| Title |
The importance of molecular complexity in the design of screening libraries
|
|---|---|
| Published in |
Perspectives in Drug Discovery and Design, October 2013
|
| DOI | 10.1007/s10822-013-9683-1 |
| Pubmed ID | |
| Authors |
Shahul H. Nilar, Ngai Ling Ma, Thomas H. Keller |
| Abstract |
The one-dimensional model of Hann et al. (JChem Inf Comput Sci 41(3):856–864) has been extended to include reverse binding and wrap-around interaction modes between the protein and ligand to explore the complete combinatorial matrix of molecular recognition. The cumulative distribution function of the Maxwell–Boltzmann distribution has been used to calculate the probability of measuring the sensitivity of the interactions as the asymptotic limits of the distribution better describe the behavior of the interactions under experimental conditions. Based on our model, we hypothesized that molecules of lower complexity are preferred for target based screening campaigns, while augmenting such a library with moieties of moderate complexities maybe better suited for phenotypic screens. The validity of the hypothesis has been assessed via the analysis of the hit rate profiles for four ChemBL datasets for enzymatic and phenotypic screens. |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 25 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 1 | 4% |
| China | 1 | 4% |
| Brazil | 1 | 4% |
| Unknown | 22 | 88% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 6 | 24% |
| Student > Ph. D. Student | 4 | 16% |
| Other | 3 | 12% |
| Student > Master | 3 | 12% |
| Professor > Associate Professor | 2 | 8% |
| Other | 4 | 16% |
| Unknown | 3 | 12% |
| Readers by discipline | Count | As % |
|---|---|---|
| Chemistry | 13 | 52% |
| Agricultural and Biological Sciences | 3 | 12% |
| Computer Science | 2 | 8% |
| Biochemistry, Genetics and Molecular Biology | 1 | 4% |
| Chemical Engineering | 1 | 4% |
| Other | 0 | 0% |
| Unknown | 5 | 20% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. 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 19 October 2013.
All research outputs
#17,348,622
of 25,457,858 outputs
Outputs from Perspectives in Drug Discovery and Design
#736
of 949 outputs
Outputs of similar age
#141,037
of 224,802 outputs
Outputs of similar age from Perspectives in Drug Discovery and Design
#8
of 14 outputs
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