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CoPriNet: graph neural networks provide accurate and rapid compound price prediction for molecule prioritisation

Abstract:
CoPriNet can predict compound prices after being trained on 6M pairs of compounds and prices collected from the Mcule catalogue.
Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1039/d2dd00071g

Authors

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Institution:
University of Oxford
Role:
Author
ORCID:
0000-0001-6156-3542
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Role:
Author
ORCID:
0000-0003-3366-4009
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Role:
Author
ORCID:
0000-0002-8090-0732
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Role:
Author
ORCID:
0009-0001-3935-9083
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Role:
Author
ORCID:
0000-0002-9616-3108


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Funder identifier:
10.13039/501100000833
Grant:
M940


Publisher:
Royal Society of Chemistry
Journal:
Digital Discovery More from this journal
Volume:
2
Issue:
1
Pages:
103-111
Publication date:
2022-11-28
DOI:
EISSN:
2635-098X
ISSN:
2635-098X


Language:
English
Keywords:
Pubs id:
1312437
Local pid:
pubs:1312437
Source identifiers:
W4311172319
Deposit date:
2026-04-30
ARK identifier:
This ORA record was generated from metadata provided by an external service. It has not been edited by the ORA Team.

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