Interpretation of ANN‐based QSAR models for prediction of antioxidant activity of flavonoids

Abstract

Quantitative structure-activity relationships (QSARs) built using machine learning methods, such as artificial neural networks (ANNs) are powerful in prediction of (antioxidant) activity from quantum mechanical (QM) parameters describing the molecular structure, but are usually not interpretable. This obvious difficulty is one of the most common obstacles in application of ANN-based QSAR models for design of potent antioxidants or elucidating the underlying mechanism. Interpreting the resulting models is often omitted or performed erroneously altogether. In this work, a comprehensive comparative study of six methods (PaD, PaD , weights, stepwise, perturbation and profile) for exploration and interpretation of ANN models built for prediction of Trolox-equivalent antioxidant capacity (TEAC) QM descriptors, is presented. Sum of ranking differences (SRD) was used for ranking of the six methods with respect to the contributions of the calculated QM molecular descriptors toward TEAC. The results show that the PaD, PaD and profile methods are the most stable and give rise to realistic interpretation of the observed correlations. Therefore, they are safely applicable for future interpretations without the opinion of an experienced chemist or bio-analyst. © 2018 Wiley Periodicals, Inc.

Structure–Activity Relationship and Prediction of the Electron‐Transfer Potential of the Xanthones Series

“…Previously, several studies reported that the catechol moiety played a critical role in phenolic antioxidants . However, Woodman insisted on a minor role of the catechol moiety .…”

“…Moreover, all subtypes are composed of a dihedral angle between the B ring and the A/C fused rings. Thus, it is difficult to predict the ET (or antioxidant) potential of flavonoids, regardless of the amount of antioxidant structure–activity relationship studies …”

“…For example, among the thousands of documented flavonoids, there are only a few flavonoids with a hydroquinone moiety, and they include rhodionin, 5,8‐dihydroxy‐3,6,7‐trimethoxyflavone, 5,8‐dihydroxy‐6,7‐dimethoxyflavone, 5,8‐dihydroxy‐6,7,4′‐trimethoxyflavone, 5,8‐dihydroxy‐6,7,3′,4′,5′‐pentamethoxyflavone, and isothymusin . This hydroquinone moiety is hardly found in other phenolics . As a result, its antioxidant role has not yet been explored.…”

“…Polygala japonica), [3] the attention they have received is not the same. Whereass ome studies have analyzed the structure-activity relationships of antioxidant flavonoids, [4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21] there are none that have focused on the structure-activity relationships of antioxidant xanthones.…”

Mixtures of QSAR models: Learning application domains of pKpredicto rs

“…Quantitative structure‐activity relationship models (QSAR models) apply the toolbox of chemometrics and chemoinformatics to predict the chemical, physical, or biological properties of chemicals based on a set of descriptor variables that describe the structure of the chemicals . The main areas of the application of QSAR models are the modelling of different biological activities (eg, antimalarial, antioxidant, and anti‐HIV), chemical properties (eg, chromatographic retention data and biodegradation), and toxic endpoints (eg, genotoxicity and hepatotoxicity). Moreover, besides predicting the properties of certain molecular structures, the inverse task, the design of molecular structures for certain properties is achieved as well, as these QSAR models are a time‐effective and economically friendly way to design new drugs .…”