Ajay N. Jain - researchr alias

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2024

Correction: Complex peptide macrocycle optimization: combining NMR restraints with conformational analysis to guide structure-based and ligand-based designAjay N. Jain, Alexander C. Brueckner, Christine Jorge, Ann E. Cleves, Purnima Khandelwal, Janet Caceres Cortes, Luciano Mueller. jcamd, 38(1):12, December 2024. [doi]

2023

Complex peptide macrocycle optimization: combining NMR restraints with conformational analysis to guide structure-based and ligand-based designAjay N. Jain, Alexander C. Brueckner, Christine Jorge, Ann E. Cleves, Purnima Khandelwal, Janet Caceres Cortes, Luciano Mueller. jcamd, 37(11):519-535, November 2023. [doi]

2021

Synergy and Complementarity between Focused Machine Learning and Physics-Based Simulation in Affinity PredictionAnn E. Cleves, Stephen R. Johnson, Ajay N. Jain. jcisd, 61(12):5948-5966, 2021. [doi]

2020

Structure- and Ligand-Based Virtual Screening on DUD-E+: Performance Dependence on Approximations to the Binding PocketAnn E. Cleves, Ajay N. Jain. jcisd, 60(9):4296-4310, 2020. [doi]

2019

Electrostatic-field and surface-shape similarity for virtual screening and pose predictionAnn E. Cleves, Stephen R. Johnson, Ajay N. Jain. jcamd, 33(10):865-886, 2019. [doi]

Complex macrocycle exploration: parallel, heuristic, and constraint-based conformer generation using ForceGenAjay N. Jain, Ann E. Cleves, Qi Gao, Xiao Wang, Yizhou Liu, Edward C. Sherer, Mikhail Y. Reibarkh. jcamd, 33(6):531-558, 2019. [doi]

2018

Quantitative surface field analysis: learning causal models to predict ligand binding affinity and poseAnn E. Cleves, Ajay N. Jain. jcamd, 32(7):731-757, 2018. [doi]

2017

ForceGen 3D structure and conformer generation: from small lead-like molecules to macrocyclic drugsAnn E. Cleves, Ajay N. Jain. jcamd, 31(5):419-439, 2017. [doi]

2016

Extrapolative prediction using physically-based QSARAnn E. Cleves, Ajay N. Jain. jcamd, 30(2):127-152, 2016. [doi]

2015

Chemical and protein structural basis for biological crosstalk between PPARα and COX enzymesAnn E. Cleves, Ajay N. Jain. jcamd, 29(2):101-112, 2015. [doi]

Knowledge-guided docking: accurate prospective prediction of bound configurations of novel ligands using Surflex-DockAnn E. Cleves, Ajay N. Jain. jcamd, 29(6):485-509, 2015. [doi]

2014

Surflex-QMOD: physically meaningful QSARAlexander Steudle, Rocco Varela, Ajay N. Jain. jcheminf, 6(S-1):55, 2014. [doi]

Prediction of Off-Target Drug Effects Through Data FusionEmmanuel R. Yera, Ann E. Cleves, Ajay N. Jain. psb 2014: 160-171 [doi]

2013

A structure-guided approach for protein pocket modeling and affinity predictionRocco Varela, Ann E. Cleves, Russell Spitzer, Ajay N. Jain. jcamd, 27(11):917-934, 2013. [doi]

2012

Does your model weigh the same as a Duck?Ajay N. Jain, Ann E. Cleves. jcamd, 26(1):57-67, 2012. [doi]

Surflex-Dock: Docking benchmarks and real-world applicationRussell Spitzer, Ajay N. Jain. jcamd, 26(6):687-699, 2012. [doi]

2010

QMOD: physically meaningful QSARAjay N. Jain. jcamd, 24(10):865-878, 2010. [doi]

2009

Effects of protein conformation in docking: improved pose prediction through protein pocket adaptationAjay N. Jain. jcamd, 23(6):355-374, 2009. [doi]

2008

Effects of inductive bias on computational evaluations of ligand-based modeling and on drug discoveryAnn E. Cleves, Ajay N. Jain. jcamd, 22(3-4):147-159, 2008. [doi]

Bias, reporting, and sharing: computational evaluations of docking methodsAjay N. Jain. jcamd, 22(3-4):201-212, 2008. [doi]

Recommendations for evaluation of computational methodsAjay N. Jain, Anthony Nicholls. jcamd, 22(3-4):133-139, 2008. [doi]

Accurate and Interpretable Computational Modeling of Chemical MutagenicityJames J. Langham, Ajay N. Jain. jcisd, 48(9):1833-1839, 2008. [doi]

Customizing scoring functions for dockingTuan A. Pham, Ajay N. Jain. jcamd, 22(5):269-286, 2008. [doi]

2007

Surflex-Dock 2.1: Robust performance from ligand energetic modeling, ring flexibility, and knowledge-based searchAjay N. Jain. jcamd, 21(5):281-306, 2007. [doi]

2006

A deterministic motif finding algorithm with application to the human genomeLawrence S. Hon, Ajay N. Jain. bioinformatics, 22(9):1047-1054, 2006. [doi]

Pathway recognition and augmentation by computational analysis of microarray expression dataBarbara A. Novak, Ajay N. Jain. bioinformatics, 22(2):233-241, 2006. [doi]

2003

Statistical issues in the analysis of the array CGH dataJane Fridlyand, Antoine Snijders, Dan Pinkel, Donna Albertson, Ajay N. Jain. csb 2003: 407-408 [doi]

Wavelet Transforms for the Analysis of Microarray ExperimentsTaku A. Tokuyasu, Donna Albertson, Dan Pinkel, Ajay N. Jain. csb 2003: 429-430 [doi]

2002

Deriving quantitative conclusions from microarray expression dataAdam B. Olshen, Ajay N. Jain. bioinformatics, 18(7):961-970, 2002.

2000

Morphological similarity: A 3D molecular similarity method correlated with protein-ligand recognitionAjay N. Jain. jcamd, 14(2):199-213, 2000. [doi]

1996

Scoring noncovalent protein-ligand interactions: A continuous differentiable function tuned to compute binding affinitiesAjay N. Jain. jcamd, 10(5):427-440, 1996.

1994

Compass: A shape-based machine learning tool for drug designAjay N. Jain, Thomas G. Dietterich, Richard H. Lathrop, David Chapman, Roger E. Critchlow Jr., Barr E. Bauer, Teresa A. Webster, Tomás Lozano-Pérez. jcamd, 8(6):635-652, 1994.

1993

A Comparison of Dynamic Reposing and Tangent Distance for Drug Activity PredictionThomas G. Dietterich, Ajay N. Jain, Richard H. Lathrop, Tomás Lozano-Pérez. nips 1994: 216-223 [doi]

1992

PARSEC: a structured connectionist parsing system for spoken languageAjay N. Jain, Alex Waibel, David S. Touretzky. icassp 1992: 205-208 [doi]

1991

1990

Robust connectionist parsing of spoken languageAjay N. Jain, Alexander H. Waibel. icassp 1990: 593-596 [doi]

1989

Incremental Parsing by Modular Recurrent Connectionist NetworksAjay N. Jain, Alex Waibel. nips 1990: 364-371 [doi]

A Connectionist Parser Aimed at Spoken LanguageAjay N. Jain, Alex Waibel. iwpt 1989: 221-229 [doi]

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