| dc.contributor.author | Pattanaik, Lagnajit | |
| dc.contributor.author | Coley, Connor Wilson | |
| dc.date.accessioned | 2021-09-03T16:33:12Z | |
| dc.date.available | 2021-09-03T16:33:12Z | |
| dc.date.issued | 2020-05 | |
| dc.identifier.issn | 2451-9294 | |
| dc.identifier.uri | https://hdl.handle.net/1721.1/131240 | |
| dc.description.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. | en_US |
| dc.language.iso | en | |
| dc.publisher | Elsevier BV | en_US |
| dc.relation.isversionof | http://dx.doi.org/10.1016/j.chempr.2020.05.002 | en_US |
| dc.rights | Creative Commons Attribution-NonCommercial-NoDerivs License | en_US |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | en_US |
| dc.source | Elsevier | en_US |
| dc.title | Molecular Representation: Going Long on Fingerprints | en_US |
| dc.type | Article | en_US |
| dc.identifier.citation | Pattanaik, Lagnajit and Connor W. Coley. "Molecular Representation: Going Long on Fingerprints." Chem 6, 6 (June 2020): 1204-1207. © 2020 Elsevier Inc | en_US |
| dc.contributor.department | Massachusetts Institute of Technology. Department of Chemical Engineering | en_US |
| dc.relation.journal | Chem | en_US |
| dc.eprint.version | Author's final manuscript | en_US |
| dc.type.uri | http://purl.org/eprint/type/JournalArticle | en_US |
| eprint.status | http://purl.org/eprint/status/PeerReviewed | en_US |
| dc.date.updated | 2021-09-03T14:55:44Z | |
| dspace.orderedauthors | Pattanaik, L; Coley, CW | en_US |
| dspace.date.submission | 2021-09-03T14:55:48Z | |
| mit.journal.volume | 6 | en_US |
| mit.journal.issue | 6 | en_US |
| mit.license | PUBLISHER_CC | |
| mit.metadata.status | Complete | en_US |
