Conformal efficiency as a metric for comparative model assessment befitting federated learning

Conformal efficiency as a metric for comparative model assessment befitting federated learning

In a drug discovery setting, pharmaceutical companies own substantial but confidential datasets. The MELLODDY project developed a privacy-preserving federated machine learning solution and deployed it at an unprecedented scale. Each partner built models for their own private assays that benefitted f...

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Bibliographic Details
Main Authors: Wouter Heyndrickx, Adam Arany, Jaak Simm, Anastasia Pentina, Noé Sturm, Lina Humbeck, Lewis Mervin, Adam Zalewski, Martijn Oldenhof, Peter Schmidtke, Lukas Friedrich, Regis Loeb, Arina Afanasyeva, Ansgar Schuffenhauer, Yves Moreau, Hugo Ceulemans
Format: Article
Language:English
Published: Elsevier 2023-12-01
Series:Artificial Intelligence in the Life Sciences
Subjects:
Small molecule drug discovery
MELLODDY
QSAR
Federated multitask learning
Applicability domain
Uncertainty
Online Access:http://www.sciencedirect.com/science/article/pii/S2667318523000144

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http://www.sciencedirect.com/science/article/pii/S2667318523000144

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