Wood apple-Its nutritive value and medicinal benefits

Yadav, Tripti; Vishwakarma, Deepak; Saloni, Shweta; Tiwari, Sandeep; Sindhu,

doi:10.15740/has/ijae/11.sp.issue/159-163

Cited by 3 publications

(2 citation statements)

References 4 publications

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“…The table below presents the predicted pIC 50 values by both field-based and atom-based QSAR models, along with the residual errors compared to the experimental pIC 50 values. The accurate alignment of compounds is crucial for ensuring the quality and predicted activity of both the 3D-QSAR and pharmacophore models [40]. Initially, the molecular structures were in the 2D-SDF format and subsequently converted into 3D structures (Figure 5).…”

Section: Datasetmentioning

confidence: 99%

Computational 3D Modeling-Based Identification of Inhibitors Targeting Cysteine Covalent Bond Catalysts for JAK3 and CYP3A4 Enzymes in the Treatment of Rheumatoid Arthritis

Faris,

Alnajjar,

Guo

et al. 2023

Molecules

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This work aimed to find new inhibitors of the CYP3A4 and JAK3 enzymes, which are significant players in autoimmune diseases such as rheumatoid arthritis. Advanced computer-aided drug design techniques, such as pharmacophore and 3D-QSAR modeling, were used. Two strong 3D-QSAR models were created, and their predictive power was validated by the strong correlation (R2 values > 80%) between the predicted and experimental activity. With an ROC value of 0.9, a pharmacophore model grounded in the DHRRR hypothesis likewise demonstrated strong predictive ability. Eight possible inhibitors were found, and six new inhibitors were designed in silico using these computational models. The pharmacokinetic and safety characteristics of these candidates were thoroughly assessed. The possible interactions between the inhibitors and the target enzymes were made clear via molecular docking. Furthermore, MM/GBSA computations and molecular dynamics simulations offered insightful information about the stability of the binding between inhibitors and CYP3A4 or JAK3. Through the integration of various computational approaches, this study successfully identified potential inhibitor candidates for additional investigation and efficiently screened compounds. The findings contribute to our knowledge of enzyme–inhibitor interactions and may help us create more effective treatments for autoimmune conditions like rheumatoid arthritis.

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Section: Datasetmentioning

confidence: 99%

Computational 3D Modeling-Based Identification of Inhibitors Targeting Cysteine Covalent Bond Catalysts for JAK3 and CYP3A4 Enzymes in the Treatment of Rheumatoid Arthritis

Faris,

Alnajjar,

Guo

et al. 2023

Molecules

View full text Add to dashboard Cite

show abstract

“…They also performed molecular docking simulations and protein−ligand interaction pattern analyses. 3 From 2016 to 2018, a number of 2D- 23−25 and 3D-QSAR studies 26−30 on small data sets with different COX-2 inhibitor scaffolds, such as dihydropyridine and hydroquinoline derivatives, 23 indole derivatives, 24 1,5-disubstituted tetrazoles, 25 hydroquinoline and thiazinan-4-one derivatives, 26 resveratrol derivatives, 27,29 and cis-stilbene derivatives, 30 have been reported. In addition, several molecular docking studies 25,31 and molecular dynamics simulations 32 were also carried out.…”

Section: Introductionmentioning

confidence: 99%

Classification of Cyclooxygenase-2 Inhibitors Using Support Vector Machine and Random Forest Methods

Qin

Yang

Zhang

et al. 2019

J. Chem. Inf. Model.

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This work reports the classification study conducted on the biggest COX-2 inhibitor data set so far. Using 2925 diverse COX-2 inhibitors collected from 168 pieces of literature, we applied machine learning methods, support vector machine (SVM) and random forest (RF), to develop 12 classification models. The best SVM and RF models resulted in MCC values of 0.73 and 0.72, respectively. The 2925 COX-2 inhibitors were reduced to a data set of 1630 molecules by removing intermediately active inhibitors, and 12 new classification models were constructed, yielding MCC values above 0.72. The best MCC value of the external test set was predicted to be 0.68 by the RF model using ECFP_4 fingerprints. Moreover, the 2925 COX-2 inhibitors were clustered into eight subsets, and the structural features of each subset were investigated. We identified substructures important for activity including halogen, carboxyl, sulfonamide, and methanesulfonyl groups, as well as the aromatic nitrogen atoms. The models developed in this study could serve as useful tools for compound screening prior to lab tests.

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Effect of maturity stages on the quality indices of wood apple (Feronia limonia) and modeling of its kinetics by applying machine learning approaches

Goyary,

Khobragade,

Chakraborty

et al. 2023

J. Hortic. Sci.

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In the present investigation, an inexpensive and non-destructive method was tested for the appropriate maturity classification of wood apple (Feronia limonia). The investigation was conducted to establish the pronounced effect of maturity stages on the growth kinetics, physico-chemical properties, and other quality indices of wood apple. A systematic trend was observed for all the properties namely sphericity, bulk density (g/cm3), true density (g/cm3), pH, total soluble solids TSS (°Brix), titratable acidity (%) and TSS/TA ratio, etc. of the fruit. In contrast, regular changes were also observed in the color properties at various maturity stages of the wood apple. The maturity kinetics was formulated by applying recurrent neural network (RNN) in compliance with K means cluster algorithm. RNN modeling was applied by considering color property (redness value) as input and six maturity indices as the output of the formulated structure. The RNN architecture, 1-6-6 showed the best results for forecasting the wood apple maturity based on color features. Further, based on the results of the K means cluster algorithm, the maturity stages were classified into three main categories, illustrated in the form of a simplified color chart. Hence, this investigation can be useful for proper control and identification of wood apple maturity during the processing.

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Wood apple-Its nutritive value and medicinal benefits

Cited by 3 publications

References 4 publications

Computational 3D Modeling-Based Identification of Inhibitors Targeting Cysteine Covalent Bond Catalysts for JAK3 and CYP3A4 Enzymes in the Treatment of Rheumatoid Arthritis

Computational 3D Modeling-Based Identification of Inhibitors Targeting Cysteine Covalent Bond Catalysts for JAK3 and CYP3A4 Enzymes in the Treatment of Rheumatoid Arthritis

Classification of Cyclooxygenase-2 Inhibitors Using Support Vector Machine and Random Forest Methods

Effect of maturity stages on the quality indices of wood apple (Feronia limonia) and modeling of its kinetics by applying machine learning approaches

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