Does tautomerism influence the outcome of QSAR modeling?

Masand, Vijay H.; Mahajan, Devidas T.; Hadda, Taïbi Ben; Jawarkar, Rahul D.; Alafeefy, Ahmed M.; Rastija, Vesna; Ali, Mohamed Ashraf

doi:10.1007/s00044-013-0776-0

Cited by 29 publications

(17 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…When studying the chemical properties of a compound, the probability distribution of atoms scattered in a spherical volume with radius of 3.0 Å is regarded as an important factor. 49,50 The LP1 feature, which belongs to the topological descriptors, is one of the 2D matrix-based descriptors, and is calculated by eigenvalues of a square (usually symmetric) matrix representing a molecular graph. 51 Du and colleagues have reported that small and large values of LP1 are indicative of compounds with less and more branches, respectively.…”

Section: Resultsmentioning

confidence: 99%

A QSAR Study on the 4-Substituted Coumarins as Potent Tubulin Polymerization Inhibitors

Dinparast

Dastmalchi

2020

Adv Pharm Bull

View full text Add to dashboard Cite

Purpose: Despite the discovery and synthesis of several anticancer drugs, cancer is still a major life threatening incident for human beings after cardiovascular diseases. Toxicity, severe side effects, and drug resistance are serious problems of available commercial anticancer drugs. Coumarins are synthetic and natural heterocycles that show promising antiproliferative activities against various tumors. The aim of this research is to computationally study the coumarin derivatives in order to develop reliable quantitative structure-activity relationship (QSAR) models for predicting their anticancer activities. Methods: A data set of thirty one coumarin analogs with significant antiproliferative activities toward HepG2 cells were selected from the literature. The molecular descriptors for these compounds were calculated using Dragon, HyperChem, and ACD/Labs programs. Genetic algorithm (GA) accompanied by multiple linear regression (MLR) for simultaneous feature selection and model development was employed for generating the QSAR models. Results: Based on the obtained results, the developed linear QSAR models with three and four descriptors showed good predictive power with r2 values of 0.670 and 0.692, respectively. Moreover, the calculated validation parameters for the models confirmed the reliability of the QSAR models. Conclusion: The findings of the current study could be useful for the design and synthesis of novel anticancer drugs based on coumarin structure.

show abstract

Section: Resultsmentioning

confidence: 99%

A QSAR Study on the 4-Substituted Coumarins as Potent Tubulin Polymerization Inhibitors

Dinparast

Dastmalchi

2020

Adv Pharm Bull

View full text Add to dashboard Cite

show abstract

“…For 3D QSARs, the cross-correlation limit was set at 0.5, the population at 100, the number of generations at 500 and the speed at 999. Genetic algorithm models [31][32][33][34][35][36] were used to select the optimum number and a set of descriptors.…”

Section: K Nearest Neighbor Methods For Building Qsar Modelsmentioning

confidence: 99%

Prediction of potent therapeutic anticonvulsant ligands to N-methyl-d-aspartate receptors among substituted 4,6-dichloroindole-2-carboxylic acids: Molecular modeling approach

Sharma

2014

Journal of Taibah University for Science

View full text Add to dashboard Cite

We performed two-and three-dimensional quantitative structure-activity relation (QSAR) pharmacophore studies with a series of substituted 4,6-dichloroindole-2-carboxylic acid analogs in order to elucidate the structural properties required for high affinity for the glycine binding site of the N-methyl-d-aspartate receptor. The partial least-squares method coupled with various feature selection methods (stepwise, genetic algorithm and simulated annealing) were used to derive QSAR models, which were validated for statistical significance and predictive ability by internal and external validation. The best two-dimensional QSAR model was selected, which had a correlation coefficient, r 2 = 0.8577, a cross-validated squared correlation coefficient, q 2 = 0.7118 and an external predictive ability pred r 2 = 0.7642. Molecular field analysis was used to construct the best three-dimensional QSAR model by the genetic algorithm-partial least-squares method, which showed good correlational and predictive capability: q 2 = 0.7538, q 2 se = 0.4833 and pred r 2 = 0.7019. In a series of 4,6-dichloroindole-2-carboxylic acid derivatives, the pharmacophore model based on the chemical feature with the lowest root mean square deviation (2.187 nm) consisted of one aromatic feature, one hydrogen bond donor, one hydrogen bond acceptor, one aliphatic and one positive coefficient feature. A representative set of 4,6-dichloroindole-2-carboxylic acid compounds with effective biological activity and a good glycine binding site on the N-methyl-d-aspartate receptor were identified, which may be potential leads for drugs with anticonvulsant activity.

show abstract

“…All the models having poor internal and external predictivity were excluded. The selected descriptors were used to build the statistically acceptable QSAR models followed by exhaustive statistical validation according to the OECD principles for QSAR model validation –…”

Section: Qsar Model Building and Validationmentioning

confidence: 99%

“…Surprisingly, till this date, for aldose reductase inhibitory activity of quinoxalinones only one attempt has been made to develop predictive and robust CoMFA like model (ligand based drug design) using a small dataset of thirty‐five quinoxalinones only . According to one school of thought, QSAR and CoMFA like modeling are preferable techniques for lead optimization as these techniques not only provide in‐depth idea about pharmacophoric features, but, are efficient to predict the activity of a yet to be synthesized drug candidate. Therefore, the main emphasis of the present work was to derive thriving QSAR and CoMFA like models to predict the activity and to determine the structural features governing the aldose reductase inhibitory activity profile of novel quinoxalinones.…”

Section: Introductionmentioning

confidence: 99%

Quinoxalinones Based Aldose Reductase Inhibitors: 2D and 3D‐QSAR Analysis

Masand¹,

El-Sayed

Thakur

et al. 2019

Molecular Informatics

Self Cite

View full text Add to dashboard Cite

In the present work, 2D-and 3D-quantitative structure-activity relationship (QSAR) analysis has been employed for a diverse set of eighty-nine quinoxalinones to identify the pharmacophoric features with significant correlation with the aldose reductase inhibitory activity. Using genetic algorithm (GA) as a variable selection method, multivariate linear regression (MLR) models were derived using a pool of molecular descriptors. All the sixdescriptor based GA-MLR QSAR models are statistically robust with coefficient of determination (R 2 ) > 0.80 and cross-validated R 2 > 0.77. The derived GA-MLR models were thoroughly validated using internal and external and Yscrambling techniques. The CoMFA like model, which is based on a combination of steric and electrostatic effects and graphically inferred using contour plots, is highly robust with R 2 > 0.93 and cross-validated R 2 > 0.73. The established QSAR and CoMFA like models are proficient in identify key pharmacophoric features that govern the aldose reductase inhibitory activity of quinoxalinones.Keywords: Aldose Reductase Activity · Quinoxalinones · QSAR · CoMFA like model [a] V.

show abstract

Does tautomerism influence the outcome of QSAR modeling?

Cited by 29 publications

References 42 publications

A QSAR Study on the 4-Substituted Coumarins as Potent Tubulin Polymerization Inhibitors

A QSAR Study on the 4-Substituted Coumarins as Potent Tubulin Polymerization Inhibitors

Prediction of potent therapeutic anticonvulsant ligands to N-methyl-d-aspartate receptors among substituted 4,6-dichloroindole-2-carboxylic acids: Molecular modeling approach

Quinoxalinones Based Aldose Reductase Inhibitors: 2D and 3D‐QSAR Analysis

Contact Info

Product

Resources

About