Ajay N. Jain, PhD

A B C D E F G H I J K L M N O P Q R S T U V W X Y Z
Ajay N. Jain, PhD

Professor, Departments of Biopharmaceutical Sciences and Laboratory Medicine, UCSF

ajain@jainlab.org

Phone: (415) 502-7242 (voice)
Box 0128, UCSF
San Francisco, CA 94143-0128

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Cancer Center Membership

Program Member » Developmental Therapeutics

Research Summary

My research and training efforts are geared toward applications of computation to drug discovery, with an emphasis on making use of multiple methods and data types, exploiting synergy between protein structural data, chemical/target annotation information, and ligand binding affinity data. My laboratory has a particular focus on cancer-related therapeutic discovery, as are part of many collaborators that make use of our computational methods.
I have been at the forefront of computer-aided drug discovery (CADD) since the early 1990’s, beginning in biotechnology start-ups such as Arris Pharmaceutical and continuing for the past 16 years at UCSF. The central areas of research in the lab are: (1) methods for docking small molecules to proteins using empirically derived scoring functions; (2) methods for inducing the shape of a protein binding pocket given the structures and affinities of ligands that bind the pocket competitively; (3) generalized surface-based approaches to computing molecular similarity, both among small molecules and among proteins; and (4) approaches for modeling and prediction of polypharmacology based on molecular structure. All of the approaches share their roots in the use of sophisticated computational algorithms involving object representation, function optimization, and search. My formal training at the PhD-level in Computer Science has been a distinct advantage in this work.
The following highlight recent work that is particularly relevant to the themes within the lab: synergy of diverse methods and data types for drug discovery. (1) We made use of a fast generalization of our ligand-based similarity approach (looking at small molecule surfaces from the outside) to instead quantify the similarity of protein binding sites (by looking at binding cavities from the inside). This approach (called PSIM) was shown to support characterization of proteins of unknown function. Using PSIM combined with other data, we developed hybrid approaches for (2) ligand affinity prediction, (3) prediction of polypharmacology, and (4) bioactive small molecule pose prediction.

Education

University of Minnesota, St. Paul, MN, BS, 1986, Biochemistry
University of Minnesota, Minneapolis, MN, BS, 1986, Computer Science
Carnegie Mellon University, Pittsburgh, PA, MS, 1989, Computer Science
Carnegie Mellon University, Pittsburgh, PA, PhD, 1991, Computer Science


Professional Experience

  • 1983-1986
    Research Engineer, Machine Vision Technology, Honeywell, Minneapolis, MN
  • 1992
    Principal Research Scientist, Multi-Sensor Signal Processing. Alliant Techsystems (formerly Honeywell), Minneapolis, MN
  • 1992-1996
    Senior Scientist and Group Leader of Computational Sciences, Arris Pharmaceutical, So. San Francisco, CA
  • 1996-1997
    Principal Scientist and Group Leader of Computational Sciences, MetaXen LLC, Hayward, CA
  • 1997-present
    Founder and Chief Scientist, BioPharmics, San Mateo, CA
  • 1998-1999
    Director of Applied Computing, Iconix Pharmaceuticals, Mountain View, CA
  • 1999-present
    Full Member, UCSF Cancer Center, University of California, San Francisco, CA
  • 1999-2002
    Director of Informatics, UCSF Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, CA
  • 1999-2002
    Associate Adjunct Professor, Cancer Research Institute and Department of Laboratory Medicine, University of California, San Francisco, CA
  • 2002-present
    Director, Informatics Core, UCSF Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, CA
  • 2002-2007
    Associate Professor, Cancer Research Institute and Department of Laboratory Medicine, University of California, San Francisco, CA
  • 2004-2007
    Associate Professor, Department of Biopharmaceutical Sciences, University of California, San Francisco, CA
  • 2007-present
    Professor, Cancer Research Institute, Department of Biopharmaceutical Sciences, and Department of Laboratory Medicine, University of California, San Francisco, CA

Honors & Awards

  • 2010
    Levinthal Lecturer, Eleventh International CUP Symposium in Molecular Modeling

Selected Publications

  1. Knowledge-guided docking: accurate prospective prediction of bound configurations of novel ligands using Surflex-Dock. J Comput Aided Mol Des. 2015 Jun; 29(6):485-509.
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  2. Chemical and protein structural basis for biological crosstalk between PPARa and COX enzymes. J Comput Aided Mol Des. 2015 Feb; 29(2):101-12.
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  3. Prediction of off-target drug effects through data fusion. Pac Symp Biocomput. 2014; 160-71.
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  4. Protein function annotation by local binding site surface similarity. Proteins. 2014 Apr; 82(4):679-94.
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  5. A structure-guided approach for protein pocket modeling and affinity prediction. J Comput Aided Mol Des. 2013 Nov; 27(11):917-34.
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  6. Tumour antigen targeted monoclonal antibodies incorporating a novel multimerisation domain significantly enhance antibody dependent cellular cytotoxicity against colon cancer. Eur J Cancer. 2013 Oct; 49(15):3344-52.
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  7. Iterative refinement of a binding pocket model: active computational steering of lead optimization. J Med Chem. 2012 Oct 25; 55(20):8926-42.
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  8. Surflex-Dock: Docking benchmarks and real-world application. J Comput Aided Mol Des. 2012 Jun; 26(6):687-99.
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  9. Successful recanalization of chronic total occlusions is associated with improved long-term survival. JACC Cardiovasc Interv. 2012 Apr; 5(4):380-8.
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  10. Does your model weigh the same as a duck? J Comput Aided Mol Des. 2012 Jan; 26(1):57-67.
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  11. Percutaneous balloon pericardiotomy for recurrent malignant pericardial effusion. J Thorac Oncol. 2011 Dec; 6(12):2138-9.
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  12. Chemical structural novelty: on-targets and off-targets. J Med Chem. 2011 Oct 13; 54(19):6771-85.
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  13. Surface-based protein binding pocket similarity. Proteins. 2011 Sep; 79(9):2746-63.
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  14. QMOD: physically meaningful QSAR. J Comput Aided Mol Des. 2010 Oct; 24(10):865-78.
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  15. Molecular shape and medicinal chemistry: a perspective. J Med Chem. 2010 May 27; 53(10):3862-86.
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  16. Physical binding pocket induction for affinity prediction. J Med Chem. 2009 Oct 8; 52(19):6107-25.
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  17. Effects of protein conformation in docking: improved pose prediction through protein pocket adaptation. J Comput Aided Mol Des. 2009 Jun; 23(6):355-74.
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  18. Accurate and interpretable computational modeling of chemical mutagenicity. J Chem Inf Model. 2008 Sep; 48(9):1833-9.
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  19. Recommendations for evaluation of computational methods. J Comput Aided Mol Des. 2008 Mar-Apr; 22(3-4):133-9.
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  20. Customizing scoring functions for docking. J Comput Aided Mol Des. 2008 May; 22(5):269-86.
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