Advanced QSRR Modeling in β-CD-Modified RP-HPLC System

Quantitative structure-retention relationship (QSRR) modeling has emerged as an efficient alternative to predict analyte retention times using molecular descriptors. However, most reported QSRR models are column-specific, requiring separate models for each high-performance liquid chromatography (HPLC) system. This study evaluates the potential of machine learning (ML) algorithms and quantum mechanical (QM) descriptors to develop QSRR models that can predict retention times across three different reversed-phase HPLC columns under varying conditions. Four machine learning methods-partial least squares (PLS) regression, ridge regression (RR), random forest (RF), and gradient boosting (GB)-were compared on a dataset of 360 retention times for 15 aromatic analytes. Molecular descriptors were calculated using density functional theory (DFT). Column characteristics like particle size and pore size and experimental conditions like temperature and gradient time were additionally used as descriptors. Results showed that the GB-QSRR model demonstrated the best predictive performance, with Q2 of 0.989 and root mean square error of prediction (RMSEP) of 0.749min on the test set. Feature analysis revealed that solvation energy (SE), HOMO-LUMO energy gap (∆E HOMO-LUMO), total dipole moment (Mtot), and global hardness (η) are among the most influential predictors for retention time prediction, indicating the significance of electrostatic interactions and hydrophobicity. Our findings underscore the efficiency of ensemble methods, GB and RF models employing non-linear learners, in capturing local variations in retention times across diverse experimental setups. This study emphasizes the potential of cross-column QSRR modeling and highlights the utility of ML models in optimizing chromatographic analysis.

Previous studies demonstrated that quantitative structure retention relationships (QSRR) combined with the linear solvent strength (LSS) model allow for prediction of gradient reversed phase high performance liquid chromatography (HPLC) retention time for any analyte of a known molecular structure under defined HPLC conditions. The QSRR model derived at the selected gradient time was tested at the same gradient time. Presently, in the first step, experimental retention data for model sets of just 5 analytes were used to derive appropriate QSRR models at two gradient times. Additionally, a new molecular modeling approach based on a more accurate ab initio method was here proposed. Those QSRR models were used to further predict gradient retention times for sets of 16 test analytes belonging to polynuclear aromatic hydrocarbons (PAHs), at two selected gradient times. Then, applying linear solvent strength (LSS) theory, only predicted retention times for PAHs were used to find the optimal gradient conditions to separate them. Satisfactory predictions of gradient retention times for PAHs were obtained. Contrary to the previous achievements, the proposed QSRR provides the chance to predict retention of PAHs with the appropriate selectivity achieving the same sequence of analytes eluted in the experiment and during the simulation performed on the computer screen.

Quantitative structure-retention relationship of selected imidazoline derivatives on α1-acid glycoprotein column

Quantitative structure-retention relationship (QSRR) models were used to predict the retention time (t(R)) of mycotoxins and fungal metabolites. Heuristic method and radial basis function neural networks (RBFNN) were utilized to construct the linear and non-linear QSRR models, respectively. The optimal QSRR model was developed based on a 5-21-1 RBFNN architecture using molecular descriptors calculated from molecular structure alone. The RBFNN model gave a square of correlation coefficient (R(2)) of 0.8709 and root mean square error of 1.2892 for the test set. This article provided a useful tool for predicting the t(R) of other mycotoxins when experiment data are unknown.

Quantitative Structure–Retention Relationship Modeling of Morphine and Its Derivatives on OV-1 Column in Gas–Liquid Chromatography Using Genetic Algorithm

Identification of impurities in macrolides by liquid chromatography–mass spectrometric detection and prediction of retention times of impurities by constructing quantitative structure–retention relationship (QSRR)

Comparative multiple quantitative structure–retention relationships modeling of gas chromatographic retention time of essential oils using multiple linear regression, principal component regression, and partial least squares techniques

Retention prediction in reversed phase high performance liquid chromatography using quantitative structure-retention relationships applied to the Hydrophobic Subtraction Model

Liquid chromatographic retention time prediction models to secure and improve the feature annotation process in high-resolution mass spectrometry

Structure identification in nontargeted metabolomics based on liquid-chromatography coupled to mass spectrometry (LC-MS) remains a significant challenge. Quantitative structure-retention relationship (QSRR) modeling is a technique capable of accelerating the structure identification of metabolites by predicting their retention, allowing false positives to be eliminated during the interpretation of metabolomics data. In this work, 191 compounds were grouped according to molecular weight and a QSRR study was carried out on the 34 resulting groups to eliminate false positives. Partial least squares (PLS) regression combined with a Genetic algorithm (GA) was applied to construct the linear QSRR models based on a variety of VolSurf+ molecular descriptors. A novel dual-filtering approach, which combines Tanimoto similarity (TS) searching as the primary filter and retention index (RI) similarity clustering as the secondary filter, was utilized to select compounds in training sets to derive the QSRR models yielding R2 of 0.8512 and an average root mean square error in prediction (RMSEP) of 8.45%. With a retention index filter expressed as ±2 standard deviations (SD) of the error, representative compounds were predicted with >91% accuracy, and for 53% of the groups (18/34), at least one false positive compound could be eliminated. The proposed strategy can thus narrow down the number of false positives to be assessed in nontargeted metabolomics.

