scholarly journals Quantitative Structure Activity Relationship for Drug Discovery

Quantitative structure-activity relationship (QSAR), gives useful information for drug design and medicinal chemistry. QSAR is a method used to anticipate the organic reaction of a molecule by developing equations which use descriptors calculated from its compounds. The molecular descriptors vary in complexity. A time consuming and expensive process for pharmaceutical industries is drug discovery. An inspiration driving these QSAR models is to help revive the revelation of molecular drug candidates through minimized test work and to bring a drug to market faster. To obtain sorted features principal component analysis is used. The biological activities of the test set are determined by training the neural network using training set. By predicting the activities it can be known whether the drug is close to the target or not.

Author(s):  
Meysam Shirmohammadi ◽  
Zakiyeh Bayat ◽  
Esmat Mohammadinasab

: Quantitative structure activity relationship (QSAR) was used to study the partition coefficient of some quinolones and their derivatives. These molecules are broad-spectrum antibiotic pharmaceutics. First, data were divided into two categories of train and test (validation) sets using random selection method. Second, three approaches including stepwise selection (STS) (forward), genetic algorithm (GA), and simulated annealing (SA) were used to select the descriptors, with the aim of examining the effect feature selection methods. To find the relation between descriptors and partition coefficient, multiple linear regression (MLR), principal component regression (PCR) and partial least squares (PLS) were used. QSAR study showed that the both regression and descriptor selection methods have vital role in the results. Different statistical metrics showed that the MLR-SA approach with (r2=0.96, q2=0.91, pred_r2=0.95) gives the best outcome. The proposed expression by MLR-SA approach can be used in the better design of novel quinolones and their derivatives.


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.


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