Ajay Jain, PhD

SchoolUCSF School of Medicine
DepartmentHDF Comprehensive Cancer Ctr
Address1450 Third St
San Francisco CA 94158
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    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 Research Activities and Funding
    Binding-Site Modeling with Multiple-Instance Machine-Learning
    NIH/NIGMS R01GM101689Jan 1, 2013 - Mar 31, 2021
    Role: Principal Investigator
    Data-Driven Approaches for Molecular Docking
    NIH/NIGMS R01GM070481Jul 1, 2005 - Jun 30, 2014
    Role: Principal Investigator
    Cancer Center Support Grant
    NIH/NCI P30CA082103Aug 5, 1999 - May 31, 2018
    Role: Co-Investigator

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    Collapse Bibliographic 
    Collapse Publications
    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 A. ForceGen 3D structure and conformer generation: from small lead-like molecules to macrocyclic drugs. J Comput Aided Mol Des. 2017 May; 31(5):419-439. PMID: 28289981.
      View in: PubMed
    2. Saxena M, Sobotka PA, Hamshere SM, Jain A, Mathur A, Knight C, Collier DJ, Lobo MD. Antihypertensive Effects of a Central Arteriovenous Anastomosis Are Mediated Through Profound Reduction in Systemic Vascular Resistance. Circ Cardiovasc Interv. 2016 Aug; 9(8):e004012. PMID: 27512091.
      View in: PubMed
    3. Cleves AE, Jain A. Extrapolative prediction using physically-based QSAR. J Comput Aided Mol Des. 2016 Feb; 30(2):127-52. PMID: 26860112; PMCID: PMC4796382.
    4. Cleves AE, Jain A. 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. PMID: 25940276; PMCID: PMC4464052.
    5. Rathod KS, Jones DA, Gallagher S, Rathod VS, Weerackody R, Jain A, Mathur A, Mohiddin SA, Archbold RA, Wragg A, Knight CJ. Atypical risk factor profile and excellent long-term outcomes of young patients treated with primary percutaneous coronary intervention for ST-elevation myocardial infarction. Eur Heart J Acute Cardiovasc Care. 2016 Feb; 5(1):23-32. PMID: 25589633.
      View in: PubMed
    6. Cleves AE, Jain A. Chemical and protein structural basis for biological crosstalk between PPARa and COX enzymes. J Comput Aided Mol Des. 2015 Feb; 29(2):101-12. PMID: 25428568; PMCID: PMC4298667.
    7. Yera ER, Cleves AE, Jain A. Prediction of off-target drug effects through data fusion. Pac Symp Biocomput. 2014; 160-71. PMID: 24297543; PMCID: PMC3897331.
    8. Spitzer R, Cleves AE, Varela R, Jain A. Protein function annotation by local binding site surface similarity. Proteins. 2014 Apr; 82(4):679-94. PMID: 24166661; PMCID: PMC3949165.
    9. Varela R, Cleves AE, Spitzer R, Jain A. A structure-guided approach for protein pocket modeling and affinity prediction. J Comput Aided Mol Des. 2013 Nov; 27(11):917-34. PMID: 24214361; PMCID: PMC3851759.
    10. 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. PMID: 23871153.
      View in: PubMed
    11. Varela R, Walters WP, Goldman BB, Jain A. Iterative refinement of a binding pocket model: active computational steering of lead optimization. J Med Chem. 2012 Oct 25; 55(20):8926-42. PMID: 23046104; PMCID: PMC3640415.
    12. Jain A, Olsen HS, Vyzasatya R, Burch E, Sakoda Y, Mérigeon EY, Cai L, Lu C, Tan M, Tamada K, Schulze D, Block DS, Strome SE. Fully recombinant IgG2a Fc multimers (stradomers) effectively treat collagen-induced arthritis and prevent idiopathic thrombocytopenic purpura in mice. Arthritis Res Ther. 2012 Aug 20; 14(4):R192. PMID: 22906120; PMCID: PMC3580588.
    13. Spitzer R, Jain A. Surflex-Dock: Docking benchmarks and real-world application. J Comput Aided Mol Des. 2012 Jun; 26(6):687-99. PMID: 22569590; PMCID: PMC3398190.
    14. Jones DA, Weerackody R, Rathod K, Behar J, Gallagher S, Knight CJ, Kapur A, Jain A, 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. PMID: 22516393.
      View in: PubMed
    15. Jain A, Cleves AE. Does your model weigh the same as a duck? J Comput Aided Mol Des. 2012 Jan; 26(1):57-67. PMID: 22187141; PMCID: PMC3276372.
