Ajay N Jain

Summary

Affiliation: University of California
Country: USA

Publications

  1. pmc Customizing scoring functions for docking
    Tuan A Pham
    University of California, San Francisco, Box 0128, San Francisco, CA 94143 0128, USA
    J Comput Aided Mol Des 22:269-86. 2008
  2. pmc Does your model weigh the same as a duck?
    Ajay N Jain
    Department of Bioengineering and Therapeutic Sciences, Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA 94158 9001, USA
    J Comput Aided Mol Des 26:57-67. 2012
  3. ncbi request reprint Surflex-Dock 2.1: robust performance from ligand energetic modeling, ring flexibility, and knowledge-based search
    Ajay N Jain
    Department of Biopharmaceutical Sciences, UCSF Cancer Research Institute, University of California San Francisco, Box 0128, San Francisco, CA 94143 0128, USA
    J Comput Aided Mol Des 21:281-306. 2007
  4. ncbi request reprint Scoring functions for protein-ligand docking
    Ajay N Jain
    UCSF Cancer Research Institute, Department of Biopharmaceutical Sciences, University of California, San Francisco, CA 94143 0218, USA
    Curr Protein Pept Sci 7:407-20. 2006
  5. ncbi request reprint Bias, reporting, and sharing: computational evaluations of docking methods
    Ajay N Jain
    University of California San Francisco, Box 0128, San Francisco, CA 94143 0128, USA
    J Comput Aided Mol Des 22:201-12. 2008
  6. pmc Recommendations for evaluation of computational methods
    Ajay N Jain
    University of California San Francisco, Box 0128, San Francisco, CA 94143 0128, USA
    J Comput Aided Mol Des 22:133-9. 2008
  7. pmc Effects of protein conformation in docking: improved pose prediction through protein pocket adaptation
    Ajay N Jain
    Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA 94158 9001, USA
    J Comput Aided Mol Des 23:355-74. 2009
  8. pmc QMOD: physically meaningful QSAR
    Ajay N Jain
    Department of Bioengineering and Therapeutic Sciences, Helen Diller Family Comprehensive Cancer Center, University of California, 1450 3rd Street, Room D373, MC 0128, P O Box 589001, San Francisco, CA 94158 9001, USA
    J Comput Aided Mol Des 24:865-78. 2010
  9. pmc Surflex-Dock: Docking benchmarks and real-world application
    Russell Spitzer
    Deparment of Bioengineering and Therapeutic Sciences, Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA
    J Comput Aided Mol Des 26:687-99. 2012
  10. ncbi request reprint Parameter estimation for scoring protein-ligand interactions using negative training data
    Tuan A Pham
    Cancer Research Institute, Department of Biopharmaceutical Sciences, University of California, San Francisco, 2340 Sutter Street, San Francisco, California 94143 0128, USA
    J Med Chem 49:5856-68. 2006

Research Grants

  1. Machine Learning in Chemistry and Biology
    Ajay Jain; Fiscal Year: 2009
  2. Data-Driven Approaches for Molecular Docking
    Ajay N Jain; Fiscal Year: 2010

