Toward Predicting Solubility of Arbitrary Solutes in Arbitrary Solvents: Prediction of Density and Refractive Index Using Machine Learning Algorithms with Global Sensitivity Analysis.

Density and refractive index (nD) are routinely used in organic process development to support solvent selection, mixture formulation, phase behavior analysis, and rapid assessment of composition during scale-up and manufacturing. Reliable knowledge of these properties enables informed process decisions when chromatographic or spectroscopic methods are impractical or unavailable, particularly at the early development stages. In addition, the two properties are strongly related to the van der Waals (VDW) energy of a molecule. Thus, accurate prediction of these two properties has great value in both organic process development, molecular mechanics force field development, and solvation free energy and solubility prediction of any arbitrary molecules. In this study, we gathered molecular characteristic information on roughly 5000 organic compounds for density records and 4000 organic compounds for nD values. Subsequently, the distinct GAFF (General AMBER Force Field) descriptors and RDKit descriptors of the compounds were generated and then applied to train various prediction models with a variety of machine learning (ML) algorithms for both properties, respectively. As a result, both GAFF and RDKit descriptors yielded various robust models with low average percent errors (APE), low root-mean-square errors (RMSE), and high correlation coefficients R