Assessment of machine learning and group contribution solvation parameter model descriptors for model retention in reversed-phase liquid chromatography and gas chromatography

Abstract

• Modelling SoluteML and SoluteGC evaluated for RPLC and GC. • SoluteML estimated descriptors fit better than SoluteGC for chromatographic systems. • SoluteML and SoluteGC descriptors are not interchangeable with WSU descriptors. Abraham's solvation parameter model is a valuable tool for modelling reversed-phase liquid chromatography and gas chromatography systems. Except for the solute descriptor McGowan's characteristic volume, V, the remaining solute descriptors E, S, A, B, and L of the solvation parameter model are experimentally determined. Estimation approaches, machine learning, and group contribution methods are two alternatives to experimental approaches to estimating solute descriptors. In this work we evaluated the applicability of solvation parameter model solute descriptors estimated using machine learning and group contribution methods. Overall solute descriptors estimated using the machine learning approach fit better than solute descriptors estimated using the group contribution method for both reversed-phase liquid chromatography and gas chromatography systems studied in this work. For the studied methanol-water binary solvent system on a Luna C18(2) stationary phase model, coefficient of determination ranged from 0.982 to 0.953 when using machine learning estimated descriptors, whereas with group contribution estimated descriptors, models ranged between 0.923 and 0.943. For the studied gas chromatography models, coefficient of determination ranged from 0.995 to 0.987 when using machine learning estimated descriptors, whereas with group contribution estimated descriptors ranged between 0.941 and 0.977. However, both machine learning and group contribution descriptors did not fit in models as well as experimentally determined reference WSU descriptors.

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