A comparative quantitative structure–retention relationship (QSRR) study was carried out to predict the retention time of polycyclic aromatic hydrocarbons (PAHs) using molecular descriptors. The molecular descriptors were generated by the software Dragon and employed to build QSRR models. The effect of chromatographic parameters, such as flow rate, temperature, and gradient time, was also considered. An artificial neural network (ANN) and Partial Least Squares Regression (PLS-R) were used to investigate the correlation between the retention time, taken as the response, and the predictors. Six descriptors were selected by the genetic algorithm for the development of the ANN model: the molecular weight (MW); ring descriptor types nCIR and nR10; radial distribution functions RDF090u and RDF030m; and the 3D-MoRSE descriptor Mor07u. The most significant descriptors in the PLS-R model were MW, RDF110u, Mor20u, Mor26u, and Mor30u; edge adjacency indice SM09_AEA (dm); 3D matrix-based descriptor SpPosA_RG; and the GETAWAY descriptor H7u. The built models were used to predict the retention of three analytes not included in the calibration set. Taking into account the statistical parameter RMSE for the prediction set (0.433 and 0.077 for the PLS-R and ANN models, respectively), the study confirmed that QSRR models, associated with chromatographic parameters, are better described by nonlinear methods.

As a potent zinc chelator, hydroxamic acid has been applied in the design of inhibitors of zinc metalloenzyme, such as histone deacetylases (HDACs). A series of hydroxamic acids with HDAC inhibitory activities were subjected to the QSRR (Quantitative Structure-Retention Relationships) study. Experimental data in combination with calculated molecular descriptors were used for the development of the QSRR model. Specially, we employed PCA (principal component analysis) to accomplish dimension reduction of descriptors and utilized the principal components of compounds (16 training compounds, 4 validation compounds and 7 test compounds) to execute GA (genetic algorithm)-BP (error backpropagation) algorithm. We performed double cross-validation approach for obtaining a more convincing model. Moreover, we introduced molecular interaction-based features (molecular docking scores) as a new type of molecular descriptor to represent the interactions between analytes and the mobile phase. Our results indicated that the incorporation of molecular interaction-based features significantly improved the accuracy of the QSRR model, (R2 value is 0.842, RMSEP value is 0.440, and MAE value is 0.573). Our study not only developed QSRR model for the prediction of the retention time of hydroxamic acid in HPLC but also proved the feasibility of using molecular interaction-based features as molecular descriptors.

Reversed-phase high performance liquid chromatography (RP-HPLC) characteristics of some 9,10-anthraquinone derivatives using binary acetonitrile–water mixtures as mobile phase

In the present work, the effect of stationary phase resin chemistry and protein physicochemical properties on protein binding affinity in hydrophobic interaction chromatography (HIC) was investigated using linear gradient chromatography and quantitative structure-retention relationship (QSRR) modeling. Linear gradient experiments were carried out for a set of model proteins on four different HIC resins having different backbone and ligand chemistry. The retention data exhibited significant differences in protein binding affinity, not only across the phenyl and butyl ligand chemistries, but also for the different backbone chemistries found in the Sepharose (cross-linked agarose) and the Toyopearl 650 M (polymethacrylate) series of resins. QSRR models based on a Support Vector Machine (SVM) approach were developed for the linear retention data using molecular descriptors based on protein crystal structure and primary sequence information as well as a set of new hydrophobicity descriptors based on the solvent accessible protein surface area. The results indicate that the QSRR models were successfully able to capture and selectivity predict the changes observed in these systems. Furthermore, the new descriptors resulted in physically interpretable models of protein retention and provided insights into the factors influencing protein affinity in these different HIC systems. The approach put forth in this study provides a framework for developing predictive tools and for gaining insight into protein selectivity in hydrophobic interaction chromatography.

The objectives of this study were to gain insights into structure-retention relationships and to propose the model to estimating their retention. Chromatographic investigation of series of 36 Schiff bases and their copper(II) and nickel(II) complexes was performed under both normal- and reverse-phase conditions. Chemical structures of the compounds were characterized by molecular descriptors which are calculated from the structure and related to the chromatographic retention parameters by multiple linear regression analysis. Effects of chelation on retention parameters of investigated compounds, under normal- and reverse-phase chromatographic conditions, were analyzed by principal component analysis, quantitative structure-retention relationship and quantitative structure-activity relationship models were developed on the basis of theoretical molecular descriptors, calculated exclusively from molecular structure, and parameters of retention and lipophilicity.