Tucker A. Patterson Source Confirmed

Affiliation confirmed via AI analysis of OpenAlex, ORCID, and web sources.

High Impact

Researcher

National Center for Toxicological Research

faculty

35 h-index 147 pubs 8,058 cited

Research Areas

Pharmacological Effects and Toxicity Studies Nanoparticles: synthesis and applications Advanced Neural Network Applications SARS-CoV-2 and COVID-19 Research Computational Drug Discovery Methods Medical Imaging Techniques and Applications Meta-analysis and systematic reviews Carcinogens and Genotoxicity Assessment

Links

ORCID Lab Website Research Group

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OverviewAI-generated summary

Tucker A. Patterson's research program focuses on the application of machine learning and computational modeling to predict the toxicity of chemical compounds and nanomaterials, with the goal of advancing alternative methods to reduce animal testing. His work also investigates molecular interactions relevant to drug discovery and disease mechanisms. He has published on the use of machine learning for predicting the cytotoxicity of nanomaterials and rat multigeneration reproductive toxicity. Additionally, Patterson has explored molecular dynamics simulations to understand interactions between the SARS-CoV-2 trimeric spike protein and ACE2. His research extends to the development of nanomaterial databases to support design and risk assessment, and he has also investigated machine learning applications in medical image segmentation for brain tumors.

Patterson has a publication record that includes 147 total publications, with a total of 8,058 citations, and an h-index of 35. He is recognized as a highly cited researcher. His collaborations include extensive work with colleagues at the National Center for Toxicological Research, including Fan Dong (18 shared publications), Wenjing Guo (13 shared publications), Zoe Li (12 shared publications), and Sugunadevi Sakkiah (8 shared publications). He maintains an active laboratory website.

Metrics

h-index: 35
Publications: 147
Citations: 8,058

Selected Publications

Challenges and solutions in measuring commonly used biomarkers for drug-induced liver injury in a liver-on-a-chip platform (2025) DOI
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) DOI
2024 international conference on neuroprotective agents conference proceedings (2025) DOI
Integrating Molecular Dynamics, Molecular Docking, and Machine Learning for Predicting SARS-CoV-2 Papain-like Protease Binders (2025) DOI
Pharmacovigilance in the digital age: gaining insight from social media data (2025) DOI
A refined set of RxNorm drug names for enhancing unstructured data analysis in drug safety surveillance (2025) DOI
Developing predictive models for µ opioid receptor binding using machine learning and deep learning techniques (2025) DOI
Analysis of Structures of SARS-CoV-2 Papain-like Protease Bound with Ligands Unveils Structural Features for Inhibiting the Enzyme (2025) DOI
Computational Toxicology (2024) DOI
Determining high priority disinfection byproducts based on experimental aquatic toxicity data and predictive models: Virtual screening and in vivo study (2024) DOI
Machine learning and deep learning approaches for enhanced prediction of hERG blockade: a comprehensive QSAR modeling study (2024) DOI
Decoding the κ Opioid Receptor (KOR): Advancements in Structural Understanding and Implications for Opioid Analgesic Development (2024) DOI
BERT-based language model for accurate drug adverse event extraction from social media: implementation, evaluation, and contributions to pharmacovigilance practices (2024) DOI
Fingerprinting Interactions between Proteins and Ligands for Facilitating Machine Learning in Drug Discovery (2024) DOI
Machine learning and deep learning for brain tumor MRI image segmentation (2023) DOI