Chemoinformatic modelling of the antioxidant activity of phenolic compounds.

BACKGROUND: Antioxidants are chemicals used to protect foods from deterioration by neutralizing free radicals and inhibiting the oxidative process. One approach to investigate the antioxidant activity is to develop quantitative structure-activity relationships (QSARs). RESULTS: A curated database of 165 structurally heterogeneous phenolic compounds with the Trolox equivalent antioxidant capacity (TEAC) was developed. Molecular geometries were optimized by means of the GFN2-xTB semiempirical method and diverse molecular descriptors were obtained afterwards. For model development, V-WSP unsupervised variable reduction was used before performing the genetic algorithms-variable subset selection (GAs-VSS) to construct the best five-descriptor multiple linear regression model. The coefficient of determination and the root mean square error were used to measure the performance in calibration (R2 = 0.789 and RMSEC = 0.381), and test set prediction (Q2 = 0.748 and RMSEP = 0.416), along several cross-validation criteria. To thoroughly understand the TEAC prediction, a fully explained mechanism of action of the descriptors is provided. In addition, the applicability domain of the model defined a theoretical chemical space for reliable predictions of new phenolic compounds. CONCLUSION: This in silico model conforms to the five principles stated by the Organisation for Economic Co-operation and Development. The model might be useful for virtual screening of the antioxidant chemical space and for identifying the most potent molecules related to an experimental measurement of TEAC activity. In addition, the model could assist chemists working on computer-aided drug design for the synthesis of new targets with improved activity and potential uses in food science. © 2023 Society of Chemical Industry.

Acute Toxicity of Daphnia magna Neonates Exposed to Single and Composite Mixtures of Four Emerging Contaminants.

The effects of emerging contaminants on environmental health are of high concern, especially those potentially induced by mixtures. We assessed single and composite mixtures of triclosan (T), 17ß-estradiol (E2), sulfamethoxazole (SMX), and nicotine (N) at various concentrations, on neonates of Daphnia magna. When used in single exposure, T and N induced high toxicity (100% immobility, each one), compared to SMX and E2 (2.5% and 10% immobility, respectively). When T, E2, SMX and N were in mixture, T had the highest contribution to the overall toxicity in mixture exposures. The N toxicity lowered when in a fourfold exposure (85% immobility in fourfold exposure). Due to the high toxicity of T and N, both alone and in the mixtures, our results can serve as a warning about the use of these substances and their release in the aquatic ecosystem.

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 (tR) 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 tR of an external set of 57 pesticides.

Classification-based QSAR Models for the Prediction of the Bioactivity of ACE-inhibitor Peptides.

BACKGROUND: Local classification models were used to establish Quantitative Structure- Activity Relationships (QSARs) of bioactive di-, tri- and tetrapeptides, with their capacity to inhibit Angiotensin Converting Enzyme (ACE). These discrete models can thus predict this activity for other peptides obtained from functional foods. These types of peptides allow some foods to be considered nutraceuticals. METHOD: A database of 313 molecules of di-, tri- and tetrapeptides was investigated and antihypertensive activities of peptides, expressed as log (1/IC50), were separated into two qualitative classes: low activity (inactive) was associated with experimental values under the 66th percentile and active peptides with values above this threshold. Chemicals were divided into a training set, including 70% of the peptides, and a test set for external validation. Genetic algorithms-variable subset selection coupled with the kNN and N3 local classifiers were applied to select the best subset of molecular descriptors from a pool of 953 Dragon descriptors. Both models were validated on the test peptides. RESULTS: The N3 model turned out to be superior to the kNN model when the classification focused on identifying the most active peptides.

A QSTR-Based Expert System to Predict Sweetness of Molecules.

This work describes a novel approach based on advanced molecular similarity to predict the sweetness of chemicals. The proposed Quantitative Structure-Taste Relationship (QSTR) model is an expert system developed keeping in mind the five principles defined by the Organization for Economic Co-operation and Development (OECD) for the validation of (Q)SARs. The 649 sweet and non-sweet molecules were described by both conformation-independent extended-connectivity fingerprints (ECFPs) and molecular descriptors. In particular, the molecular similarity in the ECFPs space showed a clear association with molecular taste and it was exploited for model development. Molecules laying in the subspaces where the taste assignation was more difficult were modeled trough a consensus between linear and local approaches (Partial Least Squares-Discriminant Analysis and N-nearest-neighbor classifier). The expert system, which was thoroughly validated through a Monte Carlo procedure and an external set, gave satisfactory results in comparison with the state-of-the-art models. Moreover, the QSTR model can be leveraged into a greater understanding of the relationship between molecular structure and sweetness, and into the design of novel sweeteners.

Quantitative structure-property relationship analysis for the retention index of fragrance-like compounds on a polar stationary phase.

A quantitative structure-property relationship (QSPR) was developed for modeling the retention index of 1184 flavor and fragrance compounds measured using a Carbowax 20M glass capillary gas chromatography column. The 4885 molecular descriptors were calculated using Dragon software, and then were simultaneously analyzed through multivariable linear regression analysis using the replacement method (RM) variable subset selection technique. We proceeded in three steps, the first one by considering all descriptor blocks, the second one by excluding conformational descriptor blocks, and the last one by analyzing only 3D-descriptor families. The models were validated through an external test set of compounds. Cross-validation methods such as leave-one-out and leave-many-out were applied, together with Y-randomization and applicability domain analysis. The developed model was used to estimate the I of a set of 22 molecules. The results clearly suggest that 3D-descriptors do not offer relevant information for modeling the retention index, while a topological index such as the Randic-like index from reciprocal squared distance matrix has a high relevance for this purpose.