Alex Tropsha

The major focus of the Tropsha group is biomolecular informatics, which aims to understand relationships between chemical structures and their functional properties. This requires the development of formal quantitative descriptors for chemical or macromolecular structures, and extensive use of statistical, datamining, and computational techniques with large chemical or biological databases. In this context, the Tropsha group has developed several important methodologies and software tools for computer-assisted drug design (available here). These methodologies employ principles of variable selection of descriptors most relevant to the biological action of drug molecules and afford predictive models of drug action that aid in the design of novel or improved pharmaceuticals. Ongoing studies include the development of new methods for data analysis, and the prediction and integration of these computational tools with experimental research. The Tropsha group has also developed a new approach to 3D protein-structure analysis and prediction based on the principles of statistical geometry (Delaunay tessellation). This approach enhances their ability to determine structural and sequence motifs responsible for protein function. These motifs can be used to annotate structural and functional classes of genomic sequences. The group’s ongoing studies include the systematic application of statistical geometry and statistical pattern-matching techniques for comparing and classifying known 3D protein structures, as well as structure/function prediction using high-performance computational resources. Some of these methodologies can be found on the Protein Structure Workbench.

Selected Publications:
Zhang S, Wei L, Bastow K, Zheng W, Brossi A, Lee KH, Tropsha A. (2007) Antitumor Agents 252. Application of validated QSAR models to database mining: discovery of novel tylophorine derivatives as potential anticancer agents. J Comput Aided Mol Des. 21(1-3):97-112.

Votano JR, Parham M, Hall LM, Hall LH, Kier LB, Oloff S, Tropsha A.(2006) QSAR modeling of human serum protein binding with several modeling techniques utilizing structure-information representation. J Med Chem. 49(24):7169-81.

Zhang S, Golbraikh A, Oloff S, Kohn H, Tropsha A. (2006) 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. 46(5):1984-95.

Bandyopadhyay D, Huan J, Liu J, Prins J, Snoeyink J, Wang W, Tropsha A. (2006) Structure-based function inference using protein family-specific fingerprints. Protein Sci. 15(6):1537-43.

Zhang S, Golbraikh A, Tropsha A. (2006) Development of quantitative structure-binding affinity relationship models based on novel geometrical chemical descriptors of the protein-ligand interfaces.
J Med Chem. 49(9):2713-24.

Huan J, Bandyopadhyay D, Wang W, Snoeyink J, Prins J, Tropsha A. (2005) Comparing graph representations of protein structure for mining family-specific residue-based packing motifs. J Comput Biol 12:657-71.

Ng C, Xiao Y, Putnam W, Lum B, Tropsha A. (2004) Quantitative structure-pharmacokinetic parameters relationships (QSPKR) analysis of antimicrobial agents in humans using simulated annealing k-nearest neighbor and partial least-square analysis methods. J Pharm Sci 93:2535-44.




					The major focus of the Tropsha group is biomolecular informatics, which aims to understand relationships between chemical structures and their functional properties. This requires the development of formal quantitative descriptors for chemical or macromolecular structures, and extensive use of statistical, datamining, and computational techniques with large chemical or biological databases. In this context, the Tropsha group has developed several important methodologies and software tools for computer-assisted drug design (available here). These methodologies employ principles of variable selection of descriptors most relevant to the biological action of drug molecules and afford predictive models of drug action that aid in the design of novel or improved pharmaceuticals. Ongoing studies include the development of new methods for data analysis, and the prediction and integration of these computational tools with experimental research. The Tropsha group has also developed a new approach to 3D protein-structure analysis and prediction based on the principles of statistical geometry (Delaunay tessellation). This approach enhances their ability to determine structural and sequence motifs responsible for protein function. These motifs can be used to annotate structural and functional classes of genomic sequences. The group’s ongoing studies include the systematic application of statistical geometry and statistical pattern-matching techniques for comparing and classifying known 3D protein structures, as well as structure/function prediction using high-performance computational resources. Some of these methodologies can be found on the Protein Structure Workbench. Selected Publications: Zhang S, Wei L, Bastow K, Zheng W, Brossi A, Lee KH, Tropsha A. (2007) Antitumor Agents 252. Application of validated QSAR models to database mining: discovery of novel tylophorine derivatives as potential anticancer agents. J Comput Aided Mol Des. 21(1-3):97-112. Votano JR, Parham M, Hall LM, Hall LH, Kier LB, Oloff S, Tropsha A.(2006) QSAR modeling of human serum protein binding with several modeling techniques utilizing structure-information representation. J Med Chem. 49(24):7169-81. Zhang S, Golbraikh A, Oloff S, Kohn H, Tropsha A. (2006) 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. 46(5):1984-95. Bandyopadhyay D, Huan J, Liu J, Prins J, Snoeyink J, Wang W, Tropsha A. (2006) Structure-based function inference using protein family-specific fingerprints. Protein Sci. 15(6):1537-43. Zhang S, Golbraikh A, Tropsha A. (2006) Development of quantitative structure-binding affinity relationship models based on novel geometrical chemical descriptors of the protein-ligand interfaces. J Med Chem. 49(9):2713-24. Huan J, Bandyopadhyay D, Wang W, Snoeyink J, Prins J, Tropsha A. (2005) Comparing graph representations of protein structure for mining family-specific residue-based packing motifs. J Comput Biol 12:657-71. Ng C, Xiao Y, Putnam W, Lum B, Tropsha A. (2004) Quantitative structure-pharmacokinetic parameters relationships (QSPKR) analysis of antimicrobial agents in humans using simulated annealing k-nearest neighbor and partial least-square analysis methods. J Pharm Sci 93:2535-44.



		contact information: [phone] (919) 966-2955 [email] [website]