| Title |
Community benchmarks for virtual screening
|
|---|---|
| Published in |
Perspectives in Drug Discovery and Design, February 2008
|
| DOI | 10.1007/s10822-008-9189-4 |
| Pubmed ID | |
| Authors |
John J. Irwin |
| Abstract |
Ligand enrichment among top-ranking hits is a key metric of virtual screening. To avoid bias, decoys should resemble ligands physically, so that enrichment is not attributable to simple differences of gross features. We therefore created a directory of useful decoys (DUD) by selecting decoys that resembled annotated ligands physically but not topologically to benchmark docking performance. DUD has 2950 annotated ligands and 95,316 property-matched decoys for 40 targets. It is by far the largest and most comprehensive public data set for benchmarking virtual screening programs that I am aware of. This paper outlines several ways that DUD can be improved to provide better telemetry to investigators seeking to understand both the strengths and the weaknesses of current docking methods. I also highlight several pitfalls for the unwary: a risk of over-optimization, questions about chemical space, and the proper scope for using DUD. Careful attention to both the composition of benchmarks and how they are used is essential to avoid being misled by overfitting and bias. |
Mendeley readers
The data shown below were compiled from readership statistics for 220 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Germany | 4 | 2% |
| Brazil | 4 | 2% |
| Spain | 3 | 1% |
| France | 2 | <1% |
| United States | 2 | <1% |
| China | 2 | <1% |
| Czechia | 1 | <1% |
| Singapore | 1 | <1% |
| India | 1 | <1% |
| Other | 4 | 2% |
| Unknown | 196 | 89% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 45 | 20% |
| Student > Ph. D. Student | 40 | 18% |
| Student > Master | 30 | 14% |
| Student > Bachelor | 21 | 10% |
| Professor > Associate Professor | 14 | 6% |
| Other | 43 | 20% |
| Unknown | 27 | 12% |
| Readers by discipline | Count | As % |
|---|---|---|
| Chemistry | 82 | 37% |
| Agricultural and Biological Sciences | 34 | 15% |
| Computer Science | 18 | 8% |
| Medicine and Dentistry | 15 | 7% |
| Biochemistry, Genetics and Molecular Biology | 12 | 5% |
| Other | 23 | 10% |
| Unknown | 36 | 16% |
Attention Score in Context
This research output has an Altmetric Attention Score of 3. 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 20 January 2024.
All research outputs
#8,571,053
of 25,457,858 outputs
Outputs from Perspectives in Drug Discovery and Design
#420
of 949 outputs
Outputs of similar age
#47,602
of 175,260 outputs
Outputs of similar age from Perspectives in Drug Discovery and Design
#9
of 14 outputs
Altmetric has tracked 25,457,858 research outputs across all sources so far. This one is in the 43rd percentile – i.e., 43% of other outputs scored the same or lower than it.
So far Altmetric has tracked 949 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.3. This one is in the 30th percentile – i.e., 30% of its peers scored the same or lower than it.
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We're also able to compare this research output to 14 others from the same source and published within six weeks on either side of this one. This one is in the 7th percentile – i.e., 7% of its contemporaries scored the same or lower than it.