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Updated: Jan 13, 2026

Solubility of Hydrophobic Compounds in Aqueous Solution Using Combinations of Self-assembling Peptide and Amino Acid
Published on: September 20, 2017
Comparative Assessment of Statistical and Thermodynamic Prediction Methods for Solvate Formation: A Case Study with
Julian Ticona-Chambi1, Duane Choquesillo-Lazarte2, Silvia Lucia Cuffini1
1Instituto de Ciência e Tecnologia (ICT), Universidade Federal de São Paulo (UNIFESP), São José dos Campos 12231-280, Brazil.
Abstract:
This study compares statistical and thermodynamic methodologies for predicting solvate formation using curcumin (CUR) and its derivatives demethoxycurcumin (DMC) and bisdemethoxycurcumin (BDMC) as models. We evaluated the performance of Statistical Frequency of Interaction for Multicomponent Prediction (SFIMP) and Conductor-like Screening Model for Realistic Solvents (COSMO-RS) methods to identify solvents likely to form solvates. A comprehensive crystallization screen yielded several new solvated and hydrated forms. Our results show that hydrogen bond propensity (HBP) performed best among individual predictors, while COSMO-RS combined with HBP yielded superior predictive accuracy overall. These insights aid rational design and screening of multicomponent solid forms in pharmaceutical development.
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