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A reliable computational workflow for the selection of optimal screening libraries

Overview of attention for article published in Journal of Cheminformatics, December 2015
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  • Above-average Attention Score compared to outputs of the same age and source (53rd percentile)

Mentioned by

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5 tweeters

Citations

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11 Dimensions

Readers on

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55 Mendeley
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1 CiteULike
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Title
A reliable computational workflow for the selection of optimal screening libraries
Published in
Journal of Cheminformatics, December 2015
DOI 10.1186/s13321-015-0108-0
Pubmed ID
Authors

Yocheved Gilad, Katalin Nadassy, Hanoch Senderowitz

Abstract

The experimental screening of compound collections is a common starting point in many drug discovery projects. Successes of such screening campaigns critically depend on the quality of the screened library. Many libraries are currently available from different vendors yet the selection of the optimal screening library for a specific project is challenging. We have devised a novel workflow for the rational selection of project-specific screening libraries. The workflow accepts as input a set of virtual candidate libraries and applies the following steps to each library: (1) data curation; (2) assessment of ADME/T profile; (3) assessment of the number of promiscuous binders/frequent HTS hitters; (4) assessment of internal diversity; (5) assessment of similarity to known active compound(s) (optional); (6) assessment of similarity to in-house or otherwise accessible compound collections (optional). For ADME/T profiling, Lipinski's and Veber's rule-based filters were implemented and a new blood brain barrier permeation model was developed and validated (85 and 74 % success rate for training set and test set, respectively). Diversity and similarity descriptors which demonstrated best performances in terms of their ability to select either diverse or focused sets of compounds from three databases (Drug Bank, CMC and CHEMBL) were identified and used for diversity and similarity assessments. The workflow was used to analyze nine common screening libraries available from six vendors. The results of this analysis are reported for each library providing an assessment of its quality. Furthermore, a consensus approach was developed to combine the results of these analyses into a single score for selecting the optimal library under different scenarios. We have devised and tested a new workflow for the rational selection of screening libraries under different scenarios. The current workflow was implemented using the Pipeline Pilot software yet due to the usage of generic components, it can be easily adapted and reproduced by computational groups interested in rational selection of screening libraries. Furthermore, the workflow could be readily modified to include additional components. This workflow has been routinely used in our laboratory for the selection of libraries in multiple projects and consistently selects libraries which are well balanced across multiple parameters.Graphical abstract.

Twitter Demographics

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Mendeley readers

The data shown below were compiled from readership statistics for 55 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 2 4%
Switzerland 1 2%
Unknown 52 95%

Demographic breakdown

Readers by professional status Count As %
Researcher 12 22%
Student > Ph. D. Student 8 15%
Student > Bachelor 8 15%
Student > Master 7 13%
Other 5 9%
Other 9 16%
Unknown 6 11%
Readers by discipline Count As %
Chemistry 13 24%
Biochemistry, Genetics and Molecular Biology 8 15%
Agricultural and Biological Sciences 6 11%
Pharmacology, Toxicology and Pharmaceutical Science 4 7%
Computer Science 4 7%
Other 10 18%
Unknown 10 18%

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 01 September 2018.
All research outputs
#3,974,439
of 13,454,271 outputs
Outputs from Journal of Cheminformatics
#335
of 544 outputs
Outputs of similar age
#105,621
of 361,149 outputs
Outputs of similar age from Journal of Cheminformatics
#32
of 69 outputs
Altmetric has tracked 13,454,271 research outputs across all sources so far. This one has received more attention than most of these and is in the 70th percentile.
So far Altmetric has tracked 544 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 9.2. This one is in the 38th percentile – i.e., 38% 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 361,149 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 70% of its contemporaries.
We're also able to compare this research output to 69 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 53% of its contemporaries.