Physicochemical modelling of the retention mechanism of temperature-responsive polymeric columns for HPLC through machine learning algorithms

• Published in: Journal of cheminformatics Vol. 16; no. 1; pp. 72 - 12
• Main Authors: Bandini, Elena, Castellano Ontiveros, Rodrigo, Kajtazi, Ardiana, Eghbali, Hamed, Lynen, Frédéric
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 21-06-2024; BioMed Central Ltd; Springer Nature B.V; BMC
• Subjects: Algorithms; Analysis; Chemistry; Chemistry and Materials Science; Chromatography; Computational Biology/Bioinformatics; Computer Applications in Chemistry; Data mining; Documentation and Information in Chemistry; Error analysis; Functional groups; High performance liquid chromatography; High temperature; Lipophilic; Liquid chromatography; Low temperature; Machine learning; Molecular descriptors; Organic solvents; Physicochemical properties; Polymers; Regression analysis; Retention; Retention mechanism; Silica; Statistical analysis; Support vector machines; Temperature; Temperature-responsive liquid chromatography; Theoretical and Computational Chemistry; Temperature-responsive liquid chromatography; Molecular descriptors; Retention mechanism; Machine learning
• ISSN: 1758-2946; 1758-2946
• DOI: 10.1186/s13321-024-00873-6

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
45
∘
C
, logP predominantly influenced retention, akin to reversed-phase columns, while at
5
∘
C
, complex interactions with lipophilic and negative MDs, along with specific functional groups, dictated retention. These findings provide deeper insights into TRLC mechanisms, facilitating method development and maximizing column potential.