A Consensus Compound/Bioactivity Dataset for Data-Driven Drug Design and Chemogenomics |
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| Authors: | Laura Isigkeit Apirat Chaikuad Daniel Merk |
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| Affiliation: | 1.Institute of Pharmaceutical Chemistry, Goethe University Frankfurt, 60438 Frankfurt, Germany; (L.I.); (A.C.);2.Structural Genomics Consortium, BMLS, Goethe University Frankfurt, 60438 Frankfurt, Germany;3.Department of Pharmacy, Ludwig Maximilian University of Munich, 81377 Munich, Germany |
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| Abstract: | Publicly available compound and bioactivity databases provide an essential basis for data-driven applications in life-science research and drug design. By analyzing several bioactivity repositories, we discovered differences in compound and target coverage advocating the combined use of data from multiple sources. Using data from ChEMBL, PubChem, IUPHAR/BPS, BindingDB, and Probes & Drugs, we assembled a consensus dataset focusing on small molecules with bioactivity on human macromolecular targets. This allowed an improved coverage of compound space and targets, and an automated comparison and curation of structural and bioactivity data to reveal potentially erroneous entries and increase confidence. The consensus dataset comprised of more than 1.1 million compounds with over 10.9 million bioactivity data points with annotations on assay type and bioactivity confidence, providing a useful ensemble for computational applications in drug design and chemogenomics. |
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| Keywords: | big data data curation medicinal chemistry machine learning de novo design |
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