Correlating physico-chemical properties of analytes with Hansen solubility parameters of solvents using machine learning algorithm for predicting suitable extraction solvent

• Published in: Scientific reports Vol. 14; no. 1; pp. 18741 - 9
• Main Authors: Mostafa, Eman A., Azim, Mohammad Abdul, ElZaher, Asmaa A., ElKady, Ehab F., Fouad, Marwa A., Ghazy, Fatma H., Radi, Esraa A., El Makarim Saleh, Mahmoud Abo, El Kerdawy, Ahmed M.
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 13.08.2024; Nature Publishing Group; Nature Portfolio
• Subjects: 639/638/11; 639/638/11/872; Algorithms; Artificial Neural Network model; Chemical properties; Drug solvents; Extraction solvent prediction; Green chemistry; Hansen solubility parameters; Humanities and Social Sciences; Information processing; Liquid chromatography; Liquid–liquid extraction; Model validation; multidisciplinary; Neural networks; Organic solvents; Physicochemical properties; Prediction models; Sample preparation; Science; Science (multidisciplinary); Solubility; Solvents; Liquid–liquid extraction; Hansen solubility parameters; Artificial Neural Network model; Extraction solvent prediction; Model validation
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-024-68981-9

Artificial neural networks (ANNs) are biologically inspired algorithms designed to simulate the way in which the human brain processes information. In sample preparation for bioanalysis, liquid–liquid extraction (LLE) represents an important step with the extraction solvent selection is the key laborious step. In the current work, a robust and reliable ANNs model for LLE solvent prediction was generated which could predict the suitable solvent for analyte extraction. The developed ANNs model takes a set of chosen descriptors for the cited analyte as an input and predicts the corresponding Hansen solubility parameters of the suitable extraction solvent as a model output. Then, from the solvent combination’s appendix, the analyst can identify the proposed extraction solvents' combination for the cited analyte easily and efficiently. For the experimental validation of the model prediction capabilities, twenty structurally diverse drugs belonging to different pharmacological classes were extracted from human plasma. The extraction process was performed using the predicted extraction solvent combination for each drug and quantitively estimated by HPLC/UV methods to assess their extraction recovery. The developed LLE solvent prediction model is in- line with the global trend towards green chemistry since it limits the consumption of organic solvents.