A machine learning approach to predicting protein–ligand binding affinity with applications to molecular docking-Reference-Cited by-同舟云学术

A machine learning approach to predicting protein–ligand binding affinity with applications to molecular docking

Published:2010-03-17 Issue:9 Volume:26 Page:1169-1175
ISSN:1460-2059
Container-title:Bioinformatics
language:en
Short-container-title:

Author:

Ballester Pedro J.¹,Mitchell John B. O.¹

Affiliation:

1. Unilever Centre for Molecular Science Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW and 2 Centre for Biomolecular Sciences, University of St Andrews, North Haugh, St Andrews KY16 9ST, UK

Publisher

Oxford University Press (OUP)

Subject

Computational Mathematics,Computational Theory and Mathematics,Computer Science Applications,Molecular Biology,Biochemistry,Statistics and Probability

Link

http://academic.oup.com/bioinformatics/article-pdf/26/9/1169/25736723/btq112.pdf

Reference51 articles.

1. A general approach for developing system-specific functions to score protein-ligand docked complexes using support vector inductive logic programming;Amini;Proteins,2007

2. Flexible docking using Tabu search and an empirical estimate of binding affinity;Baxter;Proteins: Struct., Funct., Genet.,1998

3. The Protein Data Bank;Berman;Nucleic Acids Res.,2000

4. The development of a simple empirical scoring function to estimate the binding constant for a protein-ligand complex of known three-dimensional structure;Böhm;J. Comput.-Aided Mol. Des.,1994

5. Prediction of binding constants of protein ligands: a fast method for the prioritization of hits obtained from de novo design or 3D database search programs;Böhm;J. Comput.-Aided Mol. Des.,1998

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