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Brajesh K. Rai
Researcher at Pfizer
Publications - 15
Citations - 962
Brajesh K. Rai is an academic researcher from Pfizer. The author has contributed to research in topics: Computer science & Virtual screening. The author has an hindex of 11, co-authored 12 publications receiving 820 citations. Previous affiliations of Brajesh K. Rai include Princeton University.
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Journal ArticleDOI
Comparison of several molecular docking programs: pose prediction and virtual screening accuracy.
Jason B. Cross,David C. Thompson,Brajesh K. Rai,J. Christian Baber,Kristi Fan,Yongbo Hu,Christine Humblet +6 more
TL;DR: Cognate ligand docking to 68 diverse, high-resolution X-ray complexes revealed that ICM, GLIDE, and Surflex generated ligand poses close to the X-rays conformation more often than the other docking programs.
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Structure-based druggability assessment--identifying suitable targets for small molecule therapeutics.
TL;DR: Features of a pocket's size and shape, together with measures of its hydrophobicity, are most informative in identifying suitable drug-binding pockets in structure-based target druggability assessment.
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Kinase hinge binding scaffolds and their hydrogen bond patterns
TL;DR: From systematically analyzing the kinase scaffolds extracted from Pfizer crystal structure database (CSDb), it is recognized that large number of kinase inhibitors of diverse chemical structures are derived from a relatively small number of common scaffolds.
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Understanding the Impact of the P-loop Conformation on Kinase Selectivity
Cristiano Ruch Werneck Guimarães,Brajesh K. Rai,Michael John Munchhof,Shenping Liu,Jian Wang,Samit Kumar Bhattacharya,Leonard Buckbinder +6 more
TL;DR: Statistical and computational analyses of the crystal structure database demonstrate that inhibitors that induce the P-loop folded conformation tend to be more selective, especially if they take advantage of this specific conformation by interacting more favorably with a conserved Tyr or Phe residue from theP-loop.
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Fast and accurate generation of ab initio quality atomic charges using nonparametric statistical regression
Brajesh K. Rai,Gregory A. Bakken +1 more
TL;DR: A class of partial atomic charge assignment method that provides ab initio quality description of the electrostatics of bioorganic molecules is introduced that automatically provides an estimate of potential errors in the charge assignment, enabling systematic improvement of these models using additional data.