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    Ajay Jain, PhD

    SchoolUCSF School of Medicine
    DepartmentHDF Comprehensive Cancer Ctr
    Address1450 3rd Street
    San Francisco CA 94158
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      Collapse Overview 
      Collapse Overview
      Predictive computational modeling encompasses all of the work in the Jain Lab. This takes primary form in algorithmic approaches for drug discovery. The primary 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 proteins, 4) approaches for modeling and prediction of polypharmacology based on molecular structure, and 5) applications of such methods for cancer drug discovery. All of the approaches share their roots in the use of sophisticated computational algorithms involving object representation, function optimization, and search.

      Following a long period of applied research in defense applications and in speech understanding, Prof. Jain began a research career exclusively focused on issues in computational chemistry and computational biology. His foundational work in computer-aided drug design was done in industry, beginning with the Compass and Hammerhead techniques (see papers from 1994-1997). Compass involved a new representational scheme for capturing the 3D surface-properties of small molecules that made it possible to systematically address a previously unaddressed aspect in modeling the activity of small molecules: choice of the relative alignment and conformation (or pose) of competitive ligands including the detailed relationship of their hydrophobic shapes. A key insight, made with colleagues, was that the choice of pose should be directly governed by the function being used to predict binding affinity (essentially a direct analogy to physics where the lowest energy state is sought). The difficulty was that the function to predict activity was being induced at the same time as the pose choice. The Compass method overcame this problem, and was one of the foundational methods in establishing the field of multiple-instance learning, as it has come to be known within the Computer Science community. This work lead to the development of one of the first molecular docking programs described that addressed ligand conformational flexibility. The Hammerhead docking system built upon the molecular representations, multiple-instance approach, and search strategy developed for Compass.

      Advances in Molecular Docking and Ligand-Based Modeling
      Subsequent work built on the foundation laid by Compass and Hammerhead. These methods addressed problems in computation of molecular diversity and prediction of ADME properties (see papers from 1998-2000). Our most recent work in computational drug design (see the Surflex methodological papers from 2003 onward) is focused on pushing the frontiers of molecular docking and in constructing ligand-based models of protein active sites in cases where protein structure is unknown. The Surflex docking approach is unique, both with respect to scoring function and search methodology. Surflex-Dock is competitive with the best and most widely available methods in terms of docking accuracy and screening utility on publicly available benchmarks. We have recently made a substantial innovation to the multiple-instance parameter estimation process by generalizing our approach to now include negative training data. Putative inactive molecules have been added to a set of known active molecules in re-estimation of the scoring function for the Surflex docking method. We have continued our work in ligand-based modeling as well. The Surflex similarity method has been augmented, both in search strategy and in its objective function, to support the construction of ligand-based models of protein activity. The models are competitive with the best docking methods in terms of effectiveness in identifying novel ligands, generalizing remarkably well even across different chemical scaffolds. The Surflex QMOD approach takes QSAR to a new level, by transforming the problem into one of molecular docking. A protein binding site is induced given structure-activity data using the multiple-instance machine learning paradigm developed for Compass.

      Rational and Predictive Pharmacology
      Research within the lab has branched out to encompass larger biological scales, with studies that contemplate the multiple effects of small molecules in the whole organism. Our earliest work in drug discovery focused exclusively on the therapeutically desired target. At least as important are off-targets: those that are not intended to be modulated by a therapeutic but are affected at relevant drug concentrations. We are interested in building accurate predictive models for promiscuous bad-actor targets such as hERG and cytochrome-p450 enzymes. More broadly, we are interested in building models for multitudes of human targets in order to help guide the design and selection of compounds during preclinical research. This is challenging, both in terms of the stringency on model accuracy and also in terms of information curation regarding the multiple effects of existing therapeutics and those that have undergone clinical testing.

      Wet Applications
      The laboratory is purely a dry one. We rely upon our collaborators to test predictions made by our computational tools. In addition to the hundreds of laboratories that make use of our software, we have active collaborations with both academic and industrial partners. We are particularly interested in applications involving cancer.

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      Collapse Bibliographic 
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      Publications listed below are automatically derived from MEDLINE/PubMed and other sources, which might result in incorrect or missing publications. Researchers can login to make corrections and additions, or contact us for help.
