Match tier
Likely match
Presence
Formerly Arkansas
Last published
2023
Sources
OpenAlex · ORCID
Refreshed
2026-10-06
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
Technical Co-founder & Head of Computational Chemistry, Encure Biotherapeutics
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); National Center for Biotechnology (2010); C4 Therapeutics (United States) (2022)
Formerly Arkansas Staff Scientist, NCTR through 2021; now Technical Co-founder & Head of Computational Chemistry, Encure Biotherapeutics.
29 h-index
79 pubs
3,003 cited
Research Areas
Biomedical Subjects
Links
Biography and Research Information
OverviewAI-generated summary
Sugunadevi Sakkiah's research focuses on computational toxicology and drug discovery, employing molecular modeling and simulation techniques to understand molecular interactions and predict biological activity. Her work includes the design of potential inhibitors for targets such as heat shock protein 90 (HSP90) and histone deacetylase 8 (HDAC8), utilizing methods like 3D Quantitative Structure-Activity Relationship (QSAR) pharmacophore modeling, virtual screening, and molecular docking.
Further investigations involve studying the molecular dynamics of viral proteases, specifically the Chikungunya virus nsP2 protease, to aid in antiviral drug design. Sakkiah has also contributed to research on the prediction of nanomaterial cytotoxicity using machine learning models and has explored the presence and detection of persistent organic pollutants in food. Her collaborations include significant work with researchers at the National Center for Toxicological Research, such as Tucker A. Patterson, Wenjing Guo, and Huixiao Hong.
With an h-index of 29, over 3,000 citations, and 79 publications, Sakkiah is recognized as a highly cited researcher. Her work spans diverse areas, including molecular dynamics simulations in toxicology and nanotoxicology, and the investigation of signaling pathways in cancer, such as the role of FOXC1 in hedgehog signaling.
Metrics
- h-index: 29
- Publications: 79
- Citations: 3,003
Positions
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Technical Co-founder & Head of Computational Chemistry 2026–presentEncure Biotherapeutics ORCID
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Guest Lecture 2025–presentJSS Academy of Higher Education and Research ORCID
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Research Scientist II 2021–2024C4 THERAPEUTICS LTD ORCID
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Staff Scientist 2018–2021FDA/NATIONAL CENTER FOR TOXICOLOGICAL RESEARCH ORCID
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Postdoctoral Researcher 2015–2018FDA/NATIONAL CENTER FOR TOXICOLOGICAL RESEARCH ORCID
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Postdoctoral Scientist 2014–2015Cedars-Sinai Medical Center ORCID
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Postdoctoral Researcher 2013–2014University of California at Los Angeles ORCID
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Postdoctoral Researcher 2012–2013Gwangju Institute of Science and Technology ORCID
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Research Executive 2006–2008Orchid Chemicals & Pharmaceuticals Limited ORCID
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Project Assistant 2005–2006Madurai Kamaraj University ORCID
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Project Associate 2004–2005GENOME BIOSCIENCE RESEARCH INSTITUTE ORCID
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
Huixiao Hong
National Center for Toxicological Research
28 shared publications
In database
- Persistent Organic Pollutants in Food: Contamination Sources, Health Effects and Detection Methods
- CoMPARA: Collaborative Modeling Project for Androgen Receptor Activity
- Molecular dynamics simulations and applications in computational toxicology and nanotoxicology
- Machine Learning Models for Predicting Cytotoxicity of Nanomaterials
- Similarities and differences between variants called with human reference genome HG19 or HG38
Showing 5 of 28 shared publications
Weida Tong
National Center for Toxicological Research
19 shared publications
In database
- Persistent Organic Pollutants in Food: Contamination Sources, Health Effects and Detection Methods
- Molecular dynamics simulations and applications in computational toxicology and nanotoxicology
- Similarities and differences between variants called with human reference genome HG19 or HG38
- Experimental Data Extraction and in Silico Prediction of the Estrogenic Activity of Renewable Replacements for Bisphenol A
- Structural Changes Due to Antagonist Binding in Ligand Binding Pocket of Androgen Receptor Elucidated Through Molecular Dynamics Simulations
Showing 5 of 19 shared publications
Wenjing Guo
National Center for Toxicological Research
16 shared publications
In database
- Persistent Organic Pollutants in Food: Contamination Sources, Health Effects and Detection Methods
- Machine Learning Models for Predicting Cytotoxicity of Nanomaterials
- Similarities and differences between variants called with human reference genome HG19 or HG38
- Nanomaterial Databases: Data Sources for Promoting Design and Risk Assessment of Nanomaterials
- Elucidating Interactions Between SARS-CoV-2 Trimeric Spike Protein and ACE2 Using Homology Modeling and Molecular Dynamics Simulations
Showing 5 of 16 shared publications
Bohu Pan
National Center for Toxicological Research
12 shared publications
In database
- Persistent Organic Pollutants in Food: Contamination Sources, Health Effects and Detection Methods