Collaborators

Detail Information

Publications25

  1. pmc Customizing scoring functions for docking
    Tuan A Pham
    University of California, San Francisco, Box 0128, San Francisco, CA 94143 0128, USA
    J Comput Aided Mol Des 22:269-86. 2008
    ..Analysis of the changes to the scoring function suggest that modifications can be learned that are related to protein-specific features such as active-site mobility...
  2. pmc Does your model weigh the same as a duck?
    Ajay N Jain
    Department of Bioengineering and Therapeutic Sciences, Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA 94158 9001, USA
    J Comput Aided Mol Des 26:57-67. 2012
    ..These fallacies will be discussed in the context of off-target predictive modeling, QSAR, molecular similarity computations, and docking. Examples will be shown that avoid these problems...
  3. ncbi request reprint Surflex-Dock 2.1: robust performance from ligand energetic modeling, ring flexibility, and knowledge-based search
    Ajay N Jain
    Department of Biopharmaceutical Sciences, UCSF Cancer Research Institute, University of California San Francisco, Box 0128, San Francisco, CA 94143 0128, USA
    J Comput Aided Mol Des 21:281-306. 2007
    ....
  4. ncbi request reprint Scoring functions for protein-ligand docking
    Ajay N Jain
    UCSF Cancer Research Institute, Department of Biopharmaceutical Sciences, University of California, San Francisco, CA 94143 0218, USA
    Curr Protein Pept Sci 7:407-20. 2006
    ..Generally, performance is not good enough to correctly rank among true ligands. Strategies for improvement are discussed...
  5. ncbi request reprint Bias, reporting, and sharing: computational evaluations of docking methods
    Ajay N Jain
    University of California San Francisco, Box 0128, San Francisco, CA 94143 0128, USA
    J Comput Aided Mol Des 22:201-12. 2008
    ..This paper presents detailed examples of pitfalls in each area and makes recommendations as to best practices...
  6. pmc Recommendations for evaluation of computational methods
    Ajay N Jain
    University of California San Francisco, Box 0128, San Francisco, CA 94143 0128, USA
    J Comput Aided Mol Des 22:133-9. 2008
    ..Here we propose a modest beginning, with recommendations for requirements on statistical reporting, requirements for data sharing, and best practices for benchmark preparation and usage...
  7. pmc Effects of protein conformation in docking: improved pose prediction through protein pocket adaptation
    Ajay N Jain
    Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA 94158 9001, USA
    J Comput Aided Mol Des 23:355-74. 2009
    ..Consideration of the best of two pose families (from alternate scoring regimes) yields a 75% mean success rate...
  8. pmc QMOD: physically meaningful QSAR
    Ajay N Jain
    Department of Bioengineering and Therapeutic Sciences, Helen Diller Family Comprehensive Cancer Center, University of California, 1450 3rd Street, Room D373, MC 0128, P O Box 589001, San Francisco, CA 94158 9001, USA
    J Comput Aided Mol Des 24:865-78. 2010
    ..The QMOD method offers a means to go beyond non-causative correlations in QSAR analysis...
  9. pmc Surflex-Dock: Docking benchmarks and real-world application
    Russell Spitzer
    Deparment of Bioengineering and Therapeutic Sciences, Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA
    J Comput Aided Mol Des 26:687-99. 2012
    ..In addition, use of multiple protein conformations significantly improved screening enrichment...
  10. ncbi request reprint Parameter estimation for scoring protein-ligand interactions using negative training data
    Tuan A Pham
    Cancer Research Institute, Department of Biopharmaceutical Sciences, University of California, San Francisco, 2340 Sutter Street, San Francisco, California 94143 0128, USA
    J Med Chem 49:5856-68. 2006
    ..Maximal enrichment of true ligands over nonligands exceeded 20-fold in over 80% of cases, with enrichment of greater than 100-fold in over 50% of cases...
  11. pmc Surface-based protein binding pocket similarity
    Russell Spitzer
    Department of Bioengineering and Therapeutic Sciences, Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, California 94158 9001, USA
    Proteins 79:2746-63. 2011
    ..Local protein binding pocket similarity provides qualitatively complementary information to other approaches, and it can yield quantitative information in support of functional annotation...
  12. pmc Chemical structural novelty: on-targets and off-targets
    Emmanuel R Yera
    University of California, San Francisco, Department of Bioengineering and Therapeutic Sciences, Helen Diller Family Comprehensive Cancer Center, San Francisco, California 94158, United States
    J Med Chem 54:6771-85. 2011
    ..Drug pairs that shared high 3D similarity but low 2D similarity (i.e., a novel scaffold) were shown to be much more likely to exhibit pharmacologically relevant differences in terms of specific protein target modulation...
  13. ncbi request reprint Robust ligand-based modeling of the biological targets of known drugs
    Ann E Cleves
    UCSF Cancer Research Institute and Department of Biopharmaceutical Sciences, University of California, San Francisco, California 94143, USA
    J Med Chem 49:2921-38. 2006