      List All   |   Timeline
      1. Cleves AE, Jain AN. 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.
        View in: PubMed
      2. Cleves AE, Jain AN. Chemical and protein structural basis for biological crosstalk between PPARa and COX enzymes. J Comput Aided Mol Des. 2015 Feb; 29(2):101-12.
        View in: PubMed
      3. Yera ER, Cleves AE, Jain AN. Prediction of off-target drug effects through data fusion. Pac Symp Biocomput. 2014; 160-71.
        View in: PubMed PMC3897331
      4. Spitzer R, Cleves AE, Varela R, Jain AN. Protein function annotation by local binding site surface similarity. Proteins. 2014 Apr; 82(4):679-94.
        View in: PubMed PMC3949165
      5. Varela R, Cleves AE, Spitzer R, Jain AN. A structure-guided approach for protein pocket modeling and affinity prediction. J Comput Aided Mol Des. 2013 Nov; 27(11):917-34.
        View in: PubMed PMC3851759
      6. Jain A, Poonia B, So EC, Vyzasatya R, Burch EE, Olsen HS, Mérigeon EY, Block DS, Zhang X, Schulze DH, Hanna NN, Twadell WS, Yfantis HG, Chan SL, Cai L, Strome SE. 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.
        View in: PubMed
      7. Varela R, Walters WP, Goldman BB, Jain AN. Iterative refinement of a binding pocket model: active computational steering of lead optimization. J Med Chem. 2012 Oct 25; 55(20):8926-42.
        View in: PubMed PMC3640415
      8. Spitzer R, Jain AN. Surflex-Dock: Docking benchmarks and real-world application. J Comput Aided Mol Des. 2012 Jun; 26(6):687-99.
        View in: PubMed PMC3398190
      9. Jones DA, Weerackody R, Rathod K, Behar J, Gallagher S, Knight CJ, Kapur A, Jain AK, Rothman MT, Thompson CA, Mathur A, Wragg A, Smith EJ. Successful recanalization of chronic total occlusions is associated with improved long-term survival. JACC Cardiovasc Interv. 2012 Apr; 5(4):380-8.
        View in: PubMed
      10. Jain AN, Cleves AE. Does your model weigh the same as a duck? J Comput Aided Mol Des. 2012 Jan; 26(1):57-67.
        View in: PubMed PMC3276372
      11. Jones DA, Jain AK. Percutaneous balloon pericardiotomy for recurrent malignant pericardial effusion. J Thorac Oncol. 2011 Dec; 6(12):2138-9.
        View in: PubMed
      12. Yera ER, Cleves AE, Jain AN. Chemical structural novelty: on-targets and off-targets. J Med Chem. 2011 Oct 13; 54(19):6771-85.
        View in: PubMed PMC3188662
      13. Spitzer R, Cleves AE, Jain AN. Surface-based protein binding pocket similarity. Proteins. 2011 Sep; 79(9):2746-63.
        View in: PubMed PMC3155014
      14. Jain AN. QMOD: physically meaningful QSAR. J Comput Aided Mol Des. 2010 Oct; 24(10):865-78.
        View in: PubMed PMC3109424
      15. Nicholls A, McGaughey GB, Sheridan RP, Good AC, Warren G, Mathieu M, Muchmore SW, Brown SP, Grant JA, Haigh JA, Nevins N, Jain AN, Kelley B. Molecular shape and medicinal chemistry: a perspective. J Med Chem. 2010 May 27; 53(10):3862-86.
        View in: PubMed PMC2874267
      16. Langham JJ, Cleves AE, Spitzer R, Kirshner D, Jain AN. Physical binding pocket induction for affinity prediction. J Med Chem. 2009 Oct 8; 52(19):6107-25.
        View in: PubMed PMC2766559
      17. Jain AN. Effects of protein conformation in docking: improved pose prediction through protein pocket adaptation. J Comput Aided Mol Des. 2009 Jun; 23(6):355-74.
        View in: PubMed PMC2693357
      18. Langham JJ, Jain AN. Accurate and interpretable computational modeling of chemical mutagenicity. J Chem Inf Model. 2008 Sep; 48(9):1833-9.
