (United States)
6 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
- Assessing potential desflurane-induced neurotoxicity using nonhuman primate neural stem cell models
- The NMDA Receptor System and Developmental Neurotoxicity
- The NMDA Receptor System and Developmental Neurotoxicity
Showing 5 of 6 shared publications
Weida Tong
National Center for Toxicological Research (US)
6 shared publications
- AI-powered drug repurposing for developing COVID-19 treatments
- 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
Showing 5 of 6 shared publications
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
Shuliang Liu
National Center for Toxicological Research
5 shared publications
In database
- Development of a primate model to evaluate the effects of ketamine and surgical stress on the neonatal brain
- Assessing potential desflurane-induced neurotoxicity using nonhuman primate neural stem cell models
- The NMDA Receptor System and Developmental Neurotoxicity
- The NMDA Receptor System and Developmental Neurotoxicity
- Phencyclidine (PCP)-induced neurotoxicity and behavioral deficits
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
Leah E. Latham
Neurolixis (United States)
4 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
- Assessing potential desflurane-induced neurotoxicity using nonhuman primate neural stem cell models
- Phencyclidine (PCP)-induced neurotoxicity and behavioral deficits
Similar Researchers
Based on overlapping research topics
Wenjing Guo
National Center for Toxicological Research
Pharmacological Effects and Toxicity Studies
Machine Learning Applications
Nanoparticles: synthesis and applications
Weigong Ge
National Center for Toxicological Research
Pharmacological Effects and Toxicity Studies
Machine Learning Applications
Advanced Neural Network Applications
Sugunadevi Sakkiah
National Center for Toxicological Research
Pharmacological Effects and Toxicity Studies
Machine Learning Applications
Nanoparticles: synthesis and applications
Arpan Poudel
University of Arkansas at Fayetteville
Pharmacological Effects and Toxicity Studies
Machine Learning Applications
Advanced Neural Network Applications
Zoe Li
National Center for Toxicological Research
Pharmacological Effects and Toxicity Studies
Machine Learning Applications
Advanced Neural Network Applications
Fan Dong
National Center for Toxicological Research
Pharmacological Effects and Toxicity Studies
Machine Learning Applications
Advanced Neural Network Applications