Journal of Molecular Modeling

, Volume 16, Issue 7, pp 1223–1230 | Cite as

Improving performance of docking-based virtual screening by structural filtration

  • Fedor N. Novikov
  • Viktor S. Stroylov
  • Oleg V. Stroganov
  • Ghermes G. Chilov
Original Paper


In the current study an innovative method of structural filtration of docked ligand poses is introduced and applied to improve the virtual screening results. The structural filter is defined by a protein-specific set of interactions that are a) structurally conserved in available structures of a particular protein with its bound ligands, and b) that can be viewed as playing the crucial role in protein-ligand binding. The concept was evaluated on a set of 10 diverse proteins, for which the corresponding structural filters were developed and applied to the results of virtual screening obtained with the Lead Finder software. The application of structural filtration resulted in a considerable improvement of the enrichment factor ranging from several folds to hundreds folds depending on the protein target. It appeared that the structural filtration had effectively repaired the deficiencies of the scoring functions that used to overestimate decoy binding, resulting into a considerably lower false positive rate. In addition, the structural filters were also effective in dealing with some deficiencies of the protein structure models that would lead to false negative predictions otherwise. The ability of structural filtration to recover relatively small but specifically bound molecules creates promises for the application of this technology in the fragment-based drug discovery.


Improvement of virtual screening performance by structural filtration for ADRB2 as a target. Positions of the native ligands obtained during virtual screening are depicted by vertical bars. Upper part of the plot corresponds to the performance of docking-based screening; lower part - docking-based screening followed by structural filtration. Lower part of the plot contains all (48) native ligands of ADRB2, which fall into the top 0.6% of the screened library.


Docking Focused library Fragment-based Structural filtration Virtual screening 



We thank Val Kulkov (BioMolTech Corp) for revising the manuscript. Our special thanks to Prof. Viktor Gergel and Alexander Grishagin, the Center of Supercomputer Technologies of the N.I. Lobacevsky State University of Nizhni Novgorod, for our access to the high-performance computing cluster (Nizhni Novgorod segment of the SKIF-grid program). The work was supported by the Foundation of Assistance to Small Innovative Enterprises (Contract №7168/4935r)

Supplementary material

894_2009_633_MOESM1_ESM.doc (80 kb)
ESM 1 (DOC 80 kb)
894_2009_633_MOESM2_ESM.xls (22 kb)
Table S1 Average physicochemical properties of native ligands of the studied target proteins. (XLS 22 kb)
894_2009_633_MOESM3_ESM.doc (234 kb)
Table S2 Docking of native ligands to their targets. (DOC 234 kb)
894_2009_633_MOESM4_ESM.doc (88 kb)
Figure S1 Enrichment plots corresponding to the docking-based virtual screening. True positive ligand rate (TP%) is plotted against the true negative rate (TN%). (DOC 88 kb)
894_2009_633_MOESM5_ESM.doc (81 kb)
Figure S2 Enrichment plots corresponding to the docking-based virtual screening followed by structural filtration of docked ligand poses. True positive ligand rate (TP%) is plotted against the true negative rate (TN%). (DOC 81 kb)


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Copyright information

© Springer-Verlag 2009

Authors and Affiliations

  • Fedor N. Novikov
    • 1
  • Viktor S. Stroylov
    • 1
  • Oleg V. Stroganov
    • 1
    • 2
  • Ghermes G. Chilov
    • 1
    • 2
  1. 1.MolTech LtdMoscowRussia
  2. 2.N.D. Zelinsky Institute of Organic ChemistryMoscowRussia

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