        View in: PubMed PMC2753474
      19. Jain AN, Nicholls A. Recommendations for evaluation of computational methods. J Comput Aided Mol Des. 2008 Mar-Apr; 22(3-4):133-9.
        View in: PubMed PMC2311385
      20. Pham TA, Jain AN. Customizing scoring functions for docking. J Comput Aided Mol Des. 2008 May; 22(5):269-86.
        View in: PubMed PMC3108487
      21. Jain AN. Bias, reporting, and sharing: computational evaluations of docking methods. J Comput Aided Mol Des. 2008 Mar-Apr; 22(3-4):201-12.
        View in: PubMed
      22. Cleves AE, Jain AN. Effects of inductive bias on computational evaluations of ligand-based modeling and on drug discovery. J Comput Aided Mol Des. 2008 Mar-Apr; 22(3-4):147-59.
        View in: PubMed
      23. Jain AN. Surflex-Dock 2.1: robust performance from ligand energetic modeling, ring flexibility, and knowledge-based search. J Comput Aided Mol Des. 2007 May; 21(5):281-306.
        View in: PubMed
      24. Kingsley CB, Kuo WL, Polikoff D, Berchuck A, Gray JW, Jain AN. Magellan: a web based system for the integrated analysis of heterogeneous biological data and annotations; application to DNA copy number and expression data in ovarian cancer. Cancer Inform. 2006; 2:10-21.
        View in: PubMed PMC2675496
      25. Chin K, DeVries S, Fridlyand J, Spellman PT, Roydasgupta R, Kuo WL, Lapuk A, Neve RM, Qian Z, Ryder T, Chen F, Feiler H, Tokuyasu T, Kingsley C, Dairkee S, Meng Z, Chew K, Pinkel D, Jain A, Ljung BM, Esserman L, Albertson DG, Waldman FM, Gray JW. Genomic and transcriptional aberrations linked to breast cancer pathophysiologies. Cancer Cell. 2006 Dec; 10(6):529-41.
        View in: PubMed
      26. Pham TA, Jain AN. Parameter estimation for scoring protein-ligand interactions using negative training data. J Med Chem. 2006 Oct 5; 49(20):5856-68.
        View in: PubMed
      27. Jain AN. Scoring functions for protein-ligand docking. Curr Protein Pept Sci. 2006 Oct; 7(5):407-20.
        View in: PubMed
      28. Cleves AE, Jain AN. Robust ligand-based modeling of the biological targets of known drugs. J Med Chem. 2006 May 18; 49(10):2921-38.
        View in: PubMed
      29. Fridlyand J, Snijders AM, Ylstra B, Li H, Olshen A, Segraves R, Dairkee S, Tokuyasu T, Ljung BM, Jain AN, McLennan J, Ziegler J, Chin K, Devries S, Feiler H, Gray JW, Waldman F, Pinkel D, Albertson DG. Breast tumor copy number aberration phenotypes and genomic instability. BMC Cancer. 2006; 6:96.
        View in: PubMed PMC1459181
      30. Bussey KJ, Chin K, Lababidi S, Reimers M, Reinhold WC, Kuo WL, Gwadry F. Integrating data on DNA copy number with gene expression levels and drug sensitivities in the NCI-60 cell line panel. Mol Cancer Ther. 2006 Apr; 5(4):853-67.
        View in: PubMed PMC2733874
      31. Hon LS, Jain AN. A deterministic motif finding algorithm with application to the human genome. Bioinformatics. 2006 May 1; 22(9):1047-54.
        View in: PubMed
      32. Yoshimura K, Jain A, Allen HE, Laird LS, Chia CY, Ravi S, Brockstedt DG, Giedlin MA, Bahjat KS, Leong ML, Slansky JE, Cook DN, Dubensky TW, Pardoll DM, Schulick RD. Selective targeting of antitumor immune responses with engineered live-attenuated Listeria monocytogenes. Cancer Res. 2006 Jan 15; 66(2):1096-104.
        View in: PubMed
      33. Novak BA, Jain AN. Pathway recognition and augmentation by computational analysis of microarray expression data. Bioinformatics. 2006 Jan 15; 22(2):233-41.
