Cross-column density functional theory-based quantitative structure-retention relationship model development powered by machine learning.

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 0.749 min 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.

Prediction of Chromatographic Elution Order of Analytical Mixtures Based on Quantitative Structure-Retention Relationships and Multi-Objective Optimization.

Prediction of the retention time from the molecular structure using quantitative structure-retention relationships is a powerful tool for the development of methods in reversed-phase HPLC. However, its fundamental limitation lies in the fact that low error in the prediction of the retention time does not necessarily guarantee a prediction of the elution order. Here, we propose a new method for the prediction of the elution order from quantitative structure-retention relationships using multi-objective optimization. Two case studies were evaluated: (i) separation of organic molecules in a Supelcosil LC-18 column, and (ii) separation of peptides in seven columns under varying conditions. Results have shown that, when compared to predictions based on the conventional model, the relative root mean square error of the elution order decreases by 48.84%, while the relative root mean square error of the retention time increases by 4.22% on average across both case studies. The predictive ability in terms of both retention time and elution order and the corresponding applicability domains were defined. The models were deemed stable and robust with few to no structural outliers.

Quantitative Structure-Retention Relationships with Non-Linear Programming for Prediction of Chromatographic Elution Order.

In this work, we employed a non-linear programming (NLP) approach via quantitative structure-retention relationships (QSRRs) modelling for prediction of elution order in reversed phase-liquid chromatography. With our rapid and efficient approach, error in prediction of retention time is sacrificed in favor of decreasing the error in elution order. Two case studies were evaluated: (i) analysis of 62 organic molecules on the Supelcosil LC-18 column; and (ii) analysis of 98 synthetic peptides on seven reversed phase-liquid chromatography (RP-LC) columns with varied gradients and column temperatures. On average across all the columns, all the chromatographic conditions and all the case studies, percentage root mean square error (%RMSE) of retention time exhibited a relative increase of 29.13%, while the %RMSE of elution order a relative decrease of 37.29%. Therefore, sacrificing %RMSE(tR) led to a considerable increase in the elution order predictive ability of the QSRR models across all the case studies. Results of our preliminary study show that the real value of the developed NLP-based method lies in its ability to easily obtain better-performing QSRR models that can accurately predict both retention time and elution order, even for complex mixtures, such as proteomics and metabolomics mixtures.

Target-based drug discovery through inversion of quantitative structure-drug-property relationships and molecular simulation: CA IX-sulphonamide complexes.

In this work, a target-based drug screening method is proposed exploiting the synergy effect of ligand-based and structure-based computer-assisted drug design. The new method provides great flexibility in drug design and drug candidates with considerably lower risk in an efficient manner. As a model system, 45 sulphonamides (33 training, 12 testing ligands) in complex with carbonic anhydrase IX were used for development of quantitative structure-activity-lipophilicity (property)-relationships (QSPRs). For each ligand, nearly 5,000 molecular descriptors were calculated, while lipophilicity (logkw) and inhibitory activity (logKi) were used as drug properties. Genetic algorithm-partial least squares (GA-PLS) provided a QSPR model with high prediction capability employing only seven molecular descriptors. As a proof-of-concept, optimal drug structure was obtained by inverting the model with respect to reference drug properties. 3509 ligands were ranked accordingly. Top 10 ligands were further validated through molecular docking. Large-scale MD simulations were performed to test the stability of structures of selected ligands obtained through docking complemented with biophysical experiments.

Non-target analysis of phospholipid and sphingolipid species in egg yolk using liquid chromatography/triple quadrupole tandem mass spectrometry.

In this work, phospholipids extracted from egg yolk (control group, experimental group) were identified using high performance liquid chromatography coupled with electrospray ionization-tandem mass spectrometry (HPLC-ESI-MS/MS). Combinations of fatty acyls occurring in 11 classes of phospholipids from egg yolk were investigated. Differences between the profile of fatty acyls from hens fed traditionally and the ones that received special diet supplementation were observed. Experimental findings were complemented with multivariate chemometric analysis. Multiple reaction monitoring mass spectrometry mode was utilized and 123 distinct combinations of fatty acyls occurring in phospholipids were identified. From these, large portions are polyunsaturated fatty acyls from the omega-3 and omega-6 family. HPLC MS/MS analysis allows for quick, accurate and precise determination of biologically active compounds, found in low concentrations within the tested material.

