Predicting Value of Binding Constants of Organic Ligands to Beta-Cyclodextrin: Application of MARSplines and Descriptors Encoded in SMILES String

Abstract

The quantitative structure–activity relationship (QSPR) model was formulated to quantify values of the binding constant (lnK) of a series of ligands to beta–cyclodextrin (β-CD). For this purpose, the multivariate adaptive regression splines (MARSplines) methodology was adopted with molecular descriptors derived from the simplified molecular input line entry specification (SMILES) strings. This approach allows discovery of regression equations consisting of new non-linear components (basis functions) being combinations of molecular descriptors. The model was subjected to the standard internal and external validation procedures, which indicated its high predictive power. The appearance of polarity-related descriptors, such as XlogP, confirms the hydrophobic nature of the cyclodextrin cavity. The model can be used for predicting the affinity of new ligands to β-CD. However, a non-standard application was also proposed for classification into Biopharmaceutical Classification System (BCS) drug types. It was found that a single parameter, which is the estimated value of lnK, is sufficient to distinguish highly permeable drugs (BCS class I and II) from low permeable ones (BCS class II and IV). In general, it was found that drugs of the former group exhibit higher affinity to β-CD then the latter group (class III and IV).

Computer-Aided Discovery of New Solubility-Enhancing Drug Delivery System

“…The curated dataset consisted of diverse API and non-API chemical structures in systems with 16 CDs of natural and semi-synthetic origin. Previously, to model guest-CD systems, an effort was made to curate the data originating from the distinct literature studies [ 35 , 36 ], including a large library of both guest molecules and CDs [ 17 ]. We have followed the best practices of QSAR modeling [ 31 ] to perform our study and assure of its reproducibility.…”

“…Due to the enantioselectivity of CDs [ 37 ], the information on API chiral centers was preserved by using 3D descriptors. One of the biggest advantages of this model is that it could be applied to a variety of CDs and experimental conditions instead of being specific for a single CD and predefined temperature and pH [ 35 , 38 ]. Consideration of both guest and CD structures makes the model useful in cases when no sufficient experimental data for a CD system exist.…”

A Systematic Review on Intensifications of Artificial Intelligence Assisted Green Solvent Development

“…Text strings like SMILES, as a specific alphabet, are commonly applied to encode molecular structures in which atoms, bonds, stereochemistry, and conformations are represented by typographic characters. , Zhou et al considered SMILES as a chemical language, and each SMILES notation is treated as a sentence. A deep pyramid convolutional neural network architecture is constructed for extracting the information from SMILES “sentences”, and a feed-forward neural network is used for QSPR modeling.…”