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External Resources:

Dr. Ruben Abagyan

Structure Based Drug Discovery, Toxicology, Protein Modeling and Design

Ruben Abagyan

Ruben Abagyan, Ph.D.

Skaggs School of Pharmacy and Pharmaceutical Sciences


(858) 822-3404


Research Summary: Structure Based Drug Discovery, Toxicology, Modeling and Design

Dr. Abagyan’s research focuses on the development of novel technologies for structure based drug discovery and optimization, structural systems biology for target finding and protein modeling. We screen specific biomedical targets to discover new drug leads, and validate them experimentally. The applications include antibiotics, cancer, neuro-degeneration, viral and endocrine diseases. To extend the reach of docking we model alternative functional states and allosteric pockets of the kinases, GPCRs and Nuclear Receptors. We derived comprehensive sets of ligand pockets (the Pocketome) competing for ligands and metabolites in different organisms. These data are used for target identification and polypharmacology profiling. We dock drugs, leads and environmental chemicals to the ‘anti-target’ models to predict endocrine disruption and other adverse effects. 

Academic Achievements


S.c. laude M.S. in Molecular and Chemical Biophysics (1980) Moscow Inst. Physics & Technology; Ph.D. in Protein Structure Prediction (1984) Moscow State University. 

Awards and Honors:

Two CapCure awards for excellence in prostate cancer research (2000, 2002); Princess Diana award and medal, Sydney (2003); UCSD Faculty and Staff Excellence Award (2007). 

Leadership Experience:

Director of Computational Biology & IT at Skirball Inst. of Biomolecular Medicine, New York (1994-1999); Director at Novartis Institute, GNF (1999-2002); SBDD chair, MipTec, Basel, Switzerland (2002-2009); Founder of MolSoft (1994); Member of Board of Directors of Syrrx (2001-2002); SAB Member of Plexus Vaccines (2001- 2003); Editorial Boards (current): Endocrine Disruptors, Am.J.Pharm.Tox, Cancer Genomics and proteomics, IUPAC Glossary Committee, J.Comp- Aided Mol.Des.; Steering, Review or Advisory panel member for UCSD Bioinformatics Program, the Swiss NSF NCCR- Transcure Center, and Hong Kong PolyU State Key Laboratory and Steering, Review or Advisory panel member. 


  • Pharmaceutical Chemistry II, Physical Pharmacology (SPPS 222)

Key Contributions to Pharmaceutical Sciences

  • Internal Coordinate Mechanics (ICM) for structure prediction, dynamics and accurate ligand docking.
  • Stochastic global optimization method with collective (fragment) moves, square-root sampling.
  • Ligand Guided homology modeling. Lead discovery for kinases, nuclear receptors and GPCRs.
  • The Pocketome for target profiling and poly-pharmacology prediction

Selected Recent Publications (view more)

  • Abagyan et al. (1994). Biased probability Monte Carlo conformational searches. J Mol Biol 235:983-1002. 
  • Abagyan et al. (1994). ICM: A new method for protein modeling and design: Applications to docking. J Comp Chem 15:488-506. 
  • Cavasotto et al. (2003). Structure-based identification of binding sites, native ligands for G-protein coupled receptors. Proteins 51:423-433. 
  • Bisson et al. (2007). Discovery of antiandrogen activity of nonsteroidal scaffolds of marketed drugs. PNAS 104:11927-11932
  • Kufareva et al. (2008). Type-II kinase inhibitor docking, screening, and profiling using modified structures of active kinase states. J Med Chem 51:7921-7932. 
  • Bottegoni et al. (2009). Four-dimensional docking: a fast and accurate account of discrete receptor flexibility in ligand docking. J Med Chem 52:397-406. 
  • Kufareva et al. (2012) Pocketome: an encyclopedia of small-molecule binding sites in 4D. Nucleic Acids Res (Database Issue) D535:40. 
  • Chen YC, Totrov M, Abagyan R (2014) Docking to multiple pockets or ligand fields for screening, activity prediction and scaffold hopping. Future Med Chem, 6, 1741-55

Potential Collaborative Programs with the Pharmaceutical Industry

  • Modeling by homology and docking for drug targets including kinases, NRs and GPCRs

  • Virtual ligand screening and structure-focused libraries; target identification via Pocketome

  • Predicting polypharmacology and adverse effects of drugs and environmental chemicals