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Awale M, Visini R, Probst D, et al. Chemical Space: Big Data Challenge for Molecular Diversity. Chimia (Aarau). 2017;71(10):661-666doi: 10.2533/chimia.2017.661.
Awale, M., Visini, R., Probst, D., Arús-Pous, J., & Reymond, J. L. (2017). Chemical Space: Big Data Challenge for Molecular Diversity. Chimia, 71(10), 661-666. https://doi.org/10.2533/chimia.2017.661
Awale, Mahendra, et al. "Chemical Space: Big Data Challenge for Molecular Diversity." Chimia vol. 71,10 (2017): 661-666. doi: https://doi.org/10.2533/chimia.2017.661
Awale M, Visini R, Probst D, Arús-Pous J, Reymond JL. Chemical Space: Big Data Challenge for Molecular Diversity. Chimia (Aarau). 2017 Oct 25;71(10):661-666. doi: 10.2533/chimia.2017.661. PMID: 29070411.
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Chimia (Aarau). 2017 Oct 25;71(10):661-666. doi: 10.2533/chimia.2017.661.

Chemical Space: Big Data Challenge for Molecular Diversity.

Chimia

Mahendra Awale, Ricardo Visini, Daniel Probst, Josep Arús-Pous, Jean-Louis Reymond

Affiliations

  1. Department of Chemistry and Biochemistry National Center of Competence in Research NCCR TransCure University of Bern Freiestrasse 3, CH-3012 Bern.
  2. Department of Chemistry and Biochemistry National Center of Competence in Research NCCR TransCure University of Bern Freiestrasse 3, CH-3012 Bern;, Email: [email protected].

PMID: 29070411 DOI: 10.2533/chimia.2017.661

Abstract

Chemical space describes all possible molecules as well as multi-dimensional conceptual spaces representing the structural diversity of these molecules. Part of this chemical space is available in public databases ranging from thousands to billions of compounds. Exploiting these databases for drug discovery represents a typical big data problem limited by computational power, data storage and data access capacity. Here we review recent developments of our laboratory, including progress in the chemical universe databases (GDB) and the fragment subset FDB-17, tools for ligand-based virtual screening by nearest neighbor searches, such as our multi-fingerprint browser for the ZINC database to select purchasable screening compounds, and their application to discover potent and selective inhibitors for calcium channel TRPV6 and Aurora A kinase, the polypharmacology browser (PPB) for predicting off-target effects, and finally interactive 3D-chemical space visualization using our online tools WebDrugCS and WebMolCS. All resources described in this paper are available for public use at www.gdb.unibe.ch.

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