BgN-Score and BsN-Score: Bagging and boosting based ensemble neural networks scoring functions for accurate binding affinity prediction of protein-ligand complexes
Open Access
- 23 February 2015
- journal article
- Published by Springer Science and Business Media LLC in BMC Bioinformatics
- Vol. 16 (S4), S8
- https://doi.org/10.1186/1471-2105-16-S4-S8
Abstract
No abstract availableKeywords
This publication has 42 references indexed in Scilit:
- NNScore: A Neural-Network-Based Scoring Function for the Characterization of Protein−Ligand ComplexesJournal of Chemical Information and Modeling, 2010
- A machine learning approach to predicting protein–ligand binding affinity with applications to molecular dockingBioinformatics, 2010
- Comparative Assessment of Scoring Functions on a Diverse Test SetJournal of Chemical Information and Modeling, 2009
- Chemical Probes that Competitively and Selectively Inhibit Stat3 ActivationPLOS ONE, 2009
- Molecular Docking for Substrate Identification: The Short-Chain Dehydrogenases/ReductasesJournal of Molecular Biology, 2008
- How many drug targets are there?Nature Reviews Drug Discovery, 2006
- An Extensive Test of 14 Scoring Functions Using the PDBbind Refined Set of 800 Protein−Ligand ComplexesJournal of Chemical Information and Computer Sciences, 2004
- Knowledge-based scoring function to predict protein-ligand interactionsJournal of Molecular Biology, 2000
- The Protein Data BankNucleic Acids Research, 2000
- Development and validation of a genetic algorithm for flexible dockingJournal of Molecular Biology, 1997