        View in: PubMed
      34. Mehta KR, Nakao K, Zuraek MB, Ruan DT, Bergsland EK, Venook AP, Moore DH, Tokuyasu TA, Jain AN, Warren RS, Terdiman JP, Waldman FM. Fractional genomic alteration detected by array-based comparative genomic hybridization independently predicts survival after hepatic resection for metastatic colorectal cancer. Clin Cancer Res. 2005 Mar 1; 11(5):1791-7.
        View in: PubMed
      35. Snijders AM, Nowak NJ, Huey B, Fridlyand J, Law S, Conroy J, Tokuyasu T, Demir K, Chiu R, Mao JH, Jain AN, Jones SJ, Balmain A, Pinkel D, Albertson DG. Mapping segmental and sequence variations among laboratory mice using BAC array CGH. Genome Res. 2005 Feb; 15(2):302-11.
        View in: PubMed PMC546532
      36. Rosen DG, Wang L, Jain AN, Lu KH, Luo RZ, Yu Y, Liu J, Bast RC. Expression of the tumor suppressor gene ARHI in epithelial ovarian cancer is associated with increased expression of p21WAF1/CIP1 and prolonged progression-free survival. Clin Cancer Res. 2004 Oct 1; 10(19):6559-66.
        View in: PubMed
      37. Jain AN. Virtual screening in lead discovery and optimization. Curr Opin Drug Discov Devel. 2004 Jul; 7(4):396-403.
        View in: PubMed
      38. Paris PL, Andaya A, Fridlyand J, Jain AN, Weinberg V, Kowbel D, Brebner JH, Simko J, Watson JE, Volik S, Albertson DG, Pinkel D, Alers JC, van der Kwast TH, Vissers KJ, Schroder FH, Wildhagen MF, Febbo PG, Chinnaiyan AM, Pienta KJ, Carroll PR, Rubin MA, Collins C, van Dekken H. Whole genome scanning identifies genotypes associated with recurrence and metastasis in prostate tumors. Hum Mol Genet. 2004 Jul 1; 13(13):1303-13.
        View in: PubMed
      39. Nakao K, Mehta KR, Fridlyand J, Moore DH, Jain AN, Lafuente A, Wiencke JW, Terdiman JP, Waldman FM. High-resolution analysis of DNA copy number alterations in colorectal cancer by array-based comparative genomic hybridization. Carcinogenesis. 2004 Aug; 25(8):1345-57.
        View in: PubMed
      40. Jain AN. Ligand-based structural hypotheses for virtual screening. J Med Chem. 2004 Feb 12; 47(4):947-61.
        View in: PubMed
      41. Maldonado JL, Fridlyand J, Patel H, Jain AN, Busam K, Kageshita T, Ono T, Albertson DG, Pinkel D, Bastian BC. Determinants of BRAF mutations in primary melanomas. J Natl Cancer Inst. 2003 Dec 17; 95(24):1878-90.
        View in: PubMed
      42. Hon LS, Jain AN. Compositional structure of repetitive elements is quantitatively related to co-expression of gene pairs. J Mol Biol. 2003 Sep 12; 332(2):305-10.
        View in: PubMed
      43. Hackett CS, Hodgson JG, Law ME, Fridlyand J, Osoegawa K, de Jong PJ, Nowak NJ, Pinkel D, Albertson DG, Jain A, Jenkins R, Gray JW, Weiss WA. Genome-wide array CGH analysis of murine neuroblastoma reveals distinct genomic aberrations which parallel those in human tumors. Cancer Res. 2003 Sep 1; 63(17):5266-73.
        View in: PubMed
      44. Snijders AM, Fridlyand J, Mans DA, Segraves R, Jain AN, Pinkel D, Albertson DG. Shaping of tumor and drug-resistant genomes by instability and selection. Oncogene. 2003 Jul 10; 22(28):4370-9.
        View in: PubMed
      45. Snijders AM, Nowee ME, Fridlyand J, Piek JM, Dorsman JC, Jain AN, Pinkel D, van Diest PJ, Verheijen RH, Albertson DG. Genome-wide-array-based comparative genomic hybridization reveals genetic homogeneity and frequent copy number increases encompassing CCNE1 in fallopian tube carcinoma. Oncogene. 2003 Jul 3; 22(27):4281-6.
