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Alexander Golbraikh

Associate Professor

Alexander Golbraikh, Ph.D.

Associate Professor, Division of Chemical Biology and Medicinal Chemistry


alexander_golbraikh

PHONE
(919) 966-3459
EMAIL
golbraik@email.unc.edu
ADDRESS
301 Pharmacy Lane, Beard Hall, CB# 7360, Chapel Hill, NC, 27599-7568
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Research

My areas of scientific interests include Cheminformatics, Computational Toxicology, Computer-Aided Drug Discovery and Design, and Molecular Modeling. I am a Research Associate Professor in the laboratory for Molecular Modeling directed by Prof. Alex Tropsha in the UNC Eshelman School of Pharmacy at Chapel Hill, NC. My primary role is to advance these areas of research through applied mathematics and information technology. I have a long career working at the intersection of math, statistics, chemistry, biology, and computer science. Working in the field of Quantitative Structure-Activity Relationship (QSAR) analysis, I was a key developer in the concept of dataset modelability, I have proposed several types of descriptors which account for atomic chirality and ZE-isomerism, and I have established a set of critical validation procedures of QSAR models. Currently I’m writing a book for researchers working in the field that will include different cheminformatics topics, many of which have never been discussed in the literature. My near future project is the development of a new QSAR approach which would predict experimental activity intervals rather than single activity values normally used in traditional QSAR analysis. The approach will be based on multiple experimental activity measurements for the same compounds. The approaches I developed have been used in QSAR and Toxicity studies within the laboratory for Molecular Modeling and worldwide. The scope of my scientific interests includes development of algorithms and software, multivariate data analysis and mathematical statistics, and Information and Communication Technologies in general. I am an author of 48 journal publications and six book chapters in the peer-reviewed scientific literature.

Publications

  1.  Tropsha, A.; Golbraikh, A. Structure–Activity Relationships Modeling: Data Preparation and the General Modeling Workflow. In: Handbook of Chemoinformatics Algorithms (Faulon, J.-L.; Bender, A., Eds.), Chapter 6, pp. 175-212, Chapman & Hall / CRC, London, UK, 2010.
  2. Tropsha, A.; Golbraikh, A. Predictive Quantitative Structure–Activity Relationships Modeling: Development and Validation of QSAR Models. In: Handbook of Chemoinformatics Algorithms (Faulon, J.-L.; Bender, A., Eds.), Chapter 7, pp. 213-233, Chapman & Hall / CRC, London, UK, 2010.
  3. Golbraikh, A.; Wang, X. S.; Zhu, H.; Tropsha, A. Predictive QSAR modeling: Methods and applications in drug discovery and chemical risk assessment. In: Handbook of Computational Chemistry, (Leszczynski, J., Ed.) Chapter 37, pp. 1311-1342, Springer-Verlag, Dordrecht, Heidelberg, London, New York, 2011.
  4. Golbraikh, A.; Fourches, D.; Sedykh, A.; Muratov, E.; Liepina, I.; Tropsha, A. Modelability Criteria: Statistical Characteristics Estimating Feasibility to Build Predictive QSAR Models for a Dataset. In: Practical Aspects of Computational Chemistry III (Leszcynski, J.; Shukla, M.K., Eds.) Chapter 7, pp. 187-230, Springer New York Heidelberg Dordrecht London, 2014.
  5. Golbraikh, A.; Wang, X.S.; Zhu, H.; Tropsha, A. Predictive QSAR modeling: Methods and applications in drug discovery and chemical risk assessment. In: Handbook of Computational Chemistry, (Leszczynski, J., Kaczmarek-Kedziera, A.; Puzyn, T.G.; Papadopoulos, M.; Reis, H.; Shukla, M.K., Eds.) Chapter 57, pp. 2303-2340, Springer-Verlag, Dordrecht, Heidelberg, London, New York, 2017.
  6. Golbraikh, A. & Tropsha, A. QSAR/QSPR Revisited. In: Chemoinformatics: Basic Concepts and Methods. (Engel, T. & Gasteiger, G. I Eds.) Chapter 12.  pp. 465-495, Wiley-VCH, Weinheim, Germany, 2018.

