Molecular Representation: Going Long on Fingerprints
Name
Going Long on Fingerprints - Coley.pdf
Description
Accepted version
Size
568.61 KB
Format
Adobe PDF
Checksum (MD5)
5928e41bd6bdd12f28b583faadcd27e0
Author(s)
Pattanaik, Lagnajit
Coley, Connor Wilson
Date Issued
May 2020
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
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.
MIT Department
Massachusetts Institute of Technology. Department of Chemical Engineering
Terms of Use
Creative Commons Attribution-NonCommercial-NoDerivs License
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1016/j.chempr.2020.05.002
