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Reducing the Concepts of Data Science and Machine Learning to Tools for the Bench Chemist

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Machine Learning and Data Science have enjoyed a renaissance due to the availability of increased computational power and larger data sets. Many questions can be now asked and answered, that previously were beyond our scope. This does not translate instantly into new tools that can be used by those not skilled in the field, as many of the issues and traps still exist. In this paper, we look at some of the new tools that we have created, and some of the difficulties that still need to be taken care of during the transition from a project run by an expert, to a tool for the bench chemist.
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Keywords: DATA SCIENCE; MACHINE LEARNING

Document Type: Research Article

Affiliations: 1: Computer-Aided Drug Design, Global Discovery Chemistry, Novartis Institutes of BioMedical Research, CH-4056 Basel;, Email: [email protected] 2: Computer-Aided Drug Design, Global Discovery Chemistry, Novartis Institutes of BioMedical Research, CH-4056 Basel

Publication date: December 1, 2019

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  • International Journal for Chemistry and Official Membership Journal of the Swiss Chemical Society (SCS) and its Divisions

    CHIMIA, a scientific journal for chemistry in the broadest sense, is published 10 times a year and covers the interests of a wide and diverse readership. Contributions from all fields of chemistry and related areas are considered for publication in the form of Review Articles and Notes. A characteristic feature of CHIMIA are the thematic issues, each devoted to an area of great current significance.

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