Data-Driven Classification of Solubility Space in Deep Eutectic Solvents: Deciphering Driving Forces Using PCA and K-Means Clustering.

ABSTRACT: This study presents a robust, data-driven framework for classifying and predicting drug solubility in deep eutectic solvents (DESs), moving beyond empirical approaches to enable rational formulation design. By analyzing 2010 solubility measurements of 21 diverse pharmaceutical compounds across numerous choline chloride, betaine, and menthol-based DESs, we employed Principal Component Analysis to reduce 16 COSMO-RS-derived descriptors into four chemically interpretable dimensions explaining 86.7% of the total variance. Persistence analysis confirmed component stability, revealing two key factors: PC1 (global solvation propensity, i.e., the overall capacity of the solvent to stabilize solutes through all interaction types) and PC2 (specific interaction complementarity, i.e., the degree of ma

SUBMITTER: Cysewski P 

PROVIDER: S-EPMC12693604 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature