Research Chemist — Division of Systems Biology

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Svetoslav Slavov, Ph.D.
(870) 543-7121
NCTRResearch@fda.hhs.gov  

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About  |  Publications 

Background

Dr. Svetoslav Slavov holds a Ph.D. in molecular design from the University of Tartu, Estonia. He is a molecular modeling expert with over 20 years of experience in the fields of quantitative structure-activity relationships (QSAR) modeling, molecular modeling, computational toxicology and quantum chemistry. Dr. Slavov is an author of 42 research papers and a book chapter in the field of QSAR/quantitative structure-property relationship (QSPR) and drug design. His major contributions to the fields of chem- and bioinformatics include the development of new modeling approaches and validation techniques as well as the discovery of new algorithms for structural/mechanistic interpretation of QSAR models. Information on his research has been reported on National Public Radio, The New York Times, Fox News, ABC6, Medical News Today, and the Daily Telegraph. Within the Division of Systems Biology at FDA’s National Center for Toxicological Research (NCTR), his research is focused on the development of new computational toxicology approaches and models that can supplement the regulatory decisions and shorten the review process for new Investigational New Drug (IND) Applications by substituting the lengthy and expensive animal testing with reliable and accurate estimates of various safety endpoints. He has also worked on projects for biomarker and bacteria identification developing advanced algorithms for feature extraction and pattern identification. In 2015 and 2018 Dr. Slavov was awarded the FDA “Chief Scientist’s Publication Award for Data Methods/Analysis/Study Design.”

Research Interests

• Molecular Modeling
• QSAR/QSPR
• Drug Design
• Computational Toxicology
• Statistics
• Quantum Mechanics
• Quantum Field Theory

Selected Publications

Quantitative Structure-Toxicity Relationships in Translational Toxicology.
Slavov S. and Beger R.
Current Opinion in Toxicology. 2020, https://doi.org/10.1016/j.cotox.2020.04.002.

Computational Identification of Structural Factors Affecting the Mutagenic Potential of Aromatic Amines. Study Design and Experimental Validation.
Slavov S., Stoyanova-Slavova I., Mattes W., Beger R., and Brüschweiler B.
Archives of Toxicology. 2018, doi: 10.1007/s00204-018-2216-x.

Determination of Structural Factors Affecting Binding to Mu, Kappa and Delta Opioid Receptors.
Slavov S., Mattes W., and Beger R.D.
Archives of Toxicology. 2020, doi: 10.1007/s00204-020-02684-8.

Why are Most Phospholipidosis Inducers also hERG Blockers?
Slavov S., Stoyanova-Slavova I., Li S., Zhao J., Huang R., Xia M., and Beger R.
Archives of Toxicology. 2017, doi: 10.1007/s00204-017-1995-9.

3D-SDAR Modeling of hERG Potassium Channel Affinity: A Case Study in Model Design and Toxicophore Identification.
Stoyanova-Slavova I.B., Slavov S.H., Buzatu D.A., Beger R.D., and Wilkes J.G.
Journal of Molecular Graphics and Modelling. 2017, 72: 246.

Feature Selection from Mass Spectra of Bacteria for Serotyping Salmonella.
Slavov S., Alusta P., Buzatu D.A., and Wilkes J.G.
Journal of Analytical and Applied Pyrolysis. 2016.

Rigorous 3‐Dimensional Spectral Data Activity Relationship Approach Modeling Strategy for ToxCast Estrogen Receptor Data Classification, Validation, and Feature Extraction.
Slavov S.H. and Beger R.D.
Environmental Toxicology and Chemistry. 2016, 9999: 1.

Computational Identification of a Phospholipidosis Toxicophore Using 13C and 15N NMR-Distance Based Fingerprints.
Slavov S.H., Wilkes J.G., Buzatu D.A., Kruhlak N.L., Willard J.M., Hanig J.P., and Beger R.D.
Bioorganic & Medicinal Chemistry. 2014, 22: 6706.

Partial Least Square and K‐Nearest Neighbor Algorithms for Improved 3D Quantitative Spectral Data–Activity Relationship Consensus Modeling of Acute Toxicity.
Stoyanova‐Slavova I.B., Slavov S.H., Pearce B., Buzatu D.A., Beger R.D., and Wilkes J.G.
Environmental Toxicology and Chemistry. 2014, 33: 1271.

Identification of a Metabolic Biomarker Panel in Rats for Prediction of Acute and Idiosyncratic Hepatotoxicity.
Sun J., Slavov S., Schnackenberg L.K., Ando Y., Greenhaw J., Yang X., Salminen W., Mendrick D.L., and Beger R.
Computational and Structural Biotechnology Journal. 2014, 10: 78.

Complementary PLS and KNN Algorithms for Improved 3D-QSDAR Consensus Modeling of AhR Binding.
Slavov S.H., Pearce B.A., Buzatu D.A., Wilkes J.G., and Beger R.D.
Journal of Cheminformatics. 2013, 5: 47.

13C NMR-Distance Matrix Descriptors: Optimal Abstract 3D Space Granularity for Predicting Estrogen Binding.
Slavov S.H., Geesaman E.L., Pearce B.A., Schnackenberg L.K., Buzatu D.A., Wilkes J.G., and Beger R.D.
Journal of Chemical Information and Modeling. 2012, 52: 1854.

A Computational Study of the Binding of 3-(Arylidene) Anabaseines to Two Major Brain Nicotinic Acetylcholine Receptors and to the Acetylcholine Binding Protein.
Slavov S.H., Radzvilovits M., LeFrancois S., Stoyanova-Slavova I.B., Soti F., Kem W.R., and Katritzky A.R.
European Journal of Medicinal Chemistry. 2010, 45: 2433.

Novel Carboxamides as Potential Mosquito Repellents.
Katritzky A.R., Wang Z., Slavov S., Dobchev D.A., Hall C.D., Tsikolia M., Bernier U.R., Elejalde N.M., Clark G.G., and Linthicum K.J.
Journal of Medical Entomology. 2010, 47: 924.

Quantitative Correlation of Physical and Chemical Properties with Chemical Structure: Utility for Prediction.
Katritzky A.R., Kuanar M., Slavov S., Hall C.D., Karelson M., Kahn I., and Dobchev D.A.
Chemical Reviews. 2010, 110: 5714.