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J Comput Aided Mol Des. 2014 Mar;28(3):201-9. doi: 10.1007/s10822-014-9722-6. Epub 2014 Feb 11.

SAMPL4 & DOCK3.7: lessons for automated docking procedures.

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1
Department of Pharmaceutical Chemistry, University of California, San Francisco, 1700 4th St, Box 2550, San Francisco, CA, 94158, USA, rgc@blur.compbio.ucsf.edu.

Abstract

The SAMPL4 challenges were used to test current automated methods for solvation energy, virtual screening, pose and affinity prediction of the molecular docking pipeline DOCK 3.7. Additionally, first-order models of binding affinity were proposed as milestones for any method predicting binding affinity. Several important discoveries about the molecular docking software were made during the challenge: (1) Solvation energies of ligands were five-fold worse than any other method used in SAMPL4, including methods that were similarly fast, (2) HIV Integrase is a challenging target, but automated docking on the correct allosteric site performed well in terms of virtual screening and pose prediction (compared to other methods) but affinity prediction, as expected, was very poor, (3) Molecular docking grid sizes can be very important, serious errors were discovered with default settings that have been adjusted for all future work. Overall, lessons from SAMPL4 suggest many changes to molecular docking tools, not just DOCK 3.7, that could improve the state of the art. Future difficulties and projects will be discussed.

PMID:
24515818
PMCID:
PMC4006303
DOI:
10.1007/s10822-014-9722-6
[Indexed for MEDLINE]
Free PMC Article
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