Silver-Lactoferrin Nanocomplexes as a Potent Antimicrobial Agent.

The process of silver immobilization onto and/or into bovine lactoferrin (LTF), the physicochemical properties of bovine lactoferrin and obtained silver-lactoferrin complexes, as well as antibacterial activity of silver-lactoferrin complexes were investigated in this work. Kinetic study of the silver immobilization into lactoferrin was carried out using batch sorption techniques. Spectrometric (MALDI-TOF/TOF-MS, ICP-MS), spectroscopic (FTIR, SERS), electron microscopic (TEM) and electrophoretic (I-DE) techniques, as well as zeta potential measurements, were applied for characterization of LTF and binding nature of silver in Ag-LTF complexes. On the basis of the results of the kinetics study, it was established that the silver binding to LTF is a heterogeneous process involving two main stages: (i) internal diffusion and sorption onto external surface of lactoferrin globules; and (ii) internal diffusion and binding into lactoferrin globule structure. Spectroscopic techniques combined with TEM analysis confirmed the binding process. Molecular dynamics (MD) analysis was carried out in order to simulate the mechanism of the binding process, and locate potential binding sites, as well as complement the experimental findings. Quantum mechanics (QM) simulations were performed utilizing density functional theory (DFT) in order to support the reduction mechanism of silver ions to elemental silver. Antimicrobial activity of synthesized lactoferrin complexes against selected clinical bacteria was confirmed using flow cytometry and antibiograms.

Assessment of column selection systems using Partial Least Squares.

Column selection systems based on calculation of a scalar measure based on Euclidean distance between chromatographic columns, suffer from the same issue. For diverse values of their parameters, identical or near-identical values can be calculated. Proper use of chemometric methods can not only provide a remedy, but also reveal underlying correlation between them. In this work, parameters of a well-established column selection system (CSS) developed at Katholieke Universiteit Leuven (KUL CSS) have been directly correlated to parameters of selectivity (retention time, resolution, and peak/valley ratio) toward pharmaceuticals, by employing Partial Least Squares (PLS). Two case studies were evaluated, separation of alfuzosin, lamotrigine, and their impurities, respectively. Within them, comprehensive correlation structure was revealed, which was thoroughly interpreted, confirming a causal relationship between KUL parameters and parameters of column performance. Furthermore, it was shown that the developed methodology can be applied to any distance-based column selection system.

Molecular descriptor subset selection in theoretical peptide quantitative structure-retention relationship model development using nature-inspired optimization algorithms.

In this work, performance of five nature-inspired optimization algorithms, genetic algorithm (GA), particle swarm optimization (PSO), artificial bee colony (ABC), firefly algorithm (FA), and flower pollination algorithm (FPA), was compared in molecular descriptor selection for development of quantitative structure-retention relationship (QSRR) models for 83 peptides that originate from eight model proteins. The matrix with 423 descriptors was used as input, and QSRR models based on selected descriptors were built using partial least squares (PLS), whereas root mean square error of prediction (RMSEP) was used as a fitness function for their selection. Three performance criteria, prediction accuracy, computational cost, and the number of selected descriptors, were used to evaluate the developed QSRR models. The results show that all five variable selection methods outperform interval PLS (iPLS), sparse PLS (sPLS), and the full PLS model, whereas GA is superior because of its lowest computational cost and higher accuracy (RMSEP of 5.534%) with a smaller number of variables (nine descriptors). The GA-QSRR model was validated initially through Y-randomization. In addition, it was successfully validated with an external testing set out of 102 peptides originating from Bacillus subtilis proteomes (RMSEP of 22.030%). Its applicability domain was defined, from which it was evident that the developed GA-QSRR exhibited strong robustness. All the sources of the model's error were identified, thus allowing for further application of the developed methodology in proteomics.