In Silico High-Performance Liquid Chromatography Method Development via Machine Learning.

High-performance liquid chromatography (HPLC) remains the gold standard for analyzing and purifying molecular components in solutions. However, developing HPLC methods is material- and time-consuming, so computer-aided shortcuts are highly desirable. In line with the digitalization of process development and the growth of HPLC databases, we propose a data-driven methodology to predict molecule retention factors as a function of mobile phase composition without the need for any new experiments, solely relying on molecular descriptors (MDs) obtained via simplified molecular input line entry system (SMILES) string representations of molecules. This new approach combines: (a) quantitative structure-property relationships (QSPR) using MDs to predict solute-dependent parameters in (b) linear solvation energy relationships (LSER) and (c) linear solvent strength (LSS) theory. We demonstrate the potential of this computational methodology using experimental data for retention factors of small molecules made available by the research community for which the MDs were obtained via SMILES string representations determined by the structural formulas of the molecules. This method can be adopted directly to predict elution times of molecular components; however, in combination with first-principle-based mechanistic transport models, the method can also be employed to optimize HPLC methods in-silico. Both options can reduce the experimental load and accelerate HPLC method development significantly, lowering the time and cost of the drug manufacturing cycle and reducing the time to market. Given the growing number and quality of HPLC databases, the predictive power of this methodology will only increase in the coming years.