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Glass box and black box machine learning approaches to exploit compositional descriptors of molecules in drug discovery and aid the medicinal chemist

Abstract:

The synthetic medicinal chemist plays a vital role in drug discovery. Today there are AI tools to guide next syntheses, but many are “Black Boxes” (BB). One learns little more than the prediction made. There are now also AI methods emphasizing visibility and “explainability” (thus explainable AI or XAI) that could help when “compositional data” are used, but they often still start from seemingly arbitrary learned weights and lack familiar probabilistic measures based on observation and counti...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1002/cmdc.202400169

Authors


More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Chemistry
Role:
Author
ORCID:
0000-0001-9651-6308
Publisher:
Wiley
Journal:
ChemMedChem More from this journal
Article number:
e202400169
Place of publication:
Germany
Publication date:
2024-06-04
Acceptance date:
2024-06-03
DOI:
EISSN:
1860-7187
ISSN:
1860-7179
Pmid:
38837320
Language:
English
Keywords:
Pubs id:
2007877
Local pid:
pubs:2007877
Deposit date:
2024-07-09

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