    16. Yera ER, Cleves AE, Jain A. Chemical structural novelty: on-targets and off-targets. J Med Chem. 2011 Oct 13; 54(19):6771-85. PMID: 21916467; PMCID: PMC3188662.
    17. Spitzer R, Cleves AE, Jain A. Surface-based protein binding pocket similarity. Proteins. 2011 Sep; 79(9):2746-63. PMID: 21769944; PMCID: PMC3155014.
    18. Jain A. QMOD: physically meaningful QSAR. J Comput Aided Mol Des. 2010 Oct; 24(10):865-78. PMID: 20721601; PMCID: PMC3109424.
    19. Nicholls A, McGaughey GB, Sheridan RP, Good AC, Warren G, Mathieu M, Muchmore SW, Brown SP, Grant JA, Haigh JA, Nevins N, Jain A, Kelley B. Molecular shape and medicinal chemistry: a perspective. J Med Chem. 2010 May 27; 53(10):3862-86. PMID: 20158188; PMCID: PMC2874267.
    20. Langham JJ, Cleves AE, Spitzer R, Kirshner D, Jain A. Physical binding pocket induction for affinity prediction. J Med Chem. 2009 Oct 08; 52(19):6107-25. PMID: 19754201; PMCID: PMC2766559.
    21. Jain A. Effects of protein conformation in docking: improved pose prediction through protein pocket adaptation. J Comput Aided Mol Des. 2009 Jun; 23(6):355-74. PMID: 19340588; PMCID: PMC2693357.
    22. Langham JJ, Jain A. Accurate and interpretable computational modeling of chemical mutagenicity. J Chem Inf Model. 2008 Sep; 48(9):1833-9. PMID: 18771257; PMCID: PMC2753474.
    23. Jain A, Nicholls A. Recommendations for evaluation of computational methods. J Comput Aided Mol Des. 2008 Mar-Apr; 22(3-4):133-9. PMID: 18338228; PMCID: PMC2311385.
    24. Pham TA, Jain A. Customizing scoring functions for docking. J Comput Aided Mol Des. 2008 May; 22(5):269-86. PMID: 18273558; PMCID: PMC3108487.
    25. Jain A. Bias, reporting, and sharing: computational evaluations of docking methods. J Comput Aided Mol Des. 2008 Mar-Apr; 22(3-4):201-12. PMID: 18075713.
      View in: PubMed
    26. Cleves AE, Jain A. 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. PMID: 18074107.
      View in: PubMed
    27. Jain A. 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. PMID: 17387436.
      View in: PubMed
    28. Kingsley CB, Kuo WL, Polikoff D, Berchuck A, Gray JW, Jain A. 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. 2007 Feb 05; 2:10-21. PMID: 19458754; PMCID: PMC2675496.
    29. 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. PMID: 17157792.
      View in: PubMed
    30. Pham TA, Jain A. Parameter estimation for scoring protein-ligand interactions using negative training data. J Med Chem. 2006 Oct 05; 49(20):5856-68. PMID: 17004701.
      View in: PubMed
    31. Jain A. Scoring functions for protein-ligand docking. Curr Protein Pept Sci. 2006 Oct; 7(5):407-20. PMID: 17073693.
      View in: PubMed
    32. Cleves AE, Jain A. Robust ligand-based modeling of the biological targets of known drugs. J Med Chem. 2006 May 18; 49(10):2921-38. PMID: 16686535.
      View in: PubMed
    33. Fridlyand J, Snijders AM, Ylstra B, Li H, Olshen A, Segraves R, Dairkee S, Tokuyasu T, Ljung BM, Jain A, 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 Apr 18; 6:96. PMID: 16620391; PMCID: PMC1459181.
    34. Bussey KJ, Chin K, Lababidi S, Reimers M, Reinhold WC, Kuo WL, Gwadry F, Kouros-Mehr H, Fridlyand J, Jain A, Collins C, Nishizuka S, Tonon G, Roschke A, Gehlhaus K, Kirsch I, Scudiero DA, Gray JW, Weinstein JN. 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. PMID: 16648555; PMCID: PMC2733874.
    35. Hon LS, Jain A. A deterministic motif finding algorithm with application to the human genome. Bioinformatics. 2006 May 01; 22(9):1047-54. PMID: 16455748.
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    36. 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. PMID: 16424046.
