Learning to Make Chemical Predictions: the Interplay of Feature Representation, Data, and Machine Learning Methods.

Chem
Mojtaba HaghighatlariTeresa Head-Gordon

Abstract

Recently supervised machine learning has been ascending in providing new predictive approaches for chemical, biological and materials sciences applications. In this Perspective we focus on the interplay of machine learning method with the chemically motivated descriptors and the size and type of data sets needed for molecular property prediction. Using Nuclear Magnetic Resonance chemical shift prediction as an example, we demonstrate that success is predicated on the choice of feature extracted or real-space representations of chemical structures, whether the molecular property data is abundant and/or experimentally or computationally derived, and how these together will influence the correct choice of popular machine learning methods drawn from deep learning, random forests, or kernel methods.

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Citations

Jul 15, 2020·Angewandte Chemie·Frank Würthner
Jan 14, 2021·IScience·Murat Cihan SorkunSüleyman Er
Nov 20, 2020·Chemical Reviews·Julia Westermayr, Philipp Marquetand
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Jun 5, 2021·The Journal of Physical Chemistry Letters·Anna PavlovaJames C Gumbart
Jun 11, 2021·Molecular Diversity·Anita RáczKároly Héberger
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Jul 9, 2021·The Journal of Organic Chemistry·A Filipa AlmeidaTiago Rodrigues
Aug 10, 2021·The Journal of Physical Chemistry. B·Musahid AhmedKevin R Wilson
Aug 14, 2021·Chemical Reviews·Bing Huang, O Anatole von Lilienfeld

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