Physicochemical modelling of the retention mechanism of temperature-responsive polymeric columns for HPLC through machine learning algorithms.
Machine learning
Molecular descriptors
Retention mechanism
Temperature-responsive liquid chromatography
Journal
Journal of cheminformatics
ISSN: 1758-2946
Titre abrégé: J Cheminform
Pays: England
ID NLM: 101516718
Informations de publication
Date de publication:
21 Jun 2024
21 Jun 2024
Historique:
received:
14
08
2023
accepted:
14
06
2024
medline:
22
6
2024
pubmed:
22
6
2024
entrez:
21
6
2024
Statut:
epublish
Résumé
Temperature-responsive liquid chromatography (TRLC) offers a promising alternative to reversed-phase liquid chromatography (RPLC) for environmentally friendly analytical techniques by utilizing pure water as a mobile phase, eliminating the need for harmful organic solvents. TRLC columns, packed with temperature-responsive polymers coupled to silica particles, exhibit a unique retention mechanism influenced by temperature-induced polymer hydration. An investigation of the physicochemical parameters driving separation at high and low temperatures is crucial for better column manufacturing and selectivity control. Assessment of predictability using a dataset of 139 molecules analyzed at different temperatures elucidated the molecular descriptors (MDs) relevant to retention mechanisms. Linear regression, support vector regression (SVR), and tree-based ensemble models were evaluated, with no standout performer. The precision, accuracy, and robustness of models were validated through metrics, such as r and mean absolute error (MAE), and statistical analysis. At
Identifiants
pubmed: 38907264
doi: 10.1186/s13321-024-00873-6
pii: 10.1186/s13321-024-00873-6
doi:
Types de publication
Journal Article
Langues
eng
Pagination
72Subventions
Organisme : Horizon 2020 Framework Programme
ID : 861369
Organisme : Fonds Wetenschappelijk Onderzoek
ID : 30897864
Informations de copyright
© 2024. The Author(s).
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