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    37. Novak BA, Jain A. Pathway recognition and augmentation by computational analysis of microarray expression data. Bioinformatics. 2006 Jan 15; 22(2):233-41. PMID: 16278238.
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    38. Mehta KR, Nakao K, Zuraek MB, Ruan DT, Bergsland EK, Venook AP, Moore DH, Tokuyasu TA, Jain A, 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 01; 11(5):1791-7. PMID: 15756001.
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    39. Snijders AM, Nowak NJ, Huey B, Fridlyand J, Law S, Conroy J, Tokuyasu T, Demir K, Chiu R, Mao JH, Jain A, 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. PMID: 15687294; PMCID: PMC546532.
    40. Rosen DG, Wang L, Jain A, 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 01; 10(19):6559-66. PMID: 15475444.
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    41. Jain A. Virtual screening in lead discovery and optimization. Curr Opin Drug Discov Devel. 2004 Jul; 7(4):396-403. PMID: 15338948.
      View in: PubMed
    42. Paris PL, Andaya A, Fridlyand J, Jain A, 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 01; 13(13):1303-13. PMID: 15138198.
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    43. Nakao K, Mehta KR, Fridlyand J, Moore DH, Jain A, 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. PMID: 15001537.
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    44. Jain A. Ligand-based structural hypotheses for virtual screening. J Med Chem. 2004 Feb 12; 47(4):947-61. PMID: 14761196.
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    45. Maldonado JL, Fridlyand J, Patel H, Jain A, 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. PMID: 14679157.
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    46. Hon LS, Jain A. Compositional structure of repetitive elements is quantitatively related to co-expression of gene pairs. J Mol Biol. 2003 Sep 12; 332(2):305-10. PMID: 12948482.
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    47. 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 01; 63(17):5266-73. PMID: 14500357.
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    48. Snijders AM, Fridlyand J, Mans DA, Segraves R, Jain A, Pinkel D, Albertson DG. Shaping of tumor and drug-resistant genomes by instability and selection. Oncogene. 2003 Jul 10; 22(28):4370-9. PMID: 12853973.
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    49. Snijders AM, Nowee ME, Fridlyand J, Piek JM, Dorsman JC, Jain A, 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 03; 22(27):4281-6. PMID: 12833150.
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    50. Veltman JA, Fridlyand J, Pejavar S, Olshen AB, Korkola JE, DeVries S, Carroll P, Kuo WL, Pinkel D, Albertson D, Cordon-Cardo C, Jain A, Waldman FM. Array-based comparative genomic hybridization for genome-wide screening of DNA copy number in bladder tumors. Cancer Res. 2003 Jun 01; 63(11):2872-80. PMID: 12782593.
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    51. Paris PL, Albertson DG, Alers JC, Andaya A, Carroll P, Fridlyand J, Jain A, 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. PMID: 12598311; PMCID: PMC1868117.
    52. Jain A. Surflex: fully automatic flexible molecular docking using a molecular similarity-based search engine. J Med Chem. 2003 Feb 13; 46(4):499-511. PMID: 12570372.
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    53. Olshen AB, Jain A. Deriving quantitative conclusions from microarray expression data. Bioinformatics. 2002 Jul; 18(7):961-70. PMID: 12117794.
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    54. Massion PP, Kuo WL, Stokoe D, Olshen AB, Treseler PA, Chin K, Chen C, Polikoff D, Jain A, 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 01; 62(13):3636-40. PMID: 12097266.
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    55. Wilhelm M, Veltman JA, Olshen AB, Jain A, 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. PMID: 11861363.
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    56. Jain A, 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. PMID: 11827952; PMCID: PMC155276.
    57. Ghuloum AM, Sage CR, Jain A. 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. PMID: 10346926.
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    58. Mount J, Ruppert J, Welch W, Jain A. IcePick: a flexible surface-based system for molecular diversity. J Med Chem. 1999 Jan 14; 42(1):60-6. PMID: 9888833.
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    59. Ruppert J, Welch W, Jain A. Automatic identification and representation of protein binding sites for molecular docking. Protein Sci. 1997 Mar; 6(3):524-33. PMID: 9070435; PMCID: PMC2143670.
    60. Jain A. 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. PMID: 8951652.
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    61. Welch W, Ruppert J, Jain A. Hammerhead: fast, fully automated docking of flexible ligands to protein binding sites. Chem Biol. 1996 Jun; 3(6):449-62. PMID: 8807875.
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    62. Jain A, 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. PMID: 7731016.
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    63. Jain A, 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. PMID: 7738601.
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    64. Jain A, 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. PMID: 8057280.
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