Performance and robustness of small molecule retention time prediction with molecular graph neural networks in industrial drug discovery campaigns

Performance and robustness of small molecule retention time prediction with molecular graph neural networks in industrial drug discovery campaigns

Abstract This study explores how machine-learning can be used to predict chromatographic retention times (RT) for the analysis of small molecules, with the objective of identifying a machine-learning framework with the robustness required to support a chemical synthesis production platform. We used...

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Bibliographic Details
Main Authors: Daniel Vik, David Pii, Chirag Mudaliar, Mads Nørregaard-Madsen, Aleksejs Kontijevskis
Format: Article
Language:English
Published: Nature Portfolio 2024-04-01
Series:Scientific Reports
Subjects:
Chromatography
Machine-learning
Retention time
Small molecule
Applied artificial intelligence
Pharmaceuticals
Online Access:https://doi.org/10.1038/s41598-024-59620-4

Internet

https://doi.org/10.1038/s41598-024-59620-4

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