  1. Golbraikh, A.A.; Betins, J.; Balodis, J.; Zhuk, R.A.; Nikiforovich, G.V. Conformational aspects of antiviral activity of deoxyguanosine acyclic analogues. Nucl. Acids Res. 1989, 17, 7965-7977.
  2. Nikiforovich, G.V.; Balodis, J.; Shenderovich, M.D.; Golbraikh, A.A. Conformational features responsible for binding of cyclic analogues of enkephalin to opioid receptors. I. Low-energy peptide backbone conformers of analogues containing Phe4. Int. J. Peptide Prot. Res. 1990, 36, 67-78.
  3. Nikiforovich, G.V.; Golbraikh, A.A.; Shenderovich, M.D.; Balodis, J. Conformational features responsible for binding of cyclic analogues of enkephalin to opioid receptors. II. Models of m- and d-receptor-bound structures for analogues containing Phe4. Int. J. Peptide Prot. Res. 1990, 36, 209-218.
  4. Shenderovich, M.D.; Nikiforovich, G.V.; Golbraikh, A.A. Conformational features responsible for binding of cyclic analogues of enkephalin to opioid receptors. III. Probable binding conformations of m-agonists with phenylalanin in position 3. Int. J. Pepide Prot. Res. 1991, 37, 241-251.
  5. Tarnowska, M.; Liwo, A.; Shenderovich, M.D.; Liepina, I.; Golbraikh, A.A.; Grzonka, Z.; Tempczyk, A. A molecular mechanics study of the effect of substitution in position 1 on the conformational space of the oxytocin/vasopressin ring. J. Comp. Aid. Mol. Des. 1993, 7, 699-719.
  6. Matrosovich, M.N.; Gambaryan, A.S.; Tuzikov, A.B.; Byramova, N.E.; Mochalova, L.V.; Golbraikh, A.A.; Shenderovich, M.D.; Finne, J.; Bovin, N.V. Probing of the receptor-binding sites of the H1 and H3 Influenza-A and Influenza-B Virus Hemagglutinins by Synthetic and Natural Sialosides. Virology, 1993, 196, 111-121.
  7. Balodis, J.; Golbraikh, A.; Liepina, I. Conformational analysis of  cyclic moiety of potent  angiotensin analogue. In: Aminoacids –  Peptides – Proteins (Biological functions and medical applications). Drug discovery and design. Nov. 17 – 18, 1994, Patras, Greece. Proceedings- Biomed, 1994, p. 43 – 48.
  8. Balodis, J.; Golbraikh, A. Conformational analysis of series of cyclic Angiotensin II analogues. In: Aminoacids – Peptides – Proteins (Biological functions and medical applications). Drug discovery and design. Nov. 23 – 25, 1995, Patras, Greece. Proceedings- Biomed, 1996, p. 49 – 52.
  9. Balodis, J.; Golbraikh, A. Conformational analysis of cyclic Angiotensin II analogues. Letters in Peptide Science 1996, 3, 195 -199.
  10. Bernard, P.; Golbraikh, A.; Kireev, D.; Chrétien, J.R.; Rozhkova, N. Comparison of Chemical Databases: Analysis of Molecular Diversity with Self-Organising Maps. Analusis 1998 26, 333-341.
  11. Madre, M.; Panchenko, N.; Golbraikh, A.; Zhuk, R.; Pandit, U.K.; Geenevasen, J.; Koomen, G.-J. Purine Nucleoside Analogues. 11. Some Peculiarities of the Alkylation of 9-(2-Acetoxyethoxymethyl)-N2-Acetylguanine and its 7-substituted Regioisomer. Collect. Czech. Chem. Commun. 1999, 64, 685 – 695.
  12. Brandt, W.; Golbraikh, A.; Täger, M.; Lendeckel, U. A Molecular Mechanism of the Cleavage of a Disulfide Bond as the Primary Function of Agonist Binding to G-Protein Coupled Receptors Based on Theoretical Calculations Supported by Experiments. Eur. J. Biochem. 1999, 261, 89 – 97.
  13. Golbraikh, A.; Bernard, P.; Chretien, J.R. Validation of protein-based alignment in 3D quantitative structure-activity relationships with CoMFA models. Eur. J. Med. Chem. 2000, 35, 123 – 136.
  14. Golbraikh, A. Molecular Dataset Diversity Indices and Their Applications to QSAR Analysis and Comparison of Chemical Databases. J. Chem. Inf. Comput. Sci. 2000, 40, 414 – 425.
  15. Golbraikh, A.; Bonchev, D.; Tropsha, A. Novel Chirality Descriptors Derived From Molecular Topology. J. Chem. Inf. Comput. Sci. 2001, 41, 147-158.
