Foodinformatics: Quantitative Structure-Property Relationship Modeling of Volatile Organic Compounds in Peppers

Computational prediction of retention times of veterinary antibiotics obtained by liquid chromatography-mass spectrometry

BACKGROUND

Veterinary antibiotics are chemical compounds used to kill or inhibit the growth of pathogenic bacteria associated with animal diseases. These molecules can be defined by their retention times (tR) in liquid chromatography-mass spectrometry (LC-MS). One strategy to predict the tR of new veterinary antibiotics is the development of predictive quantitative structure-property relationships (QSPRs), which were used in this study.

RESULTS

A database of 122 antibiotics was selected in which the tR was measured using a Hypersil GOLD column. An optimal three-feature model was developed by integrating the unsupervised variable reduction, replacement method variable subset selection, and multiple linear regression. The negligible differences among the coefficient of determination and the root-mean-square error for the training set (R2 = 0.902 and RMSEC = 0.871) and test set (Q2 = 0.854 and RMSEP = 1.064) indicate a stable and predictive model. In a further step, a more in-depth explanation of the mechanism of action of each descriptor in predicting the tR is provided, with the construction of the theoretical chemical space for accurate predictions of new antibiotics.

CONCLUSION

The in silico model developed in this work identified three molecular descriptors associated with aqueous solubility, octanol-water partition coefficient, and the presence of negative and lipophilic atom pairs. The QSPR developed here could be implemented by agricultural and food chemists to identify and monitor existing and new antibiotics within the framework of LC-MS. The computational model was developed in accordance with five principles outlined by the Organization for Economic Co-operation and Development. © 2024 Society of Chemical Industry.

QSAR models for the ozonation of diverse volatile organic compounds at different temperatures

In order to assess the fate and persistence of volatile organic compounds (VOCs) in the atmosphere, it is necessary to determine their oxidation rate constants for their reaction with ozone (kO3). However, given that experimental values of kO3 are only available for a few hundred compounds and their determination is expensive and time-consuming, developing predictive models for kO3 is of great importance. Thus, this study aimed to develop reliable quantitative structure-activity relationship (QSAR) models for 302 values of 149 VOCs across a broad temperature range (178-409 K). The model was constructed based on the combination of a simplified molecular-input line-entry system (SMILES) and temperature as an experimental condition, namely quasi-SMILES. In this study, temperature was incorporated in the models as an independent feature. The hybrid optimal descriptor generated from the combination of quasi-SMILES and HFG (hydrogen-filled graph) was used to develop reliable, accurate, and predictive QSAR models employing the CORAL software. The balance between the correlation method and four different target functions (target function without considering IIC or CII, target function using each IIC or CII, and target function based on the combination of IIC and CII) was used to improve the predictability of the QSAR models. The performance of the developed models based on different target functions was compared. The correlation intensity index (CII) significantly enhanced the predictability of the model. The best model was selected based on the numerical value of Rm2 of the calibration set (split #1, Rtrain2 = 0.9834, Rcalibration2 = 0.9276, Rvalidation2 = 0.9136, and calibration = 0.8770). The promoters of increase/decrease for log kO3 were also computed based on the best model. The presence of a double bond (BOND10000000 and $10 000 000 000), absence of halogen (HALO00000000), and the nearest neighbor codes for carbon equal to 321 (NNC-C⋯321) are some significant promoters of endpoint increase.

A simple and reliable QSPR model for prediction of chromatography retention indices of volatile organic compounds in peppers

