Browse
Submit Data
Databases
API
Help

Dataset Information

36 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Support vector regression-based QSAR models for prediction of antioxidant activity of phenolic compounds.

ABSTRACT: The Support vector regression (SVR) was used to investigate quantitative structure-activity relationships (QSAR) of 75 phenolic compounds with Trolox-equivalent antioxidant capacity (TEAC). Geometric structures were optimized at the EF level of the MOPAC software program. Using Pearson correlation coefficient analysis, four molecular descriptors [n(OH), Cosmo Area (CA), Core-Core Repulsion (CCR) and Final Heat of Formation (FHF)] were selected as independent variables. The QSAR model was developed from the training set consisting of 57 compounds and then used the leave-one-out cross-validation (LOOCV) correlation coefficient to evaluate the prediction ability of the QSAR model. Used Artificial neural network (ANN) and multiple linear regression (MLR) for comparing. The RMSE (root mean square error) values of LOOCV in SVR, ANN and MLR models were 0.44, 0.46 and 0.54. The RMSE values of prediction of external 18 compounds were 0.41, 0.39 and 0.54 for SVR, ANN and MLR models, respectively. The obtained result indicated that the SVR models exhibited excellent predicting performance and competent for predicting the TEAC of phenolic compounds.

SUBMITTER: Shi Y

PROVIDER: S-EPMC8062522 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Similar Datasets

Multilayer perceptron and support vector regression models for feline parturition date prediction.

Project description:A crucial challenge in feline obstetric care is the accurate prediction of the parturition date during late pregnancy. The classic simple linear regression (SLR) model, which employed the fetal biparietal diameter (BPD) as the single input feature, was frequently applied for such prediction with limited accuracy. Since Multilayer Perceptron (MLP) and Support Vector Regression (SVR) are now two of the most potent scientific regression models, this study, for the first time, introduced such models as the new promising tools for feline parturition date prediction. The following features were candidate inputs for our models: biparietal diameter (BPD), litter size, and maternal weight. We observed and compared the performance results for each model. As the best-performed model, MLP delivered the highest coefficient score (0.972 ± 0.006), lowest mean absolute error score (1.110 ± 0.060), and lowest mean squared error score (1.540 ± 0.141), respectively. For the first time in this study, BPD, litter size, and maternal weight were considered the essential features for the innovative MLP and SVR modeling. With the optimized model parameters and the described analytical platform, further verification of these advanced models in feline obstetric practices is feasible.

| S-EPMC10963322 | biostudies-literature

Prediction of the aquatic toxicity of aromatic compounds to tetrahymena pyriformis through support vector regression.

Project description:Toxicity evaluation is an extremely important process during drug development. It is usually initiated by experiments on animals, which is time-consuming and costly. To speed up such a process, a quantitative structure-activity relationship (QSAR) study was performed to develop a computational model for correlating the structures of 581 aromatic compounds with their aquatic toxicity to tetrahymena pyriformis. A set of 68 molecular descriptors derived solely from the structures of the aromatic compounds were calculated based on Gaussian 03, HyperChem 7.5, and TSAR V3.3. A comprehensive feature selection method, minimum Redundancy Maximum Relevance (mRMR)-genetic algorithm (GA)-support vector regression (SVR) method, was applied to select the best descriptor subset in QSAR analysis. The SVR method was employed to model the toxicity potency from a training set of 500 compounds. Five-fold cross-validation method was used to optimize the parameters of SVR model. The new SVR model was tested on an independent dataset of 81 compounds. Both high internal consistent and external predictive rates were obtained, indicating the SVR model is very promising to become an effective tool for fast detecting the toxicity.

| S-EPMC5564774 | biostudies-literature

Antioxidant Activity of Synthetic Polymers of Phenolic Compounds.

Project description:In recent years, developing potent antioxidants has been a very active area of research. In this context, phenolic compounds have been evaluated for their antioxidant activity. However, the use of phenolic compounds has also been limited by poor antioxidant activity in several in vivo studies. Polymeric phenols have received much attention owing to their potent antioxidant properties and increased stability in aqueous systems. To be truly effective in biological applications, it is important that these polymers be synthesized using benign methods. In this context, enzyme catalyzed synthesis of polymeric phenols has been explored as an environmentally friendly and safer approach. This review summarizes work in enzymatic syntheses of polymers of phenols. Several assays have been developed to determine the antioxidant potency of these polymeric phenols. These assays are discussed in detail along with structure-property relationships. A deeper understanding of factors affecting antioxidant activity would provide an opportunity for the design of versatile, high performing polymers with enhanced antioxidant activity.

| S-EPMC7464737 | biostudies-literature

QSAR study of phenolic compounds and their anti-DPPH radical activity by discriminant analysis.

