Research Synopsis

• Development of new methodologies and software tools for computer-assisted drug design
• Development of new approach to protein 3D structure analysis and prediction based on the principles of statistical geometry

Details of research at the Laboratory for Molecular Modeling

Profile

Alex Tropsha is an expert in the fields of computational chemistry, cheminformatics, and structural bioinformatics who works to develop new methodologies and software tools for computer-assisted drug design. He is creating new approaches to protein 3D structure analysis and prediction based on the principles of statistical geometry. His particular expertise lies in the field of cheminformatics, a discipline where information and informatics methodologies are applied to storing, managing, exploring, and exploiting chemical databases. In layman’s terms, cheminformatics combines chemistry and computer science to aid in the discovery of new drugs.

Tropsha has authored more than 125 peer-reviewed papers and twenty books and book chapters. He joined the School’s faculty in 1991 as an assistant professor and director of the Laboratory for Molecular Modeling. He was promoted to associate professor in 1997 and to full professor in 2004 and holds appointments as an adjunct professor in the UNC Department of Biomedical Engineering and in the Department of Computer Science and is a member of the UNC Lineberger Comprehensive Cancer Center. He was named as the  K. H. Lee Distinguished Professor in 2008.

As associate dean for research, Tropsha serves as the School’s chief research officer. He creates strategies to increase support for the research enterprise, oversees the School’s research centers, and works to cultivate and expand partnerships with entities within the University and with pharmaceutical and biotechnology companies.

Collaborators

Ivan Rusyn, Fred Wright, Bryan Roth

Most Recent Publications

1. Do crystal structures obviate the need for theoretical models of GPCRs for structure based virtual screening. Tang H, Wang XS, Hsieh JH, Tropsha A. Proteins. 2012 Jan 17. doi: 10.1002/prot.24035. [Epub ahead of print] PMID: 22275072 [PubMed - as supplied by publisher]

2. Quantitative high-throughput screening for chemical toxicity in a population-based in vitro model. Lock EF, Abdo N, Huang R, Xia M, Kosyk O, O'Shea SH, Zhou YH, Sedykh A, Tropsha A, Austin CP, Tice RR, Wright FA, Rusyn I. Toxicol Sci. 2012 Jan 19. [Epub ahead of print] PMID: 22268004 [PubMed - as supplied by publisher]

3. Discrete molecular dynamics distinguishes nativelike binding poses from decoys in difficult targets. Proctor EA, Yin S, Tropsha A, Dokholyan NV. Biophys J. 2012 Jan 4;102(1):144-51. Epub 2012 Jan 3. PMID: 22225808 [PubMed - in process]

4. Quantitative structure - property relationship modeling of remote liposome loading of drugs. Cern A, Golbraikh A, Sedykh A, Tropsha A, Barenholz Y, Goldblum A. J Control Release. 2011 Dec 1. [Epub ahead of print] PMID: 22154932 [PubMed - as supplied by publisher]

5. LOCAL KERNEL CANONICAL CORRELATION ANALYSIS WITH APPLICATION TO VIRTUAL DRUG SCREENING. Samarov D, Marron JS, Liu Y, Grulke C, Tropsha A. Ann Appl Stat. 2011 Sep 1;5(3):2169-2196. PMID: 22121408 [PubMed]

6. Cheminformatics meets molecular mechanics: a combined application of knowledge-based pose scoring and physical force field-based hit scoring functions improves the accuracy of structure-based virtual screening. Hsieh JH, Yin S, Wang XS, Liu S, Dokholyan NV, Tropsha A. J Chem Inf Model. 2012 Jan 23;52(1):16-28. Epub 2011 Dec 14. PMID: 22017385 [PubMed - in process]

7. Community-wide assessment of protein-interface modeling suggests improvements to design methodology. Fleishman SJ, Whitehead TA, Strauch EM, Corn JE, Qin S, Zhou HX, Mitchell JC, Demerdash ON, Takeda-Shitaka M, Terashi G, Moal IH, Li X, Bates PA, Zacharias M, Park H, Ko JS, Lee H, Seok C, Bourquard T, Bernauer J, Poupon A, Azé J, Soner S, Ovali SK, Ozbek P, Tal NB, Haliloglu T, Hwang H, Vreven T, Pierce BG, Weng Z, Pérez-Cano L, Pons C, Fernández-Recio J, Jiang F, Yang F, Gong X, Cao L, Xu X, Liu B, Wang P, Li C, Wang C, Robert CH, Guharoy M, Liu S, Huang Y, Li L, Guo D, Chen Y, Xiao Y, London N, Itzhaki Z, Schueler-Furman O, Inbar Y, Potapov V, Cohen M, Schreiber G, Tsuchiya Y, Kanamori E, Standley DM, Nakamura H, Kinoshita K, Driggers CM, Hall RG, Morgan JL, Hsu VL, Zhan J, Yang Y, Zhou Y, Kastritis PL, Bonvin AM, Zhang W, Camacho CJ, Kilambi KP, Sircar A, Gray JJ, Ohue M, Uchikoga N, Matsuzaki Y, Ishida T, Akiyama Y, Khashan R, Bush S, Fouches D, Tropsha A, Esquivel-Rodríguez J, Kihara D, Stranges PB, Jacak R, Kuhlman B, Huang SY, Zou X, Wodak SJ, Janin J, Baker D. J Mol Biol. 2011 Nov 25;414(2):289-302. Epub 2011 Sep 29. PMID: 22001016 [PubMed - indexed for MEDLINE]

8. Combined Application of Cheminformatics- and Physical Force Field-Based Scoring Functions Improves Binding Affinity Prediction for CSAR Data Sets. Hsieh JH, Yin S, Liu S, Sedykh A, Dokholyan NV, Tropsha A. J Chem Inf Model. 2011 Aug 30. [Epub ahead of print] PMID: 21780807 [PubMed - as supplied by publisher]

9. Predicting drug-induced hepatotoxicity using QSAR and toxicogenomics approaches. Low Y, Uehara T, Minowa Y, Yamada H, Ohno Y, Urushidani T, Sedykh A, Muratov E, Kuz'min V, Fourches D, Zhu H, Rusyn I, Tropsha A. Chem Res Toxicol. 2011 Aug 15;24(8):1251-62. Epub 2011 Jul 21. PMID: 21699217 [PubMed - in process]

10. Exploring quantitative nanostructure-activity relationships (QNAR) modeling as a tool for predicting biological effects of manufactured nanoparticles. Fourches D, Pu D, Tropsha A. Comb Chem High Throughput Screen. 2011 Mar 1;14(3):217-25. PMID: 21275889 [PubMed - in process]