| Title |
Enhancement of chemical rules for predicting compound reactivity towards protein thiol groups
|
|---|---|
| Published in |
Perspectives in Drug Discovery and Design, March 2007
|
| DOI | 10.1007/s10822-007-9109-z |
| Pubmed ID | |
| Authors |
James T. Metz, Jeffrey R. Huth, Philip J. Hajduk |
| Abstract |
Non-specific chemical modification of protein thiol groups continues to be a significant source of false positive hits from high-throughput screening campaigns and can even plague certain protein targets and chemical series well into lead optimization. While experimental tools exist to assess the risk and promiscuity associated with the chemical reactivity of existing compounds, computational tools are desired that can reliably identify substructures that are associated with chemical reactivity to aid in triage of HTS hit lists, external compound purchases, and library design. Here we describe a Bayesian classification model derived from more than 8,800 compounds that have been experimentally assessed for their potential to covalently modify protein targets. The resulting model can be implemented in the large-scale assessment of compound libraries for purchase or design. In addition, the individual substructures identified as highly reactive in the model can be used as look-up tables to guide chemists during hit-to-lead and lead optimization campaigns. |
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 | 1 | 2% |
| Unknown | 56 | 98% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 19 | 33% |
| Student > Ph. D. Student | 9 | 16% |
| Other | 6 | 11% |
| Student > Bachelor | 5 | 9% |
| Professor > Associate Professor | 4 | 7% |
| Other | 7 | 12% |
| Unknown | 7 | 12% |
| Readers by discipline | Count | As % |
|---|---|---|
| Chemistry | 23 | 40% |
| Agricultural and Biological Sciences | 6 | 11% |
| Pharmacology, Toxicology and Pharmaceutical Science | 6 | 11% |
| Biochemistry, Genetics and Molecular Biology | 6 | 11% |
| Philosophy | 2 | 4% |
| Other | 5 | 9% |
| Unknown | 9 | 16% |
Attention Score in Context
This research output has an Altmetric Attention Score of 7. 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 24 January 2017.
All research outputs
#5,447,003
of 25,457,297 outputs
Outputs from Perspectives in Drug Discovery and Design
#244
of 949 outputs
Outputs of similar age
#17,532
of 89,946 outputs
Outputs of similar age from Perspectives in Drug Discovery and Design
#2
of 11 outputs
Altmetric has tracked 25,457,297 research outputs across all sources so far. Compared to these this one has done well and is in the 78th 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 gotten more attention than average, scoring higher than 73% 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 89,946 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 80% of its contemporaries.
We're also able to compare this research output to 11 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 81% of its contemporaries.