The need for solvating and encapsulating hydro-sensitive molecules drives noticeable trends in the applications of cyclodextrins in pharmaceutical industry, food, polymer, materials, and agricultural science. Among them, β-cyclodextrin is one of the most used for the entrapment of phenolic acid compounds to mask the bitterness of wheat bran. In this regard, there is still a need for a good predictive model that assesses β-cyclodextrin bitterness masking capabilities for various phenolic compounds. This study uses a dataset of 20 phenolic acids docked to the β-cyclodextrin cavity to generate three different binding constants. The data from docking study were combined with topological, topographical and quantum-chemical features from the ligands in a machine learning-based structure-activity relationship study. Three different models for each binding constant were computed using a combination of Genetic Algorithm (GA) and Multiple Linear Regression (MLR) approaches. The developed ML/QSAR models showed a very good performance, with high predictive ability and correlation coefficients of 0.969 and 0.984, for training and test sets, respectively. The models revealed several factors responsible for a binding with cyclodextrin, showing positive contributions toward the binding affinity values, including such features as presence of six-membered rings in the molecule, branching, electronegativity values, and polar surface area.

The dielectric constant (ε) reflects the ability of a material to align and orient electrical dipoles within its structure in response to an externally applied electric field; the greater the polarizability of molecules, the greater the value of dielectric constant. In this study a data set of 86 polymers was investigated to develop a structure-property quantitative relations (QSPR) model to predict the dielectric constant in polymers. An initial set of 1273 descriptors was used to select a best set of descriptors and to construct two machine learning models with Gradient Boosting Regressor (GB_A and GB_B). The best performing model (GB_A) with 8 descriptors, exhibited a performance of (R2train) = 0.938 and (R2test) = 0.802. The models were internally validated by 5 folds cross-validation, demonstrating robustness. Additionally, using the Accumulative Local Effect (ALE) technique, we analyzed the relationship between the 8 descriptors involved and the impact of these descriptors to dielectric constant of polymers.