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

Richard A. Lewis; Peter Ertl; Nadine Schneider; Nikolaus Stiefl

doi:10.2533/chimia.2019.1001

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

Authors

Richard A. Lewis Computer-Aided Drug Design, Global Discovery Chemistry, Novartis Institutes of BioMedical Research, CH-4056 Basel;, Email: richard.lewis@novartis.com
Peter Ertl Computer-Aided Drug Design, Global Discovery Chemistry, Novartis Institutes of BioMedical Research, CH-4056 Basel
Nadine Schneider Computer-Aided Drug Design, Global Discovery Chemistry, Novartis Institutes of BioMedical Research, CH-4056 Basel
Nikolaus Stiefl Computer-Aided Drug Design, Global Discovery Chemistry, Novartis Institutes of BioMedical Research, CH-4056 Basel

DOI:

https://doi.org/10.2533/chimia.2019.1001

PMID:

31883551

Keywords:

Data science, Machine learning

Abstract

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.