Match tier
Likely match
Presence
Current · Arkansas
Last published
2023
Sources
OpenAlex · ORCID
Refreshed
2026-08-15
S
Sugunadevi Sakkiah
This is a likely match — the affiliation was inferred from OpenAlex, ORCID, and web sources but has not been fully confirmed. Treat with appropriate caution.
High Impact
Researcher
Also affiliated: Chonnam National University (2013); Bharathiar University (2011); Cedars-Sinai Medical Center (2015–2018); United States Food and Drug Administration (2016–2023); University of California, Los Angeles (2013–2017); Gyeongsang National University (2009–2014); Gwangju Institute of Science and Technology (2013); C4 Therapeutics (United States) (2022)
Faculty Researcher
29 h-index
81 pubs
2,980 cited
Research Areas
Biomedical Subjects
Links
Biography and Research Information
OverviewAI-generated summary
Sugunadevi Sakkiah's research focuses on applying computational methods, including molecular dynamics simulations, homology modeling, and machine learning, to understand molecular interactions and predict biological outcomes. She has investigated the interactions between the SARS-CoV-2 spike protein and ACE2, contributing to the understanding of viral entry mechanisms. Her work also extends to drug discovery and toxicity prediction, with publications on machine learning models for predicting the cytotoxicity and liver toxicity of nanomaterials and other compounds.
Sakkiah has also explored the dynamics of ligand binding to proteins, such as estrogen receptor alpha (ER-α), using a combination of molecular docking, molecular dynamics, and quantum mechanical calculations. Her research interests include the development and application of databases for nanomaterials to support design and risk assessment, as well as the study of BPA replacement compounds. She has collaborated with researchers at the National Center for Toxicological Research, including Tucker A. Patterson, Wenjing Guo, and Huixiao Hong, on numerous projects.
Metrics
- h-index: 29
- Publications: 81
- Citations: 2,980
Selected Publications
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Mold2 Descriptors Facilitate Development of Machine Learning and Deep Learning Models for Predicting Toxicity of Chemicals (2023)
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Editorial: Novel Therapeutic Interventions Against Infectious Diseases: COVID-19 (2022)
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Machine Learning Models for Predicting Liver Toxicity (2022)
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Machine Learning Models for Predicting Cytotoxicity of Nanomaterials (2022)
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Elucidation of Agonist and Antagonist Dynamic Binding Patterns in ER-α by Integration of Molecular Docking, Molecular Dynamics Simulations and Quantum Mechanical Calculations (2021)
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Informing selection of drugs for COVID-19 treatment through adverse events analysis (2021)
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Nanomaterial Databases: Data Sources for Promoting Design and Risk Assessment of Nanomaterials (2021)
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Identification of Epidemiological Traits by Analysis of SARS−CoV−2 Sequences (2021)
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BPA Replacement Compounds: Current Status and Perspectives (2021)
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Elucidating Interactions Between SARS-CoV-2 Trimeric Spike Protein and ACE2 Using Homology Modeling and Molecular Dynamics Simulations (2021)
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Development of a Nicotinic Acetylcholine Receptor nAChR α7 Binding Activity Prediction Model (2020)
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CoMPARA: Collaborative Modeling Project for Androgen Receptor Activity (2020)
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Persistent Organic Pollutants in Food: Contamination Sources, Health Effects and Detection Methods (2019)
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Correction to: Similarities and differences between variants called with human reference genome HG19 or HG38 (2019)
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Applications of Molecular Dynamics Simulations in Computational Toxicology (2019)
Collaboration Network
Top Collaborators
Wenjing Guo
United States Food and Drug Administration
9 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
- 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
Showing 5 of 9 shared publications
Huixiao Hong
National Center for Toxicological Research
9 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
- 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
Showing 5 of 9 shared publications
Tucker A. Patterson
National Center for Toxicological Research
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)
7 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
- BPA Replacement Compounds: Current Status and Perspectives
- Machine Learning Models for Predicting Liver Toxicity
Showing 5 of 7 shared publications
Jie Liu
National Center for Toxicological Research
7 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
Showing 5 of 7 shared publications
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
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
Weida Tong
National Center for Toxicological Research
3 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
Chandrabose Selvaraj
Alagappa University (IN)
2 shared publications
- Elucidation of Agonist and Antagonist Dynamic Binding Patterns in ER-α by Integration of Molecular Docking, Molecular Dynamics Simulations and Quantum Mechanical Calculations
- Editorial: Novel Therapeutic Interventions Against Infectious Diseases: COVID-19
Weigong Ge
National Center for Toxicological Research
2 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
- Mold2 Descriptors Facilitate Development of Machine Learning and Deep Learning Models for Predicting Toxicity of Chemicals
Marli S.P. Azevedo
National Center for Toxicological Research
1 shared publication
In database
- Elucidating Interactions Between SARS-CoV-2 Trimeric Spike Protein and ACE2 Using Homology Modeling and Molecular Dynamics Simulations
Jessica J. Hawes
United States Food and Drug Administration (US)
1 shared publication
- Elucidating Interactions Between SARS-CoV-2 Trimeric Spike Protein and ACE2 Using Homology Modeling and Molecular Dynamics Simulations
Yanhui Lu
United States Food and Drug Administration (US)
1 shared publication
- Informing selection of drugs for COVID-19 treatment through adverse events analysis
Takashi Komatsu
United States Food and Drug Administration (US)
1 shared publication
- Informing selection of drugs for COVID-19 treatment through adverse events analysis
Madhu Lal‐Nag
Center for Drug Evaluation and Research (US)
1 shared publication
- Informing selection of drugs for COVID-19 treatment through adverse events analysis
Similar Researchers
Based on overlapping research topics
Tucker A. Patterson
National Center for Toxicological Research
Machine Learning Applications
SARS-CoV-2 and COVID-19 Research
Pharmacological Effects and Toxicity Studies
Wenjing Guo
National Center for Toxicological Research
Machine Learning Applications
SARS-CoV-2 and COVID-19 Research
Pharmacological Effects and Toxicity Studies
Fauzia Haque
University of Arkansas at Fayetteville
Machine Learning Applications
SARS-CoV-2 and COVID-19 Research
Computational Drug Discovery Methods
Bailang Liu
National Center for Toxicological Research
SARS-CoV-2 and COVID-19 Research
Pharmacological Effects and Toxicity Studies
Computational Drug Discovery Methods
C.M. White
University of Arkansas – Fort Smith
SARS-CoV-2 and COVID-19 Research
Pharmacological Effects and Toxicity Studies
Computational Drug Discovery Methods
Ugochi H. Isu
University of Arkansas at Fayetteville
Molecular Dynamics Simulation
SARS-CoV-2 and COVID-19 Research
Protein Structure and Dynamics