  16. Golbraikh, A.; Bonchev, D.; Xiao, Y.-D.; Tropsha, A. Novel Chiral Topological Descriptors and Their Application to QSAR. In: Rational Approaches to Drug Design. Proceedings of the 13th European Symposium on Quantitative Structure-Activity Relationships. 27 Aug – 1 Sept, 2000. Prous Science, 2001, 219-223.
  17. Golbraikh, A. and Tropsha, A. Beware of q2J. Mol. Graphics Mod. 2002, 20, 269-276..
  18. Golbraikh, A.; Bonchev, D.; Tropsha, A. Novel ZE-Isomerism Descriptors Derived From Molecular Topology. J. Chem. Inf. Comput. Sci. 2002, 42, 769-787.
  19. Xiao, Z.; Xiao, Y.-D.; Feng, J.; Golbraikh, A.; Tropsha, A.; Lee, K.-H. Antitumor Agents. Modeling of Epipodophyllotoxin Derivatives using Variable Selection QSAR Approaches. J. Med. Chem. 2002, 45, 2294-2309.
  20. Shen, M.; LeTiran, A.; Xiao Y.; Golbraikh, A.; Kohn, H.; Tropsha, A. QSAR Analysis of Functionalized Amino Acid Anticonvulsant Agents Using k-Nearest Neighbor and Simulated Annealing-PLS Methods. J. Med. Chem. 2002, 45, 2811-2823.
  21. Golbraikh, A.; Tropsha, A. QSAR Modeling Using Chirality Descriptors Derived From MolecularTopology. J. Chem. Inf. Comput. Sci. 2002, 16, 357-369.
  22. Golbraikh, A.; Tropsha, A. Predictive QSAR Modeling Based on Diversity Sampling of Experimental Datasets for the Training and Test Set Selection. J. Comput.-Aided Mol. Des. 2002, 5-6, 357-369.
  23. Kovatcheva, A.; Buchbauer, G.; Golbraikh, A.; Wolschann, P. QSAR Modeling of a-Campholenic Derivatives with Sandalwood Odor. J. Chem. Inf. Comput. Sci. 2003, 43, 259-266.
  24. Golbraikh, A.; Shen, M.; Xiao, Z.; Xiao, Y.D.; Lee, K.H.; Tropsha A. Rational selection of training and test sets for the development of validated QSAR models. J Comput Aided Mol Des. 2003, 2-4, 241-253.
  25. Kovatcheva, A.; Golbraikh, A.; Oloff, S.; Xiao, Y.; Zheng, W.; Wolschann, P.; Buchbauer, G.; Tropsha, A. Combinatorial QSAR of Ambergris Fragrance Compounds. J Chem. Inf. Comput. Sci. 2004, 44, 582-95.
  26. Shen, M.; Beguin, C.; Golbraikh, A.; Stables, J.; Kohn, H.; Tropsha, A. Application of Predictive QSAR Models to Database Mining: Identification and Experimental Validation of Novel Anticonvulsant Compounds. J. Med. Chem. 2004, 47, 2356-2364.
  27. Kovatcheva, A.; Golbraikh, A.; Oloff, S.; Feng, J.; Zheng, W.; Tropsha, A. QSAR modeling of datasets with enantioselective compounds using chirality sensitive molecular descriptors. SAR QSAR Environ. Res. 2005, 16, 93-102.
  28. Medina-Franco, J.L.; Golbraikh, A.; Oloff, S.; Castillo, R.; Tropsha, A. Quantitative Structure-Activity Relationship Analysis of Pyridinone HIV-1 Reverse Transcriptase Inhibitors Using The k Nearest Neighbor Method and QSAR-Based Database Mining. J. Comput. Aided Mol. Des. 2005, 19, 229-242.
  29. Lima, P.C.; Golbraikh, A.; Oloff, S.; Xiao, Y.; Tropsha, A. Combinatorial QSAR modeling of P-Glycoprotein Substrates. J. Chem. Inf. Mod., 2006, 46, 1245-54.
  30. Zhang, S.; Golbraikh, A.; Tropsha, A. The Development of Quantitative Structure-Binding Affinity Relationship (QSBR) Models Based on Novel Geometrical Chemical Descriptors of the Protein-Ligand Interfaces. J. Med. Chem. 2006, 49, 2713-24.
  31. Ghoneim, O.M.; Legere, J.A.; Golbraikh, A.; Tropsha, A.; Booth, R.G. Novel ligands for the human histamine H1 receptor: synthesis, pharmacology, and comparative molecular field analysis studies of 2-dimethylamino-5-(6)-phenyl-1,2,3,4-tetrahydronaphthalenes. Bioorg Med Chem. 2006, 14, 6640-58.
  32. Zhang, S.; Golbraikh, A.; Oloff, S.; Kohn, H.; Tropsha, A. A novel automated lazy learning QSAR (ALL-QSAR) approach: method development, applications, and virtual screening of chemical databases using validated ALL-QSAR models. J Chem Inf Model. 2006, 46, 1984-1995.
