Quantitative Structure Activity Relationship (QSAR) study predicts small molecule binding to RNA structure

2021 ◽  
Author(s):  
Zhengguo Cai ◽  
Martina Zafferani ◽  
Olanrewaju Akande ◽  
Amanda Hargrove
Keyword(s):  
High Resolution ◽  
Small Molecules ◽  
Small Molecule ◽  
Rna Structure ◽  
Quantitative Structure ◽  
Rna Structures ◽  
Qsar Study ◽  
Structure Activity ◽  
Qsar Models ◽  
Hiv 1

The diversity of RNA structural elements and their documented role in human diseases make RNA an attractive therapeutic target. However, progress in drug discovery and development has been hindered by challenges in the determination of high-resolution RNA structures and a limited understanding of the parameters that drive RNA recognition by small molecules, including a lack of validated quantitative structure-activity relationships (QSAR). Herein, we developed QSAR models that quantitatively predict both thermodynamic and kinetic-based binding parameters of small molecules and the HIV-1 TAR model RNA system. A set of small molecules bearing diverse scaffolds was screened against the HIV-1-TAR construct using surface plasmon resonance, which provided the binding kinetics and affinities. The data was then analyzed using multiple linear regression (MLR) combined with feature selection to afford robust models for binding of diverse RNA-targeted scaffolds. The predictivity of the model was validated on untested small molecules. The QSAR models presented herein represent the first application of validated and predictive 2D-QSAR using multiple scaffolds against an RNA target. We expect the workflow to be generally applicable to other RNA structures, ultimately providing essential insight into the small molecule descriptors that drive selective binding interactions and, consequently, providing a platform that can exponentially increase the efficiency of ligand design and optimization without the need for high-resolution RNA structures.

Quantitative structure–activity relationships study of potent pyridinone scaffold derivatives as HIV-1 integrase inhibitors with therapeutic applications

2017 ◽  
Vol 16 (05) ◽  
pp. 1750038 ◽  
Author(s):  
Abolfazl Barzegar ◽  
Hossein Hamidi
Keyword(s):  
Therapeutic Agents ◽  
Quantitative Structure ◽  
Structure Activity Relationships ◽  
Structure Activity ◽  
Qsar Models ◽  
Stepwise Procedure ◽  
Quantitative Structure Activity Relationships ◽  
Anti Hiv ◽  
Hiv 1 ◽  
Mlr Model

Human immunodeficiency virus-1 (HIV-1) integrase appears to be a crucial target for developing new anti-HIV-1 therapeutic agents. Different quantitative structure–activity relationships (QSARs) algorithms have been used in order to develop efficient model(s) to predict the activity of new pyridinone derivatives against HIV-1 integrase. Multiple linear regression (MLR) and combined principal component analysis (PCA) with MLR have been applied to build QSAR models for a set of new pyridinone derivatives as potent anti-HIV-1 therapeutic agents. Four different approaches based on MLR method including; concrete-MLR, stepwise-MLR, concrete PCA–MLR and stepwise PCA–MLR were utilized for this aim. Twenty two different sets of descriptors containing 1613 descriptors were constructed for each optimized molecule. Comparison between predictability of the “concrete” and “stepwise” procedure in two different algorithms of MLR and PCA models indicated the advantage of the stepwise procedure over that of the simple concrete method. Although the PCA was employed for dimension reduction, using stepwise PCA–MLR model showed that the method has higher ability to predict the compounds’ activity. The stepwise PCA–MLR model showed highly validated statistical results both in fitting and prediction processes ([Formula: see text] and [Formula: see text]). Therefore, using stepwise PCA approach is suitable to remove ineffective descriptors, which results in remaining efficient descriptors for building good predictability stepwise PCA–MLR. The stepwise hybrid approach of PCA–MLR may be useful in derivation of highly predictive and interpretable QSAR models.

scholarly journals QSAR investigation of acute toxicity of organic compounds during oral administration to mice

