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 over 20 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 very fast conformational search, including macrocycles, optionally including context from NMR restraints or fitting X-ray density maps. 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.

Research Funding

  • January 1, 2013 - March 31, 2021 - Binding-Site Modeling with Multiple-Instance Machine-Learning, Principal Investigator. Sponsor: NIH/NIGMS, Sponsor Award ID: R01GM101689
  • July 1, 2005 - June 30, 2014 - Data-Driven Approaches for Molecular Docking, Principal Investigator. Sponsor: NIH/NIGMS, Sponsor Award ID: R01GM070481
  • August 5, 1999 - September 29, 2012 - Cancer Center Support Grant, Co-Investigator. Sponsor: NIH/NCI, Sponsor Award ID: P30CA082103
  • January 1, 2013 - March 31, 2021 - Binding-Site Modeling with Multiple-Instance Machine-Learning, Principal Investigator. Sponsor: NIH/NIGMS, Sponsor Award ID: R01GM101689


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

Honors & Awards

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

Selected Publications

  1. Poli Y, Nallamothu V, Hao A, Goud MD, Wang X, Desiraju S, Mangrauthia SK, Jain A NH787 EMS mutant of rice variety Nagina22 exhibits higher phosphate use efficiency.  View on PubMed
  2. Brueckner AC, Deng Q, Cleves AE, Lesburg CA, Alvarez JC, Reibarkh MY, Sherer EC, Jain AN Conformational Strain of Macrocyclic Peptides in Ligand-Receptor Complexes Based on Advanced Refinement of Bound-State Conformers.  View on PubMed
  3. Chen Y, Rathod KS, Hamshere S, Choudry F, Akhtar MM, Curtis M, Amersey R, Guttmann O, O'Mahony C, Jain A, Wragg A, Baumbach A, Mathur A, Jones DA COVID-19 and changes in activity and treatment of ST elevation MI from a UK cardiac centre.  View on PubMed
  4. Bajaj R, Huang X, Kilic Y, Jain A, Ramasamy A, Torii R, Moon J, Koh T, Crake T, Parker MK, Tufaro V, Serruys PW, Pugliese F, Mathur A, Baumbach A, Dijkstra J, Zhang Q, Bourantas CV A deep learning methodology for the automated detection of end-diastolic frames in intravascular ultrasound images.  View on PubMed
  5. Wang X, Jain A, Huang X, Lan X, Xu L, Zhao G, Cong X, Zhang Z, Fan X, Hu F Reducing phenanthrene uptake and translocation, and accumulation in the seeds by overexpressing OsNRT2.3b in rice.  View on PubMed
  6. Jain AN, Cleves AE, Brueckner AC, Lesburg CA, Deng Q, Sherer EC, Reibarkh MY XGen: Real-Space Fitting of Complex Ligand Conformational Ensembles to X-ray Electron Density Maps.  View on PubMed
  7. Xanthakos SA, Lavine JE, Yates KP, Schwimmer JB, Molleston JP, Rosenthal P, Murray KF, Vos MB, Jain AK, Scheimann AO, Miloh T, Fishbein M, Behling CA, Brunt EM, Sanyal AJ, Tonascia J, Progression of Fatty Liver Disease in Children Receiving Standard of Care Lifestyle Advice.  View on PubMed
  8. Cleves AE, Jain AN Structure- and Ligand-Based Virtual Screening on DUD-E+: Performance Dependence on Approximations to the Binding Pocket.  View on PubMed
  9. Cleves AE, Johnson SR, Jain AN Electrostatic-field and surface-shape similarity for virtual screening and pose prediction.  View on PubMed
  10. Jain AN, Cleves AE, Gao Q, Wang X, Liu Y, Sherer EC, Reibarkh MY Complex macrocycle exploration: parallel, heuristic, and constraint-based conformer generation using ForceGen.  View on PubMed
  11. Cleves AE, Jain AN Quantitative surface field analysis: learning causal models to predict ligand binding affinity and pose.  View on PubMed
  12. Cleves AE, Jain AN ForceGen 3D structure and conformer generation: from small lead-like molecules to macrocyclic drugs.  View on PubMed
  13. Cleves AE, Jain AN Extrapolative prediction using physically-based QSAR.  View on PubMed
  14. Cleves AE, Jain AN Knowledge-guided docking: accurate prospective prediction of bound configurations of novel ligands using Surflex-Dock.  View on PubMed
  15. Jones DA, Rathod KS, Gallagher S, Jain AK, Kalra SS, Lim P, Crake T, Ozkor M, Rakhit R, Knight CJ, Iqbal MB, Dalby MC, Malik IS, Whitbread M, Mathur A, Redwood S, MacCarthy PA, Weerackody R, Wragg A Manual Thrombus Aspiration Is Not Associated With Reduced Mortality in Patients Treated With Primary Percutaneous Coronary Intervention: An Observational Study of 10,929 Patients With ST-Segment Elevation Myocardial Infarction From the London Heart At  View on PubMed
  16. Taylor RJ, Saloura V, Jain A, Goloubeva O, Wong S, Kronsberg S, Nagilla M, Silpino L, de Souza J, Seiwert T, Vokes E, Villaflor V, Cohen EE Ex vivo antibody-dependent cellular cytotoxicity inducibility predicts efficacy of cetuximab.  View on PubMed
  17. Rathod KS, Jones DA, Bromage DI, Gallagher SM, Rathod VS, Kennon S, Knight C, Rothman MT, Mathur A, Smith E, Jain AK, Archbold RA, Wragg A Radial primary percutaneous coronary intervention is independently associated with decreased long-term mortality in high-risk ST-elevation myocardial infarction patients.  View on PubMed
  18. Jones DA, Howard JP, Rathod KS, Gallagher SM, Knight CJ, Jain AK, Mathur A, Mohiddin SA, Mills PG, Timmis AD, Archbold RA, Wragg A The impact of socio-economic status on all-cause mortality after percutaneous coronary intervention: an observational cohort study of 13,770 patients.  View on PubMed
  19. Jain AK, Wen JX, Arora S, Blomenkamp KS, Rodrigues J, Blaufuss TA, Liou V, Burrin DG, Long JP, Teckman JH Validating hyperbilirubinemia and gut mucosal atrophy with a novel ultramobile ambulatory total parenteral nutrition piglet model.  View on PubMed
  20. Cleves AE, Jain AN Chemical and protein structural basis for biological crosstalk between PPARa and COX enzymes.  View on PubMed

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