Quantitative Structure-activity Relationship (QSAR) models for color and COD removal for some dyes subjected to electrochemical oxidation

2022 ◽  
pp. 1-30
Author(s):  
Katha S. Hirpara ◽  
Upendra D. Patel
Foods ◽  
2019 ◽  
Vol 8 (12) ◽  
pp. 628 ◽  
Author(s):  
Rosa Perestrelo ◽  
Catarina Silva ◽  
Miguel X. Fernandes ◽  
José S. Câmara

Terpenoids, including monoterpenoids (C10), norisoprenoids (C13), and sesquiterpenoids (C15), constitute a large group of plant-derived naturally occurring secondary metabolites with highly diverse chemical structures. A quantitative structure–activity relationship (QSAR) model to predict terpenoid toxicity and to evaluate the influence of their chemical structures was developed in this study by assessing in real time the toxicity of 27 terpenoid standards using the Gram-negative bioluminescent Vibrio fischeri. Under the test conditions, at a concentration of 1 µM, the terpenoids showed a toxicity level lower than 5%, with the exception of geraniol, citral, (S)-citronellal, geranic acid, (±)-α-terpinyl acetate, and geranyl acetone. Moreover, the standards tested displayed a toxicity level higher than 30% at concentrations of 50–100 µM, with the exception of (+)-valencene, eucalyptol, (+)-borneol, guaiazulene, β-caryophellene, and linalool oxide. Regarding the functional group, terpenoid toxicity was observed in the following order: alcohol > aldehyde ~ ketone > ester > hydrocarbons. The CODESSA software was employed to develop QSAR models based on the correlation of terpenoid toxicity and a pool of descriptors related to each chemical structure. The QSAR models, based on t-test values, showed that terpenoid toxicity was mainly attributed to geometric (e.g., asphericity) and electronic (e.g., maximum partial charge for a carbon (C) atom (Zefirov’s partial charge (PC)) descriptors. Statistically, the most significant overall correlation was the four-parameter equation with a training coefficient and test coefficient correlation higher than 0.810 and 0.535, respectively, and a square coefficient of cross-validation (Q2) higher than 0.689. According to the obtained data, the QSAR models are suitable and rapid tools to predict terpenoid toxicity in a diversity of food products.


2015 ◽  
Vol 14 (06) ◽  
pp. 1550040 ◽  
Author(s):  
Anuradha Sharma ◽  
Poonam Piplani

Alzheimer's disease (AD) is the most common cause of dementia in old aged people and clinically used drugs for treatment are associated with side effects. Thus, there is a current demand for the discovery and development of new potential molecules. However, the recent advances in drug therapy have challenged the predominance of the disease. In this manuscript, an attempt has been made to develop the 2D and 3D quantitative structure–activity relationship (QSAR) models for a series of rutaecarpine, quinazolines and 7,8-dehydrorutaecarpine derivatives to obtain insights to Acetylcholinesterase (AChE) inhibition. Five different QSAR models have been generated and validated using a set of 52 compounds comprising of varying scaffolds with IC50 values ranging from 11,000 nM to 0.6 nM. These AChE-specific prediction models (M1–M5) adequately reflect the structure–activity relationship of the existing AChE inhibitors. Out of all developed models, QSAR model generated using ADME properties has been found to be the best with satisfactory statistical significance (regression (r2) of 0.9309 and regression adjusted coefficient of variation [Formula: see text] of 0.9194). The QSAR models highlight the importance of aromatic moiety as their presence in the structure influence the biological activity. Additional insights on the compounds show that acyclic amines attached to side chain have lower activity than cyclic amines. The QSAR models pinpointing structural basis for the AChEIs suggest new guidelines for the design of novel molecules.


2016 ◽  
Vol 36 (1-2) ◽  
pp. 1600013 ◽  
Author(s):  
Yoshifumi Fukunishi ◽  
Satoshi Yamasaki ◽  
Isao Yasumatsu ◽  
Koh Takeuchi ◽  
Takashi Kurosawa ◽  
...  

Molecules ◽  
2019 ◽  
Vol 24 (23) ◽  
pp. 4393
Author(s):  
Saw Simeon ◽  
Nathjanan Jongkon

Janus kinase 2 (JAK2) inhibitors represent a promising therapeutic class of anticancer agents against many myeloproliferative disorders. Bioactivity data on pIC 50 of 2229 JAK2 inhibitors were employed in the construction of quantitative structure-activity relationship (QSAR) models. The models were built from 100 data splits using decision tree (DT), support vector machine (SVM), deep neural network (DNN) and random forest (RF). The predictive power of RF models were assessed via 10-fold cross validation, which afforded excellent predictive performance with R 2 and RMSE of 0.74 ± 0.05 and 0.63 ± 0.05, respectively. Moreover, test set has excellent performance of R 2 (0.75 ± 0.03) and RMSE (0.62 ± 0.04). In addition, Y-scrambling was utilized to evaluate the possibility of chance correlation of the predictive model. A thorough analysis of the substructure fingerprint count was conducted to provide insights on the inhibitory properties of JAK2 inhibitors. Molecular cluster analysis revealed that pyrazine scaffolds have nanomolar potency against JAK2.


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