Worldwide, various types of pepper are used in food as an additive due to their unique pungency, aroma, taste, and color. This spice is valued for its pungency contributed by the alkaloid piperine and aroma attributed to volatile essential oils. The essential oils are composed of volatile organic compounds (VOCs) in different concentrations and ratios. In chromatography, the identification of compounds is done by comparing obtained peaks with a reference standard. However, there are cases where reference standards are either unavailable or the chemical information of VOCs is not documented in reference libraries. To overcome these limitations, theoretical methodologies are applied to estimate the retention indices (RIs) of new VOCs. The aim of the present work is to develop a reliable QSPR model for the RIs of 273 identified VOCs of different types of pepper. Experimental retention indices were measured using comprehensive two-dimensional gas chromatography coupled to quadrupole mass spectrometry (GC × GC/qMS) using a coupled BPX5 and BP20 column system. The inbuilt Monte Carlo algorithm of CORAL software is used to generate QSPR models using the hybrid optimal descriptor extracted from a combination of SMILES and HFG (hydrogen-filled graph). The whole dataset of 273 VOCs is used to make ten splits, each of which is further divided into four sets: active training, passive training, calibration, and validation. The balance of correlation method with four target functions i.e. TF0 (WIIC = WCII = 0), TF1 (WIIC = 0.5 & WCII = 0), TF2 (WIIC = 0 & WCII = 0.3) and TF3 (WIIC = 0.5 & WCII = 0.3) is used. The results of the statistical parameters of each target function are compared with each other. The simultaneous application of the index of ideality of correlation (IIC) and correlation intensity index (CII) improves the predictive potential of the model. The best model is judged on the basis of the numerical value of R2 of the validation set. The statistical result of the best model for the validation set of split 6 computed with TF3 (WIIC = 0.5 & WCII = 0.3) is R2 = 0.9308, CCC = 0.9588, IIC = 0.7704, CII = 0.9549, Q2 = 0.9281 and RMSE = 0.544. The promoters of increase/decrease for RI are also extracted using the best model (split 6). Moreover, the proposed model was used for an external validation set.

Prediction of flavor and retention index for compounds in beer depending on molecular structure using a machine learning method

In order to make a preliminary prediction of flavor and retention index (RI) for compounds in beer, this work applied the machine learning method to modeling depending on molecular structure. Towards this goal, the flavor compounds in beer from existing literature were collected. The database was classified into four groups as aromatic, bitter, sulfury, and others. The RI values on a non-polar SE-30 column and a polar Carbowax 20M column from the National Institute of Standards Technology (NIST) were investigated. The structures were converted to molecular descriptors calculated by molecular operating environment (MOE), ChemoPy and Mordred, respectively. By combining the pretreatment of the descriptors, machine learning models, including support vector machine (SVM), random forest (RF) and k-nearest neighbour (kNN) were utilized for beer flavor models. Principal component regression (PCR), random forest regression (RFR) and partial least squares (PLS) regression were employed to predict the RI. The accuracy of the test set was obtained by SVM, RF, and kNN. Among them, the combination of descriptors calculated by Mordred and RF model afforded the highest accuracy of 0.686. R² of the optimal regression model achieved 0.96. The results indicated that the models can be used to predict the flavor of a specific compound in beer and its RI value.

In order to make a preliminary prediction of flavor and retention index (RI) for compounds in beer, this work applied the machine learning method to modeling depending on molecular structure.

Foodinformatic prediction of the retention time of pesticide residues detected in fruits and vegetables using UHPLC/ESI Q-Orbitrap

The present work describes the development of an in silico model to predict the retention time (t_R) of a large Compound DataBase (CDB) of pesticides detected in fruits and vegetables. The model utilizes ultrahigh-performance liquid chromatography electrospray ionization quadrupole-Orbitrap (UHPLC/ESI Q-Orbitrap) mass spectrometry (MS) data. The available CDB was properly curated, and the pesticides were represented by conformation-independent molecular descriptors. In an attempt to improve the model predictions, the best four MLR models obtained were subjected to a consensus analysis. The optimal model was evaluated by means of the coefficient of determination and the residual standard deviation in calibration, validation, and prediction, along other internal and external validation criteria to accomplish the guidelines defined by the Organization for Economic Co-operation and Development. Finally, the in silico model was applied to predict the t_R of an external set of 57 pesticides.

The impact of chemoinformatics on drug discovery in the pharmaceutical industry

Introduction: Even though there have been substantial advances in our understanding of biological systems, research in drug discovery is only just now beginning to utilize this type of information. The single-target paradigm, which exemplifies the reductionist approach, remains a mainstay of drug research today. A deeper view of the complexity involved in drug discovery is necessary to advance on this field.Areas covered: This perspective provides a summary of research areas where cheminformatics has played a key role in drug discovery, including of the available resources as well as a personal perspective of the challenges still faced in the field.Expert opinion: Although great strides have been made in the handling and analysis of biological and pharmacological data, more must be done to link the data to biological pathways. This is crucial if one is to understand how drugs modify disease phenotypes, although this will involve a shift from the single drug/single target paradigm that remains a mainstay of drug research. Moreover, such a shift would require an increased awareness of the role of physiology in the mechanism of drug action, which will require the introduction of new mathematical, computer, and biological methods for chemoinformaticians to be trained in.