Limitations of representation learning in small molecule property prediction
Ana Laura Dias,
Latimah Bustillo and
Tiago Rodrigues ()
Additional contact information
Ana Laura Dias: Universidade de Lisboa
Latimah Bustillo: Universidade de Lisboa
Tiago Rodrigues: Universidade de Lisboa
Nature Communications, 2023, vol. 14, issue 1, 1-2
Abstract:
Representation learning is making inroads into drug discovery. A study in Nature Communications emphasizes multiple limitations in property prediction. The results suggest that continued research and improvements are required for this specific area that coalesces machine learning and molecular medicine.
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:
Downloads: (external link)
https://www.nature.com/articles/s41467-023-41967-3 Abstract (text/html)
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-41967-3
Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/
DOI: 10.1038/s41467-023-41967-3
Access Statistics for this article
Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie
More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().