        View in: PubMed
      46. Veltman JA, Fridlyand J, Pejavar S, Olshen AB, Korkola JE, DeVries S, Carroll P, Kuo WL, Pinkel D, Albertson D, Cordon-Cardo C, Jain AN, Waldman FM. Array-based comparative genomic hybridization for genome-wide screening of DNA copy number in bladder tumors. Cancer Res. 2003 Jun 1; 63(11):2872-80.
        View in: PubMed
      47. Paris PL, Albertson DG, Alers JC, Andaya A, Carroll P, Fridlyand J, Jain AN, Kamkar S, Kowbel D, Krijtenburg PJ, Pinkel D, Schröder FH, Vissers KJ, Watson VJ, Wildhagen MF, Collins C, Van Dekken H. High-resolution analysis of paraffin-embedded and formalin-fixed prostate tumors using comparative genomic hybridization to genomic microarrays. Am J Pathol. 2003 Mar; 162(3):763-70.
        View in: PubMed PMC1868117
      48. Jain AN. Surflex: fully automatic flexible molecular docking using a molecular similarity-based search engine. J Med Chem. 2003 Feb 13; 46(4):499-511.
        View in: PubMed
      49. Olshen AB, Jain AN. Deriving quantitative conclusions from microarray expression data. Bioinformatics. 2002 Jul; 18(7):961-70.
        View in: PubMed
      50. Massion PP, Kuo WL, Stokoe D, Olshen AB, Treseler PA, Chin K, Chen C, Polikoff D, Jain AN, Pinkel D, Albertson DG, Jablons DM, Gray JW. Genomic copy number analysis of non-small cell lung cancer using array comparative genomic hybridization: implications of the phosphatidylinositol 3-kinase pathway. Cancer Res. 2002 Jul 1; 62(13):3636-40.
        View in: PubMed
      51. Wilhelm M, Veltman JA, Olshen AB, Jain AN, Moore DH, Presti JC, Kovacs G, Waldman FM. Array-based comparative genomic hybridization for the differential diagnosis of renal cell cancer. Cancer Res. 2002 Feb 15; 62(4):957-60.
        View in: PubMed
      52. Jain AN, Tokuyasu TA, Snijders AM, Segraves R, Albertson DG, Pinkel D. Fully automatic quantification of microarray image data. Genome Res. 2002 Feb; 12(2):325-32.
        View in: PubMed PMC155276
      53. Ghuloum AM, Sage CR, Jain AN. Molecular hashkeys: a novel method for molecular characterization and its application for predicting important pharmaceutical properties of molecules. J Med Chem. 1999 May 20; 42(10):1739-48.
        View in: PubMed
      54. Mount J, Ruppert J, Welch W, Jain AN. IcePick: a flexible surface-based system for molecular diversity. J Med Chem. 1999 Jan 14; 42(1):60-6.
        View in: PubMed
      55. Ruppert J, Welch W, Jain AN. Automatic identification and representation of protein binding sites for molecular docking. Protein Sci. 1997 Mar; 6(3):524-33.
        View in: PubMed PMC2143670
      56. Jain AN. Scoring noncovalent protein-ligand interactions: a continuous differentiable function tuned to compute binding affinities. J Comput Aided Mol Des. 1996 Oct; 10(5):427-40.
        View in: PubMed
      57. Welch W, Ruppert J, Jain AN. Hammerhead: fast, fully automated docking of flexible ligands to protein binding sites. Chem Biol. 1996 Jun; 3(6):449-62.
        View in: PubMed
      58. Jain AN, Harris NL, Park JY. Quantitative binding site model generation: compass applied to multiple chemotypes targeting the 5-HT1A receptor. J Med Chem. 1995 Apr 14; 38(8):1295-308.
        View in: PubMed
      59. Jain AN, Dietterich TG, Lathrop RH, Chapman D, Critchlow RE, Bauer BE, Webster TA, Lozano-Perez T. A shape-based machine learning tool for drug design. J Comput Aided Mol Des. 1994 Dec; 8(6):635-52.
        View in: PubMed
      60. Jain AN, Koile K, Chapman D. Compass: predicting biological activities from molecular surface properties. Performance comparisons on a steroid benchmark. J Med Chem. 1994 Jul 22; 37(15):2315-27.
        View in: PubMed
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