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Mol Inform. 2016 Dec;35(11-12):615-621. doi: 10.1002/minf.201600073. Epub 2016 Jul 28.

BIGCHEM: Challenges and Opportunities for Big Data Analysis in Chemistry.

Author information

1
Helmholtz Zentrum München - German Research Center for Environmental Health (GmbH), Institute of Structural Biology, Ingolstädter Landstraße 1, b. 60w, D-85764, Neuherberg, Germany.
2
BIGCHEM GmbH, Ingolstädter Landstraße 1, b. 60w, D-85764, Neuherberg, Germany.
3
Discovery Sciences, AstraZeneca R&D Gothenburg, Pepparedsleden 1, Mölndal, SE-43183, Sweden.
4
Lead Discovery Center GmbH, Otto-Hahn Strasse 15, Dortmund, 44227, Germany.
5
Department of Chemistry and Biochemistry, University of Bern, Freiestrasse 3, 3012, Bern, Switzerland.

Abstract

The increasing volume of biomedical data in chemistry and life sciences requires the development of new methods and approaches for their handling. Here, we briefly discuss some challenges and opportunities of this fast growing area of research with a focus on those to be addressed within the BIGCHEM project. The article starts with a brief description of some available resources for "Big Data" in chemistry and a discussion of the importance of data quality. We then discuss challenges with visualization of millions of compounds by combining chemical and biological data, the expectations from mining the "Big Data" using advanced machine-learning methods, and their applications in polypharmacology prediction and target de-convolution in phenotypic screening. We show that the efficient exploration of billions of molecules requires the development of smart strategies. We also address the issue of secure information sharing without disclosing chemical structures, which is critical to enable bi-party or multi-party data sharing. Data sharing is important in the context of the recent trend of "open innovation" in pharmaceutical industry, which has led to not only more information sharing among academics and pharma industries but also the so-called "precompetitive" collaboration between pharma companies. At the end we highlight the importance of education in "Big Data" for further progress of this area.

PMID:
27464907
PMCID:
PMC5129546
DOI:
10.1002/minf.201600073
[Indexed for MEDLINE]
Free PMC Article

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