Dataset Information

Flavones' and Flavonols' Antiradical Structure-Activity Relationship-A Quantum Chemical Study.

ABSTRACT: Flavonoids are known for their antiradical capacity, and this ability is strongly structure-dependent. In this research, the activity of flavones and flavonols in a water solvent was studied with the density functional theory methods. These included examination of flavonoids' molecular and radical structures with natural bonding orbitals analysis, spin density analysis and frontier molecular orbitals theory. Calculations of determinants were performed: specific, for the three possible mechanisms of action-hydrogen atom transfer (HAT), electron transfer-proton transfer (ETPT) and sequential proton loss electron transfer (SPLET); and the unspecific-reorganization enthalpy (RE) and hydrogen abstraction enthalpy (HAE). Intramolecular hydrogen bonding, catechol moiety activity and the probability of electron density swap between rings were all established. Hydrogen bonding seems to be much more important than the conjugation effect, because some structures tends to form more intramolecular hydrogen bonds instead of being completely planar. The very first hydrogen abstraction mechanism in a water solvent is SPLET, and the most privileged abstraction site, indicated by HAE, can be associated with the C3 hydroxyl group of flavonols and C4' hydroxyl group of flavones. For the catechol moiety, an intramolecular reorganization to an o-benzoquinone-like structure occurs, and the ETPT is favored as the second abstraction mechanism.

SUBMITTER: Spiegel M

PROVIDER: S-EPMC7346117 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Flavones' and Flavonols' Antiradical Structure-Activity Relationship-A Quantum Chemical Study.

Spiegel Maciej M Andruniów Tadeusz T Sroka Zbigniew Z

Antioxidants (Basel, Switzerland) 20200527 6

Flavonoids are known for their antiradical capacity, and this ability is strongly structure-dependent. In this research, the activity of flavones and flavonols in a water solvent was studied with the density functional theory methods. These included examination of flavonoids' molecular and radical structures with natural bonding orbitals analysis, spin density analysis and frontier molecular orbitals theory. Calculations of determinants were performed: specific, for the three possible mechanisms ...[more]

PMID: 32471289

Similar Datasets

Project description:The specificity of toxicant-target biomolecule interactions lends to the very imbalanced nature of many toxicity datasets, causing poor performance in Structure-Activity Relationship (SAR)-based chemical classification. Undersampling and oversampling are representative techniques for handling such an imbalance challenge. However, removing inactive chemical compound instances from the majority class using an undersampling technique can result in information loss, whereas increasing active toxicant instances in the minority class by interpolation tends to introduce artificial minority instances that often cross into the majority class space, giving rise to class overlapping and a higher false prediction rate. In this study, in order to improve the prediction accuracy of imbalanced learning, we employed SMOTEENN, a combination of Synthetic Minority Over-sampling Technique (SMOTE) and Edited Nearest Neighbor (ENN) algorithms, to oversample the minority class by creating synthetic samples, followed by cleaning the mislabeled instances. We chose the highly imbalanced Tox21 dataset, which consisted of 12 in vitro bioassays for?>?10,000 chemicals that were distributed unevenly between binary classes. With Random Forest (RF) as the base classifier and bagging as the ensemble strategy, we applied four hybrid learning methods, i.e., RF without imbalance handling (RF), RF with Random Undersampling (RUS), RF with SMOTE (SMO), and RF with SMOTEENN (SMN). The performance of the four learning methods was compared using nine evaluation metrics, among which F1 score, Matthews correlation coefficient and Brier score provided a more consistent assessment of the overall performance across the 12 datasets. The Friedman's aligned ranks test and the subsequent Bergmann-Hommel post hoc test showed that SMN significantly outperformed the other three methods. We also found that a strong negative correlation existed between the prediction accuracy and the imbalance ratio (IR), which is defined as the number of inactive compounds divided by the number of active compounds. SMN became less effective when IR exceeded a certain threshold (e.g.,?>?28). The ability to separate the few active compounds from the vast amounts of inactive ones is of great importance in computational toxicology. This work demonstrates that the performance of SAR-based, imbalanced chemical toxicity classification can be significantly improved through the use of data rebalancing.

Dataset Information

Flavones' and Flavonols' Antiradical Structure-Activity Relationship-A Quantum Chemical Study.

Publications

Flavones' and Flavonols' Antiradical Structure-Activity Relationship-A Quantum Chemical Study.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets