ArticleFuture science OA2018
Computationally derived compound profiling matrices.
Article in Future science OA, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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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.
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Who cites it
1 citing paper in PubMed.
- Accurate prediction of activity cliff compounds based on bioactivity profiles depends on assay nearest neighbor relationships.Journal of cheminformatics · 2026Article
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
aimScreening of compounds against panels of targets yields profiling matrices. Such matrices are excellent test cases for the analysis and prediction of ligand-target interactions. We made three matrices freely available that were extracted from public screening data. METHODOLOGY: A new algorithm was used to derive complete profiling matrices from assay data. DATA: Two profiling matrices were derived from confirmatory assays containing 53 different targets and 109,925 and 143,310 distinct compounds, respectively. A third matrix was extracted from primary screening assays covering 171 different targets and 224,251 compounds. NEXT STEPS: Profiling matrices can be used to test computational chemogenomics methods for their ability to predict ligand-target pairs. Additional matrices will be generated for individual target families.
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Registered trials
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