Dataset Information

Computationally derived compound profiling matrices.

ABSTRACT: Aim:Screening 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.

SUBMITTER: Vogt M

PROVIDER: S-EPMC6153460 | biostudies-literature | 2018 Sep

REPOSITORIES: biostudies-literature

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Computationally derived compound profiling matrices.

Vogt Martin M Jasial Swarit S Bajorath Jürgen J

Future science OA 20180724 8

<h4>Aim</h4>Screening 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.<h4>Methodology</h4>A new algorithm was used to derive complete profiling matrices from assay data.<h4>Data</h4>Two profiling matrices were derived from confirmatory assays containing 53 different targets and 109,925 and 14 ...[more]

PMID: 30271615

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Computationally derived compound profiling matrices.

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Computationally derived compound profiling matrices.

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