title: Data-driven medicinal chemistry in the era of big data.
abstract: Science, and the way we undertake research, is changing. The increasing rate of data generation across all scientific disciplines is providing incredible opportunities for data-driven research, with the potential to transform our current practices. The exploitation of so-called 'big data' will enable us to undertake research projects never previously possible but should also stimulate a re-evaluation of all our data practices. Data-driven medicinal chemistry approaches have the potential to improve decision making in drug discovery projects, providing that all researchers embrace the role of 'data scientist' and uncover the meaningful relationships and patterns in available data.
authors.affiliation: Netherlands eScience Center, Amsterdam, The Netherlands; Computational Drug Discovery Group, Radboud University, Nijmegen, The Netherlands. Electronic address: s.lusher@esciencecenter.nl.
authors.forename: Scott J
authors.initials: SJ
authors.lastname: Lusher
authors.affiliation: Computational Drug Discovery Group, Radboud University, Nijmegen, The Netherlands; Bioaxis Research, Pivot Park, Oss, The Netherlands.
authors.forename: Ross
authors.initials: R
authors.lastname: McGuire
authors.affiliation: Netherlands eScience Center, Amsterdam, The Netherlands.
authors.forename: René C
authors.initials: RC
authors.lastname: van Schaik
authors.affiliation: Bayer Crop Science, Monheim am Rhein, Germany.
authors.forename: C David
authors.initials: CD
authors.lastname: Nicholson
authors.affiliation: Netherlands eScience Center, Amsterdam, The Netherlands; Computational Drug Discovery Group, Radboud University, Nijmegen, The Netherlands.
authors.forename: Jacob
authors.initials: J
authors.lastname: de Vlieg
mesh_terms: D000818:Animals; D002626:Chemistry, Pharmaceutical / Q000379:methods / Q000639:trends*; D055808:Drug Discovery / Q000379:methods / Q000639:trends*; D006801:Humans; D013223:Statistics as Topic / Q000379:methods / Q000639:trends*