3D-QSAR predictions for α-cyclodextrin binding constants using quantum mechanically based descriptors

A Review of Machine Learning and QSAR/QSPR Predictions for Complexes of Organic Molecules with Cyclodextrins

Cyclodextrins are macrocyclic rings composed of glucose residues. Due to their remarkable structural properties, they can form host-guest inclusion complexes, which is why they are frequently used in the pharmaceutical, cosmetic, and food industries, as well as in environmental and analytical chemistry. This review presents the reports from 2011 to 2023 on the quantitative structure-activity/property relationship (QSAR/QSPR) approach, which is primarily employed to predict the thermodynamic stability of inclusion complexes. This article extensively discusses the significant developments related to the size of available experimental data, the available sets of descriptors, and the machine learning (ML) algorithms used, such as support vector machines, random forests, artificial neural networks, and gradient boosting. As QSAR/QPR analysis only requires molecular structures of guests and experimental values of stability constants, this approach may be particularly useful for predicting these values for complexes with randomly substituted cyclodextrins, as well as for estimating their dependence on pH. This work proposes solutions on how to effectively use this knowledge, which is especially important for researchers who will deal with this topic in the future. This review also presents other applications of ML in relation to CD complexes, including the prediction of physicochemical properties of CD complexes, the development of analytical methods based on complexation with CDs, and the optimisation of experimental conditions for the preparation of the complexes.

Computational Methods for the Interaction between Cyclodextrins and Natural Compounds: Technology, Benefits, Limitations, and Trends

Cyclodextrins (CDs) have a hollow structure with a hydrophobic interior and hydrophilic exterior. Forming inclusion complexes with CDs will maximize the bioavailability of natural compounds and enable active components to be processed into functional foods, medicines, additives, and so forth. However, experimental methods cannot explain CD-guest binding at the atomic level. Different models have been recently developed to simulate the interaction between CDs and guests to study the binding conformation and analyze noncovalent forces. This review paper summarizes modeling methods of CD-natural compound complexes. The methods include quantitative structure-activity relationships, molecular docking, molecular dynamics simulations, and quantum-chemical calculations. The applications of these methods to enhance the solubility and bioactivities of guest molecules, assist material transportation, and promote compound extraction are also discussed. The purpose of this review is to explore interaction mechanisms of CDs and guests and to help expand new applications of CDs.

Towards Rational Biosurfactant Design-Predicting Solubilization in Rhamnolipid Solutions

The efficiency of micellar solubilization is dictated inter alia by the properties of the solubilizate, the type of surfactant, and environmental conditions of the process. We, therefore, hypothesized that using the descriptors of the aforementioned features we can predict the solubilization efficiency, expressed as molar solubilization ratio (MSR). In other words, we aimed at creating a model to find the optimal surfactant and environmental conditions in order to solubilize the substance of interest (oil, drug, etc.). We focused specifically on the solubilization in biosurfactant solutions. We collected data from literature covering the last 38 years and supplemented them with our experimental data for different biosurfactant preparations. Evolutionary algorithm (EA) and kernel support vector machines (KSVM) were used to create predictive relationships. The descriptors of biosurfactant (logP_BS, measure of purity), solubilizate (logP_sol, molecular volume), and descriptors of conditions of the measurement (T and pH) were used for modelling. We have shown that the MSR can be successfully predicted using EAs, with a mean R² _val of 0.773 ± 0.052. The parameters influencing the solubilization efficiency were ranked upon their significance. This represents the first attempt in literature to predict the MSR with the MSR calculator delivered as a result of our research.

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

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).

3D-QSAR for binding constants of β-cyclodextrin host-guest complexes by utilising spectrophores as molecular descriptors

The increasing use of cyclodextrins (CDs) as host-molecules for host-guest complexes and their remediation application in environmental science requires to establish easily accessible models of "quantitative structure-activity relationships" (QSARs) to predict the binding constants. A new open-source molecular descriptor, so called spectrophores, was utilised to build 3D-QSAR models which have R² of 0.95 and RMSE of 0.20.

Development of QSAR model for predicting the inclusion constants of organic chemicals with α-cyclodextrin

Solubility is a crucial limiting factor in pharmaceutical research and contaminated site remediation. Cyclodextrin, with its structure of hydrophilic exterior and hydrophobic cavity, has a potential ability to enhance the hydrophobic chemical's solubility through the formation of host-guest complex. The stability of host-guest complex is often quantified by the inclusion constant. In this study, the logarithm of 1:1 α-cyclodextrin inclusion constants (log K_α) for 195 organic chemicals was collected. With this parameter as the endpoint, a quantitative structure-activity relationship (QSAR) model was developed using DRAGON descriptors and stepwise multiple linear regression analysis. The model statistics parameters indicated that the established model had a good determination coefficient of 0.857, a high cross-validation coefficient of 0.835, a low root mean square error of 0.380, together with the acceptable results of external validation, which indicate a satisfactory goodness-of-fit, robustness, and predictive ability of the model. Based on the screened eight descriptors, we propose an appropriate mechanism interpretation for the inclusion interaction. Additionally, the applicability domain of the current model was characterized by the Euclidean distance-based method and Williams plot, and results indicated that the model covered a large number of structurally diverse chemicals belonging to 13 different classes. Comparing with the previous reported models, this model has obvious advantages with a larger dataset, a higher value of correlation coefficient, and a wider application domain.

Detection of Organochlorine Pesticides in Contaminated Marine Environments via Cyclodextrin-Promoted Fluorescence Modulation

The development of practical and robust detection methods for pesticides is an important research objective owing to the known toxicity, carcinogenicity, and environmental persistence of these compounds. Pesticides have been found in bodies of water that are located near areas where pesticides are commonly used and easily spread to beaches, lakes, and rivers; affect the species living in those waterways; and harm humans who come into contact with or eat fish from such water. Reported herein is the rapid, sensitive, and selective detection of four organochlorine pesticides in a variety of water sources across the state of Rhode Island using cyclodextrin-promoted fluorescence detection. This method relies on the ability of cyclodextrin to promote analyte-specific fluorescence modulation of a high quantum yield fluorophore when a pesticide is in close proximity, combined with subsequent array-based statistical analyses of the measurable changes in the emission signals. This system operates with high sensitivity (low micromolar detection limits), selectivity (100% differentiation between structurally similar analytes), and general applicability (for different water samples with varying salinity and pH as well as for different water temperatures).

3D-QSAR predictions for bovine serum albumin-water partition coefficients of organic anions using quantum mechanically based descriptors

Ionic organic chemicals are a class of chemicals that is released in the environment in a large amount from anthropogenic sources. Among various chemical and biological processes, binding to serum albumin is particularly relevant for the toxicokinetic behavior of ionic chemicals. Several experimental studies showed that steric effects have a crucial influence on the sorption to bovine serum albumin (BSA). In this study, we investigated whether a 3D quantitative structure-activity relationship (3D-QSAR) model can accurately account for these steric effects by predicting the BSA-water partition coefficients (K_BSA/water) of neutral and anionic organic chemicals. The 3D-QSAR tested here uses quantum mechanically derived local sigma profiles as descriptors. In general, the 3D-QSAR model was able to predict the partition coefficients of neutral and anionic chemicals with an acceptable quality (RMSE_{test set} 0.63 ± 0.10, R_{test set}² 0.52 ± 0.15, both for log K_BSA/water). Particularly notable is that steric effects that cause a large difference in the log K_BSA/water values between isomers were successfully reproduced by the model. The prediction of unknown K_BSA/water values with the proposed model should contribute to improved environmental and toxicological assessments of chemicals.