1

SCIENTIFIC PUBLICATIONS

BOOK CHAPTERS

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.
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.

SCIENTIFIC JOURNALS

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.
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.
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.
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.
33.	Tropsha, A.; Golbraikh, A. Predictive QSAR modeling workflow, model applicability domains, and virtual screening. Curr Pharm Des. 2007, 13, 3494-3504. Review.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
21.	Golbraikh, A.; Tropsha, A. QSAR Modeling Using Chirality Descriptors Derived From MolecularTopology. J. Chem. Inf. Comput. Sci. 2002, 16, 357-369.
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.
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.
18.	Golbraikh, A.; Bonchev, D.; Tropsha, A. Novel ZE-Isomerism Descriptors Derived From Molecular Topology. J. Chem. Inf. Comput. Sci. 2002, 42, 769-787.
17.	Golbraikh, A. and Tropsha, A. Beware of q²! J. Mol. Graphics Mod. 2002, 20, 269-276..
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 13^th European Symposium on Quantitative Structure-Activity Relationships. 27 Aug – 1 Sept, 2000. Prous Science, 2001, 219-223.
15.	Golbraikh, A.; Bonchev, D.; Tropsha, A. Novel Chirality Descriptors Derived From Molecular Topology. J. Chem. Inf. Comput. Sci. 2001, 41, 147-158.
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.
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.
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.
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.
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.
9.	Balodis, J.; Golbraikh, A. Conformational analysis of cyclic Angiotensin II analogues. Letters in Peptide Science 1996, 3, 195 -199.
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.
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.
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.
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.
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.
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.
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.
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.