- Similarities and differences between variants called with human reference genome HG19 or HG38
- Elucidating Interactions Between SARS-CoV-2 Trimeric Spike Protein and ACE2 Using Homology Modeling and Molecular Dynamics Simulations
- Structural Changes Due to Antagonist Binding in Ligand Binding Pocket of Androgen Receptor Elucidated Through Molecular Dynamics Simulations
- Endocrine Disrupting Chemicals Mediated through Binding Androgen Receptor Are Associated with Diabetes Mellitus
Showing 5 of 12 shared publications
Weigong Ge
National Center for Toxicological Research
9 shared publications
In database
- Persistent Organic Pollutants in Food: Contamination Sources, Health Effects and Detection Methods
- Similarities and differences between variants called with human reference genome HG19 or HG38
- Structural Changes Due to Antagonist Binding in Ligand Binding Pocket of Androgen Receptor Elucidated Through Molecular Dynamics Simulations
- Consensus Modeling for Prediction of Estrogenic Activity of Ingredients Commonly Used in Sunscreen Products
- 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
- Nanomaterial Databases: Data Sources for Promoting Design and Risk Assessment of Nanomaterials
- Elucidating Interactions Between SARS-CoV-2 Trimeric Spike Protein and ACE2 Using Homology Modeling and Molecular Dynamics Simulations
- 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
National Center for Toxicological Research (US)
7 shared publications
- Machine Learning Models for Predicting Cytotoxicity of Nanomaterials
- Nanomaterial Databases: Data Sources for Promoting Design and Risk Assessment of Nanomaterials
- Elucidating Interactions Between SARS-CoV-2 Trimeric Spike Protein and ACE2 Using Homology Modeling and Molecular Dynamics Simulations
- 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
Hui Wen Ng
National Center for Toxicological Research (US)
6 shared publications
- Experimental Data Extraction and in Silico Prediction of the Estrogenic Activity of Renewable Replacements for Bisphenol A
- Structures of androgen receptor bound with ligands: advancing understanding of biological functions and drug discovery
- sNebula, a network-based algorithm to predict binding between human leukocyte antigens and peptides
- A Rat α-Fetoprotein Binding Activity Prediction Model to Facilitate Assessment of the Endocrine Disruption Potential of Environmental Chemicals
- Pathway Analysis Revealed Potential Diverse Health Impacts of Flavonoids that Bind Estrogen Receptors
Showing 5 of 6 shared publications
Chandrabose Selvaraj
Alagappa University (IN)
6 shared publications
- Molecular dynamics simulations and applications in computational toxicology and nanotoxicology
- Development of estrogen receptor beta binding prediction model using large sets of chemicals
- Consensus Modeling for Prediction of Estrogenic Activity of Ingredients Commonly Used in Sunscreen Products
- Elucidation of Agonist and Antagonist Dynamic Binding Patterns in ER-α by Integration of Molecular Docking, Molecular Dynamics Simulations and Quantum Mechanical Calculations
- Competitive docking model for prediction of the human nicotinic acetylcholine receptor α7 binding of tobacco constituents
Showing 5 of 6 shared publications
Rebecca Kusko
Oceaneering International (United States) (US)
6 shared publications
- Similarities and differences between variants called with human reference genome HG19 or HG38
- Structural Changes Due to Antagonist Binding in Ligand Binding Pocket of Androgen Receptor Elucidated Through Molecular Dynamics Simulations
- Computational prediction models for assessing endocrine disrupting potential of chemicals
- Applications of Molecular Dynamics Simulations in Computational Toxicology
- Correction to: Similarities and differences between variants called with human reference genome HG19 or HG38
Showing 5 of 6 shared publications
Ping Gong
United States Army (US)
5 shared publications
- Similarities and differences between variants called with human reference genome HG19 or HG38
- Development of estrogen receptor beta binding prediction model using large sets of chemicals
- A Rat α-Fetoprotein Binding Activity Prediction Model to Facilitate Assessment of the Endocrine Disruption Potential of Environmental Chemicals
- Correction to: Similarities and differences between variants called with human reference genome HG19 or HG38
- Mold2 Descriptors Facilitate Development of Machine Learning and Deep Learning Models for Predicting Toxicity of Chemicals
Gökhan Yavaş
National Center for Toxicological Research (US)
5 shared publications
- Persistent Organic Pollutants in Food: Contamination Sources, Health Effects and Detection Methods
- 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
Wenming Xiao
National Center for Toxicological Research (US)
3 shared publications
- Similarities and differences between variants called with human reference genome HG19 or HG38
- A Rat α-Fetoprotein Binding Activity Prediction Model to Facilitate Assessment of the Endocrine Disruption Potential of Environmental Chemicals
- Correction to: Similarities and differences between variants called with human reference genome HG19 or HG38
Wen Zou
National Center for Toxicological Research
3 shared publications
In database
- Persistent Organic Pollutants in Food: Contamination Sources, Health Effects and Detection Methods
- Endocrine Disrupting Chemicals Mediated through Binding Androgen Receptor Are Associated with Diabetes Mellitus
- Machine Learning Models for Predicting Liver Toxicity
Similar Researchers
Based on overlapping research topics
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