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Published on: September 20, 2017
Hybrid Improved Grey Wolf Support Vector Regression Algorithm for Modeling Solubilities of APIs in Pure Ionic
Imane Euldji1, Widad Benmouloud1, Kamil Paduszyński2
1Faculty of Technology, Department of Process and Environmental Engineering, Biomaterials and Transport Phenomena Laboratory (LBMPT), University of Yahia Fares, Medea 26000, Algeria.
Abstract:
The objective of this study was to model the solubility of active pharmaceutical ingredients (APIs) in different ionic liquids (ILs) based on the σ-moments of cations, anions, and APIs that were used as molecular descriptors calculated using the σ-profiles of three categories of descriptors based on conductor-like screening model for real solvents. The database of 83 API-ILs systems composed of 14 APIs, 12 cations, and 7 anions (25 ILs combinations) was collected as 850 data points at different temperature ranges. A hybrid Improved Grey Wolf Support vector regression, abbreviated as I-GWO-SVR(r), algorithm was selected as the learning method. Based on a comprehensive comparison with 11 different models, various statistical factors, and graphical analyses, including an external validation test, analysis of variance (ANOVA), and sensitivity analysis, the capability and validity of the proposed approach have been assessed and verified. The overall study confirmed that the proposed new model provided the best results in terms of predicting the solubility of APIs in ILs.
