Biography and Research Information
OverviewAI-generated summary
Sugunadevi Sakkiah's research focuses on the application of computational methods, including molecular docking, molecular dynamics simulations, and machine learning, to investigate molecular interactions and predict toxicity and pharmacological effects. Her work has contributed to understanding the binding patterns of ligands to proteins, such as estrogen receptor alpha, and investigating interactions between viral proteins like the SARS-CoV-2 spike protein and the ACE2 receptor.
Sakkiah has also explored the use of machine learning models for predicting the cytotoxicity of nanomaterials and liver toxicity, aligning with efforts to promote the design and risk assessment of nanomaterials. Her research interests extend to the analysis of adverse events in drug selection for COVID-19 treatment and the identification of epidemiological traits from viral sequences. She has published 81 papers, with a total of 2,889 citations, and holds an h-index of 29. She has collaborated with researchers at the National Center for Toxicological Research, including Tucker A. Patterson and Wenjing Guo, on multiple publications.
Metrics
- h-index: 29
- Publications: 81
- Citations: 2,917
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)
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 (US)
3 shared publications
- 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)
3 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
- Molecular Insights into Agonist/Antagonist Effects on Macromolecules Involved inHuman Disease Mechanisms
- 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
Wenjing Guo
National Center for Toxicological Research
Computational Drug Discovery Methods
Protein Structure and Dynamics
Pharmacological Effects and Toxicity Studies
Fauzia Haque
University of Arkansas at Fayetteville
Computational Drug Discovery Methods
Protein Structure and Dynamics
SARS-CoV-2 and COVID-19 Research
Tucker A. Patterson
National Center for Toxicological Research
Computational Drug Discovery Methods
Protein Structure and Dynamics
Pharmacological Effects and Toxicity Studies
Sayo Fakayode
University of Arkansas – Fort Smith
Computational Drug Discovery Methods
Protein Structure and Dynamics
Pharmacological Effects and Toxicity Studies
Darin E. Jones
University of Arkansas for Medical Sciences
Computational Drug Discovery Methods
Protein Structure and Dynamics
Pharmacological Effects and Toxicity Studies
Weigong Ge
National Center for Toxicological Research
Computational Drug Discovery Methods
Pharmacological Effects and Toxicity Studies
SARS-CoV-2 and COVID-19 Research