Predicting the Aqueous Solubility of Neutral Organic Compounds by Reversed-Phase Liquid Chromatography

Accurate estimation of water solubility is crucial for chemical screening, yet traditional predictive tools such as the General Solubility Equation (GSE) require melting point and octanol-water partition coefficient data and often struggle with weak electrolytes. This study presents two highly efficient reversed-phase liquid chromatography (RPLC) correlation strategies evaluated, comprising 49 binary solvent systems on three stationary phases. First, simple, melting point, octanol-water partition coefficient, and descriptor-free, isocratic models were explored to estimate the water solubility of neutral organic compounds using RPLC retention as the sole input. This approach demonstrated superior predictive accuracy of 0.307 log units and a more uniform error distribution across both non-electrolytes and weak electrolytes than the more conventional GSE. Second, to improve the predictive accuracy of estimating water solubility, RPLC retention and melting point data were explored. Predictive accuracy improved notably at higher organic volume fractions (50%-70% v/v) across all evaluated stationary phases. The correlation model comprising 70% (v/v) acetonitrile on an XTerra MS C18 stationary phase yielded a coefficient of determination of 0.962, a standard deviation of the model fit of 0.276, and an average absolute error value of 0.222. Together, these complementary chromatographic modelling strategies offer powerful, high-throughput alternatives for rapid solubility screening: providing either a direct, descriptor-free approach for compounds lacking structural information or a highly precise RPLC model approach combining retention and melting point data that minimizes prediction errors below 0.3 log units.