Hybrid Improved Grey Wolf Support Vector Regression Algorithm for Modeling Solubilities of APIs in Pure Ionic Liquids: σ‑Profile Descriptors

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.