Molecular set transformer: attending to the co-crystals in the Cambridge structural database-Reference-Cited by-同舟云学术

Molecular set transformer: attending to the co-crystals in the Cambridge structural database

Published:2022 Issue:6 Volume:1 Page:834-850
ISSN:2635-098X
Container-title:Digital Discovery
language:en
Short-container-title:Digital Discovery

Author:

Vriza Aikaterini¹²^ORCID,Sovago Ioana³,Widdowson Daniel²⁴,Kurlin Vitaliy²⁴,Wood Peter A.³^ORCID,Dyer Matthew S.¹²^ORCID

Affiliation:

1. Department of Chemistry and Materials Innovation Factory, University of Liverpool, 51 Oxford Street, Liverpool L7 3NY, UK

2. Leverhulme Research Centre for Functional Materials Design, University of Liverpool, Oxford Street, Liverpool L7 3NY, UK

3. Cambridge Crystallographic Data Centre, 12 Union Road, Cambridge CB2 1EZ, UK

4. Materials Innovation Factory and Computer Science Department, University of Liverpool, Liverpool, L69 3BX UK

Abstract

Molecular set transformer is a deep learning architecture for scoring molecular pairs found in co-crystals, whilst tackling the class imbalance problem observed on datasets that include only successful synthetic attempts.

Funder

Royal Academy of Engineering

Cambridge Crystallographic Data Centre

Leverhulme Trust

Engineering and Physical Sciences Research Council

Publisher

Royal Society of Chemistry (RSC)

Link

http://pubs.rsc.org/en/content/articlepdf/2022/DD/D2DD00068G

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