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Computationally derived compound profiling matrices

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...

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Detalles Bibliográficos
Autores principales: Vogt, Martin, Jasial, Swarit, Bajorath, Jürgen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Future Science Ltd 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6153460/
https://www.ncbi.nlm.nih.gov/pubmed/30271615
http://dx.doi.org/10.4155/fsoa-2018-0050
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author Vogt, Martin
Jasial, Swarit
Bajorath, Jürgen
author_facet Vogt, Martin
Jasial, Swarit
Bajorath, Jürgen
author_sort Vogt, Martin
collection PubMed
description 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.
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spelling pubmed-61534602018-09-28 Computationally derived compound profiling matrices Vogt, Martin Jasial, Swarit Bajorath, Jürgen Future Sci OA Data Note 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. Future Science Ltd 2018-07-24 /pmc/articles/PMC6153460/ /pubmed/30271615 http://dx.doi.org/10.4155/fsoa-2018-0050 Text en © 2018 Jürgen Bajorath This work is licensed under a Creative Commons Attribution 4.0 License (http://creativecommons.org/licenses/by/4.0/)
spellingShingle Data Note
Vogt, Martin
Jasial, Swarit
Bajorath, Jürgen
Computationally derived compound profiling matrices
title Computationally derived compound profiling matrices
title_full Computationally derived compound profiling matrices
title_fullStr Computationally derived compound profiling matrices
title_full_unstemmed Computationally derived compound profiling matrices
title_short Computationally derived compound profiling matrices
title_sort computationally derived compound profiling matrices
topic Data Note
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6153460/
https://www.ncbi.nlm.nih.gov/pubmed/30271615
http://dx.doi.org/10.4155/fsoa-2018-0050
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