Project description:Phenolic compounds (PCs) could be applied to reduce reactive oxygen species (ROS) levels, and are used to prevent and treat diseases related to oxidative stress. QSAR study was applied to elucidate the relationship between the molecular descriptors and physicochemical properties of polyphenol analogues and their DPPH radical scavenging capability, to guide the design and discovery of highly-potent antioxidant substances more efficiently. PubMed database was used to collect 99 PCs with antioxidant activity, whereas, 105 negative PCs were found in ChEMBL database; their molecular descriptors were generated with Python's Rdkit package. While the molecular descriptors significantly related to the antioxidant activity of PCs were filtered by t-test. The prediction QSAR model was then established by discriminant analysis, and the obtained model was verified by the back-substitution and Leave-One-Out cross-validation methods along with heat map. It was revealed that the anti-DPPH radical activity of PCs was correlated with the drug-likeness and molecular fingerprints, physicochemical, topological, constitutional and electronic property. The established QSAR model could explicitly predict the antioxidant activity of polyphenols, thus were applicable to evaluate the potential of candidates as antioxidants.

| S-EPMC9098848 | biostudies-literature

In Silico Prediction of Intestinal Permeability by Hierarchical Support Vector Regression.

Project description:The vast majority of marketed drugs are orally administrated. As such, drug absorption is one of the important drug metabolism and pharmacokinetics parameters that should be assessed in the process of drug discovery and development. A nonlinear quantitative structure-activity relationship (QSAR) model was constructed in this investigation using the novel machine learning-based hierarchical support vector regression (HSVR) scheme to render the extremely complicated relationships between descriptors and intestinal permeability that can take place through various passive diffusion and carrier-mediated active transport routes. The predictions by HSVR were found to be in good agreement with the observed values for the molecules in the training set (n = 53, r2 = 0.93, q CV 2 = 0.84, RMSE = 0.17, s = 0.08), test set (n = 13, q2 = 0.75-0.89, RMSE = 0.26, s = 0.14), and even outlier set (n = 8, q2 = 0.78-0.92, RMSE = 0.19, s = 0.09). The built HSVR model consistently met the most stringent criteria when subjected to various statistical assessments. A mock test also assured the predictivity of HSVR. Consequently, this HSVR model can be adopted to facilitate drug discovery and development.

| S-EPMC7279352 | biostudies-literature

Antioxidant Activity of Selected Phenolic Acids-Ferric Reducing Antioxidant Power Assay and QSAR Analysis of the Structural Features.

Project description:Phenolic acids are naturally occurring compounds that are known for their antioxidant and antiradical activity. We present experimental and theoretical studies on the antioxidant potential of the set of 22 phenolic acids with different models of hydroxylation and methoxylation of aromatic rings. Ferric reducing antioxidant power assay was used to evaluate this property. 2,3-dihydroxybenzoic acid was found to be the strongest antioxidant, while mono hydroxylated and methoxylated structures had the lowest activities. A comprehensive structure-activity investigation with density functional theory methods elucidated the influence of compounds topology, resonance stabilization, and intramolecular hydrogen bonding on the exhibited activity. The key factor was found to be a presence of two or more hydroxyl groups being located in ortho or para position to each other. Finally, the quantitative structure-activity relationship approach was used to build a multiple linear regression model describing the dependence of antioxidant activity on structure of compounds, using features exclusively related to their topology. Coefficients of determination for training set and for the test set equaled 0.9918 and 0.9993 respectively, and Q2 value for leave-one-out was 0.9716. In addition, the presented model was used to predict activities of phenolic acids that haven't been tested here experimentally.

| S-EPMC7412039 | biostudies-literature

Profiling of Phenolic Compounds and Antioxidant Activity of 12 Cruciferous Vegetables.

