Predicting the adsorption of organic pollutants on boron nitride nanosheets via in silico techniques: DFT computations and QSAR modeling

2021 ◽  
Author(s):  
Ya Wang ◽  
Weihao Tang ◽  
Yue Peng ◽  
Zhongfang Chen ◽  
Jingwen Chen ◽  
...  
Keyword(s):  
Boron Nitride ◽  
Organic Pollutants ◽  
In Silico ◽  
Quantitative Structure Activity Relationship ◽  
Quantitative Structure ◽  
Qsar Modeling ◽  
Boron Nitride Nanosheets ◽  
Structure Activity ◽  
Aqueous Environments ◽  
Qsar Models

Four quantitative structure–activity relationship (QSAR) models were developed for predicting the log K values of organic pollutants adsorbed onto boron nitride nanosheets in gaseous and aqueous environments.

QSAR modeling for reaction rate constants of eaq− with diverse organic compounds in water

2020 ◽  
Vol 6 (7) ◽  
pp. 1931-1938
Author(s):  
Shanshan Zheng ◽  
Chao Li ◽  
Gaoliang Wei
Keyword(s):  
Organic Compounds ◽  
Reaction Rate ◽  
Quantitative Structure Activity Relationship ◽  
Structural Features ◽  
Quantitative Structure ◽  
Qsar Modeling ◽  
Reaction Rate Constants ◽  
Structure Activity ◽  
Qsar Models ◽  
The Impact

Two quantitative structure–activity relationship (QSAR) models to predict keaq− of diverse organic compounds were developed and the impact of molecular structural features on eaq− reactivity was investigated.

Workshop 1.6 QSAR and related procedures for prescreening and predicting potential endocrine-active compounds

2004 ◽  
Vol 76 (10) ◽  
pp. 1927-1931
Author(s):  
T. Fujita
Keyword(s):  
In Silico ◽  
Model Building ◽  
Quantitative Structure Activity Relationship ◽  
Quantitative Prediction ◽  
Quantitative Structure ◽  
Activity Data ◽  
Endocrine Activity ◽  
The Past ◽  
Structure Activity ◽  
Computer Aided

This workshop has been organized to cover various quantitative structure-activity relationship (QSAR) and computer aided procedures currently carried out for the prediction of the endocrine activity of unknown compounds. Each of the procedures has own scope as well as limitations. It seems inappropriate to consider that a single quantitative prediction model derived from each of these procedures could solve the entire issue. Because the model building is highly dependent on the data/knowledge about endocrine activity of a large number of existing compounds accumulated to date and the data/knowledge are growing constantly, the model has a destiny to be amended “forever ”as the structure-activity data of newly synthesized compounds are accumulated. The skepticism about in silico and QSAR procedures put forward in the past is likely to be cleared at least to some extent if not entirely by participating in this workshop.

scholarly journals Prediction of Terpenoid Toxicity Based on a Quantitative Structure–Activity Relationship Model

Foods ◽  
2019 ◽  
Vol 8 (12) ◽  
pp. 628 ◽  
Author(s):  
Rosa Perestrelo ◽  
Catarina Silva ◽  
Miguel X. Fernandes ◽  
José S. Câmara
Keyword(s):  
Vibrio Fischeri ◽  
Quantitative Structure Activity Relationship ◽  
Structure Activity Relationship ◽  
Activity Relationship ◽  
Partial Charge ◽  
Quantitative Structure ◽  
Chemical Structures ◽  
Structure Activity ◽  
Qsar Models ◽  
Toxicity Level

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.

Understanding the quantitative structure–activity relationship of acetylcholinesterase inhibitors for the treatment of Alzheimer's disease

2015 ◽  
Vol 14 (06) ◽  
pp. 1550040 ◽  
Author(s):  
Anuradha Sharma ◽  
Poonam Piplani
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Quantitative Structure Activity Relationship ◽  
Structure Activity Relationship ◽  
Activity Relationship ◽  
Structural Basis ◽  
Quantitative Structure ◽  
Structure Activity ◽  
Qsar Models ◽  
Relationship Of

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.

Sign in / Sign up

Export Citation Format

Share Document