2019 ◽  
Vol 65 (2) ◽  
pp. 123-132 ◽  
Author(s):  
O.V. Tinkov ◽  
V.Yu. Grigorev ◽  
P.G. Polishchuk ◽  
A.V. Yarkov ◽  
O.A. Raevsky
Keyword(s):  
Acute Toxicity ◽  
Organic Compounds ◽  
Quantitative Structure Activity Relationship ◽  
Mechanisms Of Action ◽  
Arithmetic Mean ◽  
Quantitative Structure ◽  
Qsar Study ◽  
Structure Activity ◽  
Qsar Models ◽  
On Line

The effect of the structure of organic compounds on the acute toxicity upon oral injection in mice was studied using 2D simplex representation of the molecular structure and Random forest (RF) methods. Satisfactory quantitative structure-activity relationship (QSAR) models were constructed (R2 test = 0,61–0,62). The interpretation of the obtained QSAR models was carried out. The contributions of known toxicophores with established mechanisms of action were calculated in order to confirm the ability of the interpretation approach to correctly rank them relative to other structural fragments. The influence of the molecular surroundings of some toxicophores was analyzed. We analyzed the contributions of other highly ranked fragments from the list of common functional groups and ring systems in order to find new potential toxicophores. The on-line version of the expert system “OCHEM” (https://ochem.eu) and Arithmetic Mean Toxicity (AMT) approach were used for a comparative QSAR study.

Prediction of Lipophilicity of some Quinolone Derivatives by using Quantitative Structure- Activity Relationship

2019 ◽  
Vol 16 ◽  
Author(s):  
Meysam Shirmohammadi ◽  
Zakiyeh Bayat ◽  
Esmat Mohammadinasab
Keyword(s):  
Partition Coefficient ◽  
Principal Component ◽  
Quantitative Structure Activity Relationship ◽  
Structure Activity Relationship ◽  
Activity Relationship ◽  
Quantitative Structure ◽  
Selection Methods ◽  
Qsar Study ◽  
Structure Activity ◽  
Descriptor Selection

: 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.

Studies on the EC50 of Natural Monoterpenes as Fungal Inhibitors with Quantitative Structure-Activity Relationships (QSARs)

2020 ◽  
Vol 10 (1) ◽  
pp. 44-60
Author(s):  
Mohamed E.I. Badawy ◽  
Entsar I. Rabea ◽  
Samir A.M. Abdelgaleil
Keyword(s):  
Biological Activity ◽  
Plant Pathogens ◽  
Molecular Descriptors ◽  
Microbial Pathogens ◽  
Quantitative Structure ◽  
Qsar Study ◽  
Qsar Analysis ◽  
Pharmacophore Modelling ◽  
Structure Activity ◽  
Wide Range

Background:Monoterpenes are the main constituents of the essential oils obtained from plants. These natural products offered wide spectra of biological activity and extensively tested against microbial pathogens and other agricultural pests.Methods:Antifungal activity of 10 monoterpenes, including two hydrocarbons (camphene and (S)- limonene) and eight oxygenated hydrocarbons ((R)-camphor, (R)-carvone, (S)-fenchone, geraniol, (R)-linalool, (+)-menthol, menthone, and thymol), was determined against fungi of Alternaria alternata, Botrytis cinerea, Botryodiplodia theobromae, Fusarium graminearum, Phoma exigua, Phytophthora infestans, and Sclerotinia sclerotiorum by the mycelia radial growth technique. Subsequently, Quantitative Structure-Activity Relationship (QSAR) analysis using different molecular descriptors with multiple regression analysis based on systematic search and LOOCV technique was performed. Moreover, pharmacophore modelling was carried out using LigandScout software to evaluate the common features essential for the activity and the hypothetical geometries adopted by these ligands in their most active forms.Results:The results showed that the antifungal activities were high, but depended on the chemical structure and the type of microorganism. Thymol showed the highest effect against all fungi tested with respective EC50 in the range of 10-86 mg/L. The QSAR study proved that the molecular descriptors HBA, MR, Pz, tPSA, and Vp were correlated positively with the biological activity in all of the best models with a correlation coefficient (r) ≥ 0.98 and cross-validated values (Q2) ≥ 0.77.Conclusion:The results of this work offer the opportunity to choose monoterpenes with preferential antimicrobial activity against a wide range of plant pathogens.

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