Svetoslav H. Slavov
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Also affiliated: Tallinn University of Technology (2006–2010); University of North Texas (2008); United States Food and Drug Administration (2012–2022); Barrow Neurological Institute (2020); University of Florida Health (2020); University of Florida (2006–2020); Integrated Laboratory Systems, Inc. (2016); University of Tartu (2006–2008); Romanian Academy (2008); Sofia University "St. Kliment Ohridski" (2006–2011); University of Pennsylvania (2020); Politecnico di Milano (2008)
Research Areas
Biomedical Subjects
Links
Biography and Research Information
OverviewAI-generated summary
Svetoslav Slavov's research focuses on the application of computational methods and molecular modeling to understand drug interactions and toxicity. He investigates the structural factors that influence binding to specific biological targets, such as the cannabinoid receptor type 1, and has published work on predicting and experimentally evaluating the cardiotoxic potential of drugs, specifically focusing on the hERG channel. His work also extends to the development and application of algorithms and computer simulations for molecular modeling and drug design. Slavov's scholarly output includes 49 publications with over 1,800 citations, and he holds an h-index of 19. He has collaborated with Richard D. Beger at the National Center for Toxicological Research on shared publications.
Metrics
- h-index: 19
- Publications: 48
- Citations: 1,834
Selected Publications
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Prediction and Experimental Evaluation of the hERG Blocking Potential of Drugs Showing Clinical Signs of Cardiotoxicity (2022)
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Identification of structural factors that affect binding to cannabinoid receptor type 1 (2021)
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A Methyl Scan of the Pyrrolidinium Ring of Nicotine Reveals Significant Differences in Its Interactions with α7 and α4β2 Nicotinic Acetylcholine Receptors (2020)
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Quantitative structure–toxicity relationships in translational toxicology (2020)
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Determination of structural factors affecting binding to mu, kappa and delta opioid receptors (2020)
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Computational identification of structural factors affecting the mutagenic potential of aromatic amines: study design and experimental validation (2018)
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Why are most phospholipidosis inducers also hERG blockers? (2017)
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3D-SDAR modeling of hERG potassium channel affinity: A case study in model design and toxicophore identification (2017)
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Feature selection from mass spectra of bacteria for serotyping Salmonella (2016)
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Rigorous 3-dimensional spectral data activity relationship approach modeling strategy for ToxCast estrogen receptor data classification, validation, and feature extraction (2016)
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Partial least square and k-nearest neighbor algorithms for improved 3D quantitative spectral data–activity relationship consensus modeling of acute toxicity (2014)
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Computational identification of a phospholipidosis toxicophore using 13C and 15N NMR-distance based fingerprints (2014)
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Identification of a metabolic biomarker panel in rats for prediction of acute and idiosyncratic hepatotoxicity (2014)
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Comprehensive analysis of alterations in lipid and bile acid metabolism by carbon tetrachloride using integrated transcriptomics and metabolomics (2014)
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Complementary PLS and KNN algorithms for improved 3D-QSDAR consensus modeling of AhR binding (2013)
Collaboration Network
Top Collaborators
- Computational identification of a phospholipidosis toxicophore using 13C and 15N NMR-distance based fingerprints
- Why are most phospholipidosis inducers also hERG blockers?
- Identification of a metabolic biomarker panel in rats for prediction of acute and idiosyncratic hepatotoxicity
- Partial least square and k-nearest neighbor algorithms for improved 3D quantitative spectral data–activity relationship consensus modeling of acute toxicity
- Complementary PLS and KNN algorithms for improved 3D-QSDAR consensus modeling of AhR binding
Showing 5 of 14 shared publications
- Computational identification of a phospholipidosis toxicophore using 13C and 15N NMR-distance based fingerprints
- Partial least square and k-nearest neighbor algorithms for improved 3D quantitative spectral data–activity relationship consensus modeling of acute toxicity
- Complementary PLS and KNN algorithms for improved 3D-QSDAR consensus modeling of AhR binding
- 3D-SDAR modeling of hERG potassium channel affinity: A case study in model design and toxicophore identification
- 13C NMR–Distance Matrix Descriptors: Optimal Abstract 3D Space Granularity for Predicting Estrogen Binding
Showing 5 of 6 shared publications
- Computational identification of a phospholipidosis toxicophore using 13C and 15N NMR-distance based fingerprints
- Partial least square and k-nearest neighbor algorithms for improved 3D quantitative spectral data–activity relationship consensus modeling of acute toxicity
- Complementary PLS and KNN algorithms for improved 3D-QSDAR consensus modeling of AhR binding
- 3D-SDAR modeling of hERG potassium channel affinity: A case study in model design and toxicophore identification
- 13C NMR–Distance Matrix Descriptors: Optimal Abstract 3D Space Granularity for Predicting Estrogen Binding
Showing 5 of 6 shared publications
- Why are most phospholipidosis inducers also hERG blockers?
- Partial least square and k-nearest neighbor algorithms for improved 3D quantitative spectral data–activity relationship consensus modeling of acute toxicity
- 3D-SDAR modeling of hERG potassium channel affinity: A case study in model design and toxicophore identification
- Computational identification of structural factors affecting the mutagenic potential of aromatic amines: study design and experimental validation
- Identification of a metabolic biomarker panel in rats for prediction of acute and idiosyncratic hepatotoxicity
- Comprehensive analysis of alterations in lipid and bile acid metabolism by carbon tetrachloride using integrated transcriptomics and metabolomics
- 13C NMR–Distance Matrix Descriptors: Optimal Abstract 3D Space Granularity for Predicting Estrogen Binding
- Partial least square and k-nearest neighbor algorithms for improved 3D quantitative spectral data–activity relationship consensus modeling of acute toxicity
- Complementary PLS and KNN algorithms for improved 3D-QSDAR consensus modeling of AhR binding
- 13C NMR–Distance Matrix Descriptors: Optimal Abstract 3D Space Granularity for Predicting Estrogen Binding
- Identification of a metabolic biomarker panel in rats for prediction of acute and idiosyncratic hepatotoxicity
- Comprehensive analysis of alterations in lipid and bile acid metabolism by carbon tetrachloride using integrated transcriptomics and metabolomics
- Identification of a metabolic biomarker panel in rats for prediction of acute and idiosyncratic hepatotoxicity
- Comprehensive analysis of alterations in lipid and bile acid metabolism by carbon tetrachloride using integrated transcriptomics and metabolomics
- Identification of a metabolic biomarker panel in rats for prediction of acute and idiosyncratic hepatotoxicity
- Comprehensive analysis of alterations in lipid and bile acid metabolism by carbon tetrachloride using integrated transcriptomics and metabolomics
- Identification of a metabolic biomarker panel in rats for prediction of acute and idiosyncratic hepatotoxicity
- Comprehensive analysis of alterations in lipid and bile acid metabolism by carbon tetrachloride using integrated transcriptomics and metabolomics
- Identification of a metabolic biomarker panel in rats for prediction of acute and idiosyncratic hepatotoxicity
- Comprehensive analysis of alterations in lipid and bile acid metabolism by carbon tetrachloride using integrated transcriptomics and metabolomics
- Why are most phospholipidosis inducers also hERG blockers?
- Prediction and Experimental Evaluation of the hERG Blocking Potential of Drugs Showing Clinical Signs of Cardiotoxicity
- Computational identification of structural factors affecting the mutagenic potential of aromatic amines: study design and experimental validation
- Determination of structural factors affecting binding to mu, kappa and delta opioid receptors
- Mold2Molecular Descriptors for QSAR
- Mold2Molecular Descriptors for QSAR
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