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Benchmark of four popular virtual screening programs: construction of the active/decoy dataset remains a major determinant of measured performance

Overview of attention for article published in Journal of Cheminformatics, October 2016
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Title
Benchmark of four popular virtual screening programs: construction of the active/decoy dataset remains a major determinant of measured performance
Published in
Journal of Cheminformatics, October 2016
DOI 10.1186/s13321-016-0167-x
Pubmed ID
Authors

Ludovic Chaput, Juan Martinez-Sanz, Nicolas Saettel, Liliane Mouawad

Abstract

In a structure-based virtual screening, the choice of the docking program is essential for the success of a hit identification. Benchmarks are meant to help in guiding this choice, especially when undertaken on a large variety of protein targets. Here, the performance of four popular virtual screening programs, Gold, Glide, Surflex and FlexX, is compared using the Directory of Useful Decoys-Enhanced database (DUD-E), which includes 102 targets with an average of 224 ligands per target and 50 decoys per ligand, generated to avoid biases in the benchmarking. Then, a relationship between these program performances and the properties of the targets or the small molecules was investigated. The comparison was based on two metrics, with three different parameters each. The BEDROC scores with α = 80.5, indicated that, on the overall database, Glide succeeded (score > 0.5) for 30 targets, Gold for 27, FlexX for 14 and Surflex for 11. The performance did not depend on the hydrophobicity nor the openness of the protein cavities, neither on the families to which the proteins belong. However, despite the care in the construction of the DUD-E database, the small differences that remain between the actives and the decoys likely explain the successes of Gold, Surflex and FlexX. Moreover, the similarity between the actives of a target and its crystal structure ligand seems to be at the basis of the good performance of Glide. When all targets with significant biases are removed from the benchmarking, a subset of 47 targets remains, for which Glide succeeded for only 5 targets, Gold for 4 and FlexX and Surflex for 2. The performance dramatic drop of all four programs when the biases are removed shows that we should beware of virtual screening benchmarks, because good performances may be due to wrong reasons. Therefore, benchmarking would hardly provide guidelines for virtual screening experiments, despite the tendency that is maintained, i.e., Glide and Gold display better performance than FlexX and Surflex. We recommend to always use several programs and combine their results. Graphical AbstractSummary of the results obtained by virtual screening with the four programs, Glide, Gold, Surflex and FlexX, on the 102 targets of the DUD-E database. The percentage of targets with successful results, i.e., with BDEROC(α = 80.5) > 0.5, when the entire database is considered are in Blue, and when targets with biased chemical libraries are removed are in Red.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 135 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Spain 1 <1%
Denmark 1 <1%
Germany 1 <1%
Unknown 132 98%

Demographic breakdown

Readers by professional status Count As %
Researcher 33 24%
Student > Ph. D. Student 28 21%
Student > Master 13 10%
Student > Bachelor 10 7%
Student > Doctoral Student 6 4%
Other 16 12%
Unknown 29 21%
Readers by discipline Count As %
Chemistry 30 22%
Biochemistry, Genetics and Molecular Biology 19 14%
Agricultural and Biological Sciences 15 11%
Computer Science 15 11%
Pharmacology, Toxicology and Pharmaceutical Science 13 10%
Other 8 6%
Unknown 35 26%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 29 July 2017.
All research outputs
#14,909,862
of 24,143,470 outputs
Outputs from Journal of Cheminformatics
#743
of 891 outputs
Outputs of similar age
#178,876
of 320,159 outputs
Outputs of similar age from Journal of Cheminformatics
#23
of 26 outputs
Altmetric has tracked 24,143,470 research outputs across all sources so far. This one is in the 37th percentile – i.e., 37% of other outputs scored the same or lower than it.
So far Altmetric has tracked 891 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.7. This one is in the 15th percentile – i.e., 15% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 320,159 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 42nd percentile – i.e., 42% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 26 others from the same source and published within six weeks on either side of this one. This one is in the 15th percentile – i.e., 15% of its contemporaries scored the same or lower than it.