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A Critical Assessment of Docking Programs and Scoring Functions
Gregory L. Warren, C. Webster Andrews, Anna-Maria Capelli, Brian Clarke, Judith LaLonde, Millard H. Lambert, Mika Lindvall, Neysa Nevins, Simon F. Semus, Stefan Senger, Giovanna Tedesco, Ian D. Wall, Jamse M. Woolven, Catherine E. Peishoff, and Martha S. Head
In this paper the authors report about an evaluation of 10 docking programs and 37 scoring functions. Docking programs comprise FlexX, Glide and Gold, as well as Dock, Flo, Fred, LigFit, DockIt, MOE®, and MVP. They were tested on seven different protein target classes. In the redocking accuracy test, FlexX proved itself well on the target classes Kinase, Polypeptide Deformylase and Gyrase B, outperforming Glide and/or Gold. In the correlation coefficient score between the logA and the docking score, it provided the best or second best correlation in more than 50% of cases. Note that this comparison was performed with FlexX version 1.10.1 – we expect Release 2 to show even better results.
Improved FlexX Docking Using FlexS-Determined Base Fragment Placement
Simon S. J. Cross
The author describes a novel hybrid FlexS/FlexX base fragment selection approach. Instead of letting FlexX decide about the base fragment, FlexS is used to align the base fragment of the test ligand with a cocrystallized reference structure. The cocrystallized template does not need to have the same atoms as the base fragment, because correspondence between atoms is not a prerequisite for FlexS. Using this approach on the flexx200 data set, 63% of the complexes can be reproduced at rank 1 versus 47% using FlexX alone.
In cross docking experiments, the method described here can provide solutions in cases where FlexX alone fails, or provide solutions that are more precisely positioned, using a different ligand template. Please note that this study was performed with a version of FlexX prior to Release 2.
Virtual Screening of Biogenic Amine-Binding G-Protein Coupled Receptors: Comparative Evaluation of Protein- and Ligand-Based Virtual Screening Protocols
A. Evers, G. Hessler, H. Matter, and T. Klabunde
The authors, all affiliated with Sanofi-Aventis, compare four ways to assess the screening challenge against GCPRs:
- docking into homology models
- pharmacophore and Feature Trees searching
- 3D similarity searching
- 3D searching with statistical post-processing steps
This paper demonstrates that under certain circumstances ligand-based approaches may be superior to docking. The FTrees program with its underlying Feature Tree descriptor outperforms all other screening methods evaluated in the work at the interesting, i.e. early, enrichment stages.
Based on a consensus-like scheme, a striking hit rate of 100% has been obtained in three out of four cases with Feature Tree models. Despite the fact that this hit rate applies to only the first 1% of a ranked one thousand member database, it is the best result for this bin and all methods that have been compared. In addition, the resulting and interpretable pharmacophore is in good agreement with existing knowledge about molecular recognition at the alpha1A receptor.