2026-04-21T09:20:41Zhttps://riubu.ubu.es/oai/request

oai:riubu.ubu.es:10259/114382026-02-27T01:05:47Zcom_10259_4323com_10259_5086com_10259_2604col_10259_4330

Huerta Sainz, Sergio de la Escobedo Monge, María Antonieta Marcos Villa, Pedro A. Esteban-Ollo, José Antonio Montejo-Gil, Laura Conde-Rioll, María Atilhan, Mert Bol Arreba, Alfredo Aparicio Martínez, Santiago Plant metabolite extraction Natural Deep Eutectic Solvents (NADES) COSMO-based design Sustainable extraction Bioactive compounds Conventional extraction of valuable plant compounds often relies on hazardous volatile organic solvents (VOCs), posing environmental and health risks. This study explores a sustainable alternative using Natural Deep Eutectic Solvents (NADES) designed in-silico through the Conductor-like Screening Model for Realistic Solvents methodology (COSMO-RS) for efficient extraction of target plant metabolites. A library of NADES with varying compositions was designed using COSMO-RS to predict their physicochemical properties and affinity for target natural compounds, selecting the most promising candidates in terms of versatility, cost-effectiveness and biocompatibility. To complete the study, a predictive Artificial Intelligence based method (Decision Trees) was developed for reverse design of NADES for target bioactive compounds from energetic and structural molecular descriptors. From a compendium of 58 plant metabolites of interest and 66 natural compounds as NADES components, nearly 3000 solubility in silico tests were conducted and a total of 12 NADES were selected. Three solubility models were created, and a clear dependance of the target compound properties was observed. 2026-02-26 2026-02-26 2025-03 2026-12-25 info:eu-repo/semantics/article 2950-3574 https://hdl.handle.net/10259/11438 10.1016/j.scowo.2024.100043 eng Sustainable Chemistry One World. 2025, V. 5, 100043 https://doi.org/10.1016/j.scowo.2024.100043 http://creativecommons.org/licenses/by-nc-nd/4.0/ info:eu-repo/semantics/embargoedAccess Attribution-NonCommercial-NoDerivatives 4.0 Internacional Elsevier