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Paul C. Sanschagrin
Researcher at Harvard University
Publications - 21
Citations - 6405
Paul C. Sanschagrin is an academic researcher from Harvard University. The author has contributed to research in topics: Ligand (biochemistry) & Protein structure. The author has an hindex of 13, co-authored 21 publications receiving 5246 citations. Previous affiliations of Paul C. Sanschagrin include University of Marburg & Michigan State University.
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Journal ArticleDOI
Extra Precision Glide: Docking and Scoring Incorporating a Model of Hydrophobic Enclosure for Protein-Ligand Complexes
Richard A. Friesner,Robert B. Murphy,Matthew P. Repasky,Leah L. Frye,Jeremy R. Greenwood,Thomas A. Halgren,Paul C. Sanschagrin,Daniel T. Mainz +7 more
TL;DR: Enrichment results demonstrate the importance of the novel XP molecular recognition and water scoring in separating active and inactive ligands and avoiding false positives.
Journal ArticleDOI
Collaboration gets the most out of software
Andrew Morin,Ben Eisenbraun,Jason Key,Paul C. Sanschagrin,Michael A Timony,Michelle Ottaviano,Piotr Sliz +6 more
TL;DR: By centralizing many of the tasks associated with the upkeep of scientific software, SBGrid allows researchers to spend more of their time on research.
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Predicting conserved water-mediated and polar ligand interactions in proteins using a K-nearest-neighbors genetic algorithm.
Michael L. Raymer,Paul C. Sanschagrin,William F. Punch,Sridhar Venkataraman,Erik D. Goodman,Leslie A. Kuhn +5 more
TL;DR: The ability to predict water-mediated and polar interactions from the free protein structure indicates the surprising extent to which the conservation or displacement of active-site bound water is independent of the ligand, and shows that the protein micro-environment of each water molecule is the dominant influence.
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SFCscore: scoring functions for affinity prediction of protein-ligand complexes.
TL;DR: Empirical scoring functions to calculate binding affinities of protein–ligand complexes have been calibrated based on experimental structure and affinity data collected from public and industrial sources and superior performance is observed in many cases, but the results also illustrate the need for further improvements.
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Distilling the essential features of a protein surface for improving protein-ligand docking, scoring, and virtual screening.
TL;DR: This paper presents an alternative method of protein template and ligand interaction point design that identifies the most favorable points for making hydrophobic and hydrogen–bond interactions by using a knowledge base.