ArticleACS medicinal chemistry letters2010
Design of a high fragment efficiency library by molecular graph theory.
Article in ACS medicinal chemistry letters, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed, 14 citations in OpenAlex.
- Medicinal chemistry for 2020.Future medicinal chemistry · 2011Article
- Visualisation of the chemical space of fragments, lead-like and drug-like molecules in PubChem.Journal of computer-aided molecular design · 2011Article
- Visualisation and subsets of the chemical universe database GDB-13 for virtual screening.Journal of computer-aided molecular design · 2011Article
- Design of a fragment library that maximally represents available chemical space.Journal of computer-aided molecular design · 2011Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Molecular graph theory was used to design a unique and diverse, high-efficiency fragment screening collection. A data set retrieved from the annotated database AurSCOPE GPS was used as the reference set, and the GDB-13 database, a virtual library of enumerated organic molecules, was used as a source for the fragment selection. The data graph collection of Discngine as implemented in PipelinePilot was applied to perform the graph pharmacophore similarity matching between the reference and the GDB-13 data sets, leading to the ultimate fragment screening library. The relevance of this unique fragment collection was demonstrated by means of a virtual screening exercise using human trypsin as a test case. Several novel entities with high similarity to known trypsin inhibitors were identified in the in silico exercise. The application of this unique, high fragment efficiency collection to other protein targets in the framework of fragment-based drug discovery is warranted.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.