  33. Tropsha, A.; Golbraikh, A. Predictive QSAR modeling workflow, model applicability domains, and virtual screening. Curr Pharm Des. 2007, 13, 3494-3504. Review.
  34. Hsieh, J.H.; Wang, X.S.; Teotico, D.; Golbraikh, A.; Tropsha, A. Differentiation of AmpC beta-lactamase binders vs. decoys using classification kNN QSAR modeling and application of the QSAR classifier to virtual screening. J. Comput. Aided Mol. Des. 2008, 22, 593-609.
  35. Wang, X.S.; Tang, H.; Golbraikh, A.; Tropsha A. Combinatorial QSAR Modeling of Specificity and Subtype Selectivity of Ligands Binding to Serotonin Receptors 5HT1E and 5HT1F. J. Chem. Inf. Model. 2008, 48, 997-1013.
  36. Zhang, L.; Zhu, H.; Oprea, T.I.; Golbraikh, A.; Tropsha, A. QSAR Modeling of the Blood-Brain Barrier Permeability for Diverse Organic Compounds. Pharm. Res. 2008, 25, 1902-1914.
  37. Zhu, H.; Ye, L.; Richard, A.; Golbraikh, A.; Wright, F.A.; Rusyn, I.; Tropsha, A. A Novel Two-step Hierarchical Quantitative Structure Activity Relationship Modeling Workflow for Predicting Acute Toxicity of Chemicals in Rodents. Environ. Health Perspect. 2009, 117, 1257-1264.
  38. Hajjo, R.; Grulke, C.M.; Golbraikh, A.; Setola, V.; Huang, X.P.; Roth, B.L.; Tropsha, A. Development, validation, and use of quantitative structure-activity relationship models of 5-hydroxytryptamine (2B) receptor ligands to identify novel receptor binders and putative valvulopathic compounds among common drugs. J. Med. Chem. 2010, 53, 7573-86.
  39. Tropsha, A.; Golbraikh, A.; Won-Jea, C. Development of kNN QSAR Models for 3-Arylisoquinoline Antitumor Agents, Bulletin of the Korean Chemical Society 2011, 32, 2397-2404.
  40. Bagheri, M,; Golbraikh, A. Rank-based ant system method for non-linear QSPR analysis: QSPR studies of the solubility parameter. SAR QSAR Environ. Res. 2012, 23, 59-86.
  41. Cern, A.; Golbraikh, A.; Sedykh, A.; Tropsha, A.; Barenholz, Y.; Goldblum, A. Quantitative structure-property relationship modeling of remote liposome loading of drugs. J. Control Release 2012, 160, 147-57.
  42. Bagheri, M.; Bagheri, M.; Gandomi, A.H.; Golbraikh, A. Simple yet accurate prediction method for sublimation enthalpies of organic contaminants using their molecular structure. Thermochimica Acta 2012, 543, 96-106.
  43. Martin, T.M.; Harten, P.; Young, D.M.; Muratov, E.N.; Golbraikh, A.; Zhu, H.; Tropsha, A. Does rational selection of training and test sets improve the outcome of QSAR modeling? J. Chem. Inf. Model. 2012, 52, 2570-2578.
  44. Zhang, L.; Fourches, D.; Sedykh, A.; Zhu, H.; Golbraikh, A.; Ekins, S.; Clark. J.; Connelly, M.C.; Sigal, M.; Hodges, D.; Guiguemde, A.; Guy, R.K.; Tropsha, A. Discovery of novel antimalarial compounds enabled by QSAR-based virtual screening. J. Chem. Inf. Model. 2013, 53, 475-492.
  45. Low, Y.; Sedykh, A.; Fourches, D.; Golbraikh, A.; Whelan, M.; Rusyn, I.; Tropsha, A. Integrative chemical-biological read-across approach for chemical hazard classification. Chem. Res. Toxicol. 2013, 26,1199-1208.
  46. Golbraikh, A.; Muratov, E.; Fourches, D.; Tropsha, A.  Data Set Modelability by QSAR. J. Chem. Inf. Model. 2014, 54,1-4.
  47. Luo, M.; Wang, X.S.; Roth, B.L.; Golbraikh, A.; Tropsha, A. Application of Quantitative Structure–Activity Relationship Models of 5-HT1A Receptor Binding to Virtual Screening Identifies Novel and Potent 5-HT1A Ligands. J. Chem. Inf. Model. 2014, 54, 634-647.
  48. Alves, V.M.; Golbraikh, A.; Capuzzi, S.J.; Liu, K.; Lam, W.I.; Korn, D.R.; Pozefsky, D.; Andrade, C.H.; Muratov, E.N.; Tropsha, A. Multi-Descriptor Read Across (MuDRA): A Simple and Transparent Approach for Developing Accurate Quantitative Structure-Activity Relationship Models. J Chem Inf Model. 2018, 58,1214-1223.