    ..Predicted activities derived from crossing drugs against modeled targets identified a number of known side effects, drug specificities, and drug-drug interactions that have a rational basis in molecular structure...
  14. ncbi request reprint Ligand-based structural hypotheses for virtual screening
    Ajay N Jain
    UCSF Cancer Research Institute and Comprehensive Cancer Center, University of California, San Francisco, California 94143 0128, USA
    J Med Chem 47:947-61. 2004
    ..The methods are practically applicable for rational design of ligands and for high-throughput virtual screening and offer competitive performance to many structure-based docking algorithms...
  15. pmc Physical binding pocket induction for affinity prediction
    James J Langham
    Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, California 94158 9001, USA
    J Med Chem 52:6107-25. 2009
    ....
  16. pmc Iterative refinement of a binding pocket model: active computational steering of lead optimization
    Rocco Varela
    Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, California 94143 0912, USA
    J Med Chem 55:8926-42. 2012
    ....
  17. pmc Accurate and interpretable computational modeling of chemical mutagenicity
    James J Langham
    Cancer Research Institute, University of California, San Francisco, 2340 Sutter Street, San Francisco, California 94143 0128, USA
    J Chem Inf Model 48:1833-9. 2008
    ..While we have focused on chemical mutagenicity in demonstrating this method, we anticipate that it may be more generally useful in modeling other molecular properties such as other types of chemical toxicity...
  18. ncbi request reprint Virtual screening in lead discovery and optimization
    Ajay N Jain
    University of California, San Francisco, Cancer Research Institute and Comprehensive Cancer Center, Box 0128, San Francisco, CA 94143 0128, USA
    Curr Opin Drug Discov Devel 7:396-403. 2004
    ..This review will discuss recent advances in both domains of virtual screening, including theoretical and practical advances and the implications for their application...
  19. doi request reprint Protein function annotation by local binding site surface similarity
    Russell Spitzer
    Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, California
    Proteins 82:679-94. 2014
    ..Proteins 2014; 82:679-694. © 2013 Wiley Periodicals, Inc. ..
  20. pmc Breast tumor copy number aberration phenotypes and genomic instability
    Jane Fridlyand
    Department of Epidemiology and Biostatistics, University of California San Francisco, CA 94143, USA
    BMC Cancer 6:96. 2006
    ..g. those involved in mitosis, replication, repair, and telomeres) are rarely mutated in chromosomally unstable sporadic tumors, even though such mutations are associated with some heritable cancer prone syndromes...
  21. ncbi request reprint Pathway recognition and augmentation by computational analysis of microarray expression data
    Barbara A Novak
    UCSF Cancer Research Institute and Comprehensive Cancer Center, University of California at San Francisco San Francisco, CA 94143 0128, USA
    Bioinformatics 22:233-41. 2006
    ..QPACA supports data visualization and both fine- and coarse-grained specifications, but, more importantly, addresses the problems of pathway recognition and pathway augmentation...
  22. ncbi request reprint Surflex: fully automatic flexible molecular docking using a molecular similarity-based search engine
    Ajay N Jain
    UCSF Cancer Research Institute and Comprehensive Cancer Center, University of California, San Francisco, California 94143 0128, USA
    J Med Chem 46:499-511. 2003
    ..Docking time was roughly linear in number of rotatable bonds, beginning with a few seconds for rigid molecules and adding approximately 10 s per rotatable bond...
  23. ncbi request reprint Prediction of off-target drug effects through data fusion
    Emmanuel R Yera
    Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA 94143, USA
    Pac Symp Biocomput 19:160-71. 2014
    ..For prediction of off-target effects, 3D-similarity performed best as a single modality, but combining all methods produced performance gains. Striking examples of structurally surprising off-target predictions are presented. ..
  24. ncbi request reprint A deterministic motif finding algorithm with application to the human genome
    Lawrence S Hon
    UCSF Cancer Research Institute and Comprehensive Cancer Center, University of California San Francisco, CA, USA
    Bioinformatics 22:1047-54. 2006
    ..MaMF is a very fast algorithm, suitable for application to large numbers of interesting gene sets...
  25. ncbi request reprint Effects of inductive bias on computational evaluations of ligand-based modeling and on drug discovery
    Ann E Cleves
    BioPharmics LLC, 36 Avila Road, San Mateo, CA 94402, USA
    J Comput Aided Mol Des 22:147-59. 2008
    ..We propose specific strategies to explicitly address the problems posed by inductive bias considerations...

Research Grants6

  1. Machine Learning in Chemistry and Biology
    Ajay Jain; Fiscal Year: 2009
    ..All methods and data will be made widely available to both academic and industrial investigators. ..
  2. Data-Driven Approaches for Molecular Docking
    Ajay N Jain; Fiscal Year: 2010
    ..Our proposed work is to make substantial improvements in both the docking case (where we know a protein structure) and in the ligand-based modeling case (where we do not). ..