Project description:The phenolic profiles of 12 cruciferous vegetables (pakchoi, choysum, Chinese cabbage, kailan, Brussels sprout, cabbage, cauliflower, broccoli, rocket salad, red cherry radish, daikon radish, and watercress) were studied with UHPLC-MS/MS. Antioxidant activity and total phenolic content (TPC) were also evaluated. A total of 74 phenolic compounds were identified, including 16 hydroxycinnamic acids and derivatives, and 58 flavonoids and derivatives. The main flavonoids identified were glycosylated quercetin, kaempferol and isorhamnetin, and the main hydroxycinnamic acids were ferulic, sinapic, caffeic and p-coumaric acids. Principal component analysis (PCA) revealed that the distribution of phenolic compounds in different genera of cruciferous vegetables was in accordance with their conventional taxonomy. The DPPH, ORAC and TPC values ranged from 1.11 to 9.54 µmoles Trolox equivalent/g FW, 5.34 to 32.92 µmoles Trolox equivalent/g FW, and 0.16 to 1.93 mg gallic acid equivalent/g FW respectively. Spearman’s correlation showed significant (p < 0.05) positive correlations between TPC, flavonoids and antioxidant activity.

| S-EPMC6100362 | biostudies-literature

Antioxidant Activity and Profile of Phenolic Compounds in Selected Herbal Plants.

Project description:Herbs are characterized by a high content of biologically active substances that positively affect human health. Phenolic compounds are one of the main bioactive compounds in these plants with highly beneficial properties (e.g., anti-carcinogenic, cardioprotective, immune system support and antibacterial). Therefore, this study aimed to determine the composition of free and bound phenolic compounds and antioxidant activity in 10 different herbs (bogbean leaves, sage leaves, whole Indian hemp, whole heartsease, whole horsetail, whole blessed thistle, whole thyme, chamomile flower, corn silk and pine buds). Phenolic compounds were analyzed using the HPLC-TOF-MS/MS method, total phenolic (TP) and flavonoid (TF) contents were measured using spectrophotometric methods, while antioxidant activity was determined by ABTS and DPPH assays. The highest content of free TP was found in thyme, while sage was characterized by the highest range of these compounds released from ester and glycosidic derivatives by hydrolysis. In turn, the highest values of TF in non-hydrolyzed extracts were found in Indian hemp. The highest values of TF, including bound phenols were observed in extracts obtained from sage leaves. In the analyzed herbs thirty-three phenolic compounds were found, constituting 16 phenolic acids, 9 flavonoids and eight stilbenes. Moreover, the analyzed compounds appeared in the bound form than in the free form. The antioxidant activity of herb extracts differed significantly across varieties (P < 0.05). The research showed that each tested herb possesses its own fingerprint of phenolic compounds and antioxidant properties.

| S-EPMC9463321 | biostudies-literature

Systematic artifacts in support vector regression-based compound potency prediction revealed by statistical and activity landscape analysis.

Project description:Support vector machines are a popular machine learning method for many classification tasks in biology and chemistry. In addition, the support vector regression (SVR) variant is widely used for numerical property predictions. In chemoinformatics and pharmaceutical research, SVR has become the probably most popular approach for modeling of non-linear structure-activity relationships (SARs) and predicting compound potency values. Herein, we have systematically generated and analyzed SVR prediction models for a variety of compound data sets with different SAR characteristics. Although these SVR models were accurate on the basis of global prediction statistics and not prone to overfitting, they were found to consistently mispredict highly potent compounds. Hence, in regions of local SAR discontinuity, SVR prediction models displayed clear limitations. Compared to observed activity landscapes of compound data sets, landscapes generated on the basis of SVR potency predictions were partly flattened and activity cliff information was lost. Taken together, these findings have implications for practical SVR applications. In particular, prospective SVR-based potency predictions should be considered with caution because artificially low predictions are very likely for highly potent candidate compounds, the most important prediction targets.

| S-EPMC4350943 | biostudies-literature

DomSVR: domain boundary prediction with support vector regression from sequence information alone.

Project description:Protein domains are structural and fundamental functional units of proteins. The information of protein domain boundaries is helpful in understanding the evolution, structures and functions of proteins, and also plays an important role in protein classification. In this paper, we propose a support vector regression-based method to address the problem of protein domain boundary identification based on novel input profiles extracted from AAindex database. As a result, our method achieves an average sensitivity of approximately 36.5% and an average specificity of approximately 81% for multi-domain protein chains, which is overall better than the performance of published approaches to identify domain boundary. As our method used sequence information alone, our method is simpler and faster.

| S-EPMC2909371 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data