| Title |
LASSO—ligand activity by surface similarity order: a new tool for ligand based virtual screening
|
|---|---|
| Published in |
Perspectives in Drug Discovery and Design, January 2008
|
| DOI | 10.1007/s10822-007-9164-5 |
| Pubmed ID | |
| Authors |
Darryl Reid, Bashir S. Sadjad, Zsolt Zsoldos, Aniko Simon |
| Abstract |
Virtual Ligand Screening (VLS) has become an integral part of the drug discovery process for many pharmaceutical companies. Ligand similarity searches provide a very powerful method of screening large databases of ligands to identify possible hits. If these hits belong to new chemotypes the method is deemed even more successful. eHiTS LASSO uses a new interacting surface point types (ISPT) molecular descriptor that is generated from the 3D structure of the ligand, but unlike most 3D descriptors it is conformation independent. Combined with a neural network machine learning technique, LASSO screens molecular databases at an ultra fast speed of 1 million structures in under 1 min on a standard PC. The results obtained from eHiTS LASSO trained on relatively small training sets of just 2, 4 or 8 actives are presented using the diverse directory of useful decoys (DUD) dataset. It is shown that over a wide range of receptor families, eHiTS LASSO is consistently able to enrich screened databases and provides scaffold hopping ability. |
Mendeley readers
The data shown below were compiled from readership statistics for 57 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 2 | 4% |
| India | 1 | 2% |
| Russia | 1 | 2% |
| Unknown | 53 | 93% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 19 | 33% |
| Student > Ph. D. Student | 16 | 28% |
| Other | 6 | 11% |
| Student > Master | 4 | 7% |
| Professor > Associate Professor | 3 | 5% |
| Other | 5 | 9% |
| Unknown | 4 | 7% |
| Readers by discipline | Count | As % |
|---|---|---|
| Chemistry | 15 | 26% |
| Agricultural and Biological Sciences | 12 | 21% |
| Computer Science | 10 | 18% |
| Biochemistry, Genetics and Molecular Biology | 8 | 14% |
| Pharmacology, Toxicology and Pharmaceutical Science | 4 | 7% |
| Other | 1 | 2% |
| Unknown | 7 | 12% |
Attention Score in Context
This research output has an Altmetric Attention Score of 10. 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 25 June 2019.
All research outputs
#3,769,873
of 25,457,858 outputs
Outputs from Perspectives in Drug Discovery and Design
#129
of 949 outputs
Outputs of similar age
#15,265
of 167,124 outputs
Outputs of similar age from Perspectives in Drug Discovery and Design
#4
of 10 outputs
Altmetric has tracked 25,457,858 research outputs across all sources so far. Compared to these this one has done well and is in the 85th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
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 has done well, scoring higher than 86% of its peers.
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 167,124 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 90% of its contemporaries.
We're also able to compare this research output to 10 others from the same source and published within six weeks on either side of this one. This one has scored higher than 6 of them.