Molecular Representation: Going Long on Fingerprints

Author(s)
Pattanaik, Lagnajit; Coley, Connor Wilson
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Abstract
Machine learning for chemistry requires a strategy for representing (featurizing) molecules. In this issue of Chem, Sandfort et al. describe an approach that concatenates 24 fingerprint representations into 71,375-dimensional vectors, which are then used for a variety of supervised learning tasks related to chemical reactivity.
Date issued
2020-05
URI
https://hdl.handle.net/1721.1/131240
Department
Massachusetts Institute of Technology. Department of Chemical Engineering
Journal
Chem
Publisher
Elsevier BV
Citation
Pattanaik, Lagnajit and Connor W. Coley. "Molecular Representation: Going Long on Fingerprints." Chem 6, 6 (June 2020): 1204-1207. © 2020 Elsevier Inc
Version: Author's final manuscript
ISSN
2451-9294
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