Evidence map›Paper›PMID 26807156›Full record

ArticleJournal of cheminformatics2016

vSDC: a method to improve early recognition in virtual screening when limited experimental resources are available.

Ludovic Chaput, Juan Martinez-Sanz, Eric Quiniou, Pascal Rigolet, Nicolas Saettel, Liliane Mouawad

Abstract read
In one paragraph

Article in Journal of cheminformatics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

8 citing papers in PubMed.

  1. Article
  2. Negative Image-Based Screening: Rigid Docking Using Cavity Information.Methods in molecular biology (Clifton, N.J.) · 2021
    Article
  3. Article
  4. Review
  5. Key Topics in Molecular Docking for Drug Design.International journal of molecular sciences · 2019
    Review
  6. Review
  7. Article
  8. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Ludovic ChaputChemistry, Modelling and Imaging for Biology (CMIB), Centre de Recherche, Institut Curie-PSL Research University, Bâtiment 112, Centre Universitaire, 91405 Orsay Cedex, France ; Paris-Sud University, Orsay, France ; Inserm, U1196, Orsay, France ; CNRS, UMR 9187, Orsay, France.
Juan Martinez-SanzChemistry, Modelling and Imaging for Biology (CMIB), Centre de Recherche, Institut Curie-PSL Research University, Bâtiment 112, Centre Universitaire, 91405 Orsay Cedex, France ; Paris-Sud University, Orsay, France ; Inserm, U1196, Orsay, France ; CNRS, UMR 9187, Orsay, France.
Eric QuiniouChemistry, Modelling and Imaging for Biology (CMIB), Centre de Recherche, Institut Curie-PSL Research University, Bâtiment 112, Centre Universitaire, 91405 Orsay Cedex, France ; Paris-Sud University, Orsay, France ; Inserm, U1196, Orsay, France ; CNRS, UMR 9187, Orsay, France.
Pascal RigoletChemistry, Modelling and Imaging for Biology (CMIB), Centre de Recherche, Institut Curie-PSL Research University, Bâtiment 112, Centre Universitaire, 91405 Orsay Cedex, France ; Paris-Sud University, Orsay, France ; Inserm, U1196, Orsay, France ; CNRS, UMR 9187, Orsay, France.
Nicolas SaettelChemistry, Modelling and Imaging for Biology (CMIB), Centre de Recherche, Institut Curie-PSL Research University, Bâtiment 112, Centre Universitaire, 91405 Orsay Cedex, France ; Inserm, U1196, Orsay, France ; CNRS, UMR 9187, Orsay, France ; School of Pharmacy, University of Caen, Normandy, Boulevard Becquerel, Caen, 14032 France.
Liliane MouawadChemistry, Modelling and Imaging for Biology (CMIB), Centre de Recherche, Institut Curie-PSL Research University, Bâtiment 112, Centre Universitaire, 91405 Orsay Cedex, France ; Paris-Sud University, Orsay, France ; Inserm, U1196, Orsay, France ; CNRS, UMR 9187, Orsay, France.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundIn drug design, one may be confronted to the problem of finding hits for targets for which no small inhibiting molecules are known and only low-throughput experiments are available (like ITC or NMR studies), two common difficulties encountered in a typical academic setting. Using a virtual screening strategy like docking can alleviate some of the problems and save a considerable amount of time by selecting only top-ranking molecules, but only if the method is very efficient, i.e. when a good proportion of actives are found in the 1-10 % best ranked molecules.

resultsThe use of several programs (in our study, Gold, Surflex, FlexX and Glide were considered) shows a divergence of the results, which presents a difficulty in guiding the experiments. To overcome this divergence and increase the yield of the virtual screening, we created the standard deviation consensus (SDC) and variable SDC (vSDC) methods, consisting of the intersection of molecule sets from several virtual screening programs, based on the standard deviations of their ranking distributions.

conclusionsSDC allowed us to find hits for two new protein targets by testing only 9 and 11 small molecules from a chemical library of circa 15,000 compounds. Furthermore, vSDC, when applied to the 102 proteins of the DUD-E benchmarking database, succeeded in finding more hits than any of the four isolated programs for 13-60 % of the targets. In addition, when only 10 molecules of each of the 102 chemical libraries were considered, vSDC performed better in the number of hits found, with an improvement of 6-24 % over the 10 best-ranked molecules given by the individual docking programs.Graphical abstractIn drug design, for a given target and a given chemical library, the results obtained with different virtual screening programs are divergent. So how to rationally guide the experimental tests, especially when only a few number of experiments can be made? The variable Standard Deviation Consensus (vSDC) method was developed to answer this issue. Left panel the vSDC principle consists of intersecting molecule sets, chosen on the basis of the standard deviations of their ranking distributions, obtained from various virtual screening programs. In this study Glide, Gold, FlexX and Surflex were used and tested on the 102 targets of the DUD-E database. Right panel Comparison of the average percentage of hits found with vSDC and each of the four programs, when only 10 molecules from each of the 102 chemical libraries of the DUD-E database were considered. On average, vSDC was capable of finding 38 % of the findable hits, against 34 % for Glide, 32 % for Gold, 16 % for FlexX and 14 % for Surflex, showing that with vSDC, it was possible to overcome the unpredictability of the virtual screening results and to improve them.

Indexed as

CalcineurinCdk2DUD-EFlexXGlideGoldStandard deviation consensusSurflexVirtual screeningvSDC

Identifiers

PMID26807156
PMCPMC4722699

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.