Dataset Information

QSAR study and molecular design of open-chain enaminones as anticonvulsant agents.

ABSTRACT: Present work employs the QSAR formalism to predict the ED(50) anticonvulsant activity of ringed-enaminones, in order to apply these relationships for the prediction of unknown open-chain compounds containing the same types of functional groups in their molecular structure. Two different modeling approaches are applied with the purpose of comparing the consistency of our results: (a) the search of molecular descriptors via multivariable linear regressions; and (b) the calculation of flexible descriptors with the CORAL (CORrelation And Logic) program. Among the results found, we propose some potent candidate open-chain enaminones having ED(50) values lower than 10 mg·kg(-1) for corresponding pharmacological studies. These compounds are classified as Class 1 and Class 2 according to the Anticonvulsant Selection Project.

SUBMITTER: Garro Martinez JC

PROVIDER: S-EPMC3257134 | biostudies-literature | 2011

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

QSAR study and molecular design of open-chain enaminones as anticonvulsant agents.

Garro Martinez Juan C JC Duchowicz Pablo R PR Estrada Mario R MR Zamarbide Graciela N GN Castro Eduardo A EA

International journal of molecular sciences 20111214 12

Present work employs the QSAR formalism to predict the ED(50) anticonvulsant activity of ringed-enaminones, in order to apply these relationships for the prediction of unknown open-chain compounds containing the same types of functional groups in their molecular structure. Two different modeling approaches are applied with the purpose of comparing the consistency of our results: (a) the search of molecular descriptors via multivariable linear regressions; and (b) the calculation of flexible desc ...[more]

PMID: 22272137

Similar Datasets

Project description:ObjectivesBreast tumor is ranked as the most common tumor type identified among women globally with over 1.7 million cases annually, representing 11.9% of the total number of cancer cases. Approved anti-breast tumor drugs exhibit several side effects and some patients develop resistance during the early treatment stage. This study aimed to use an in-silico approach to identify and design potential therapeutic agents.MethodsRobust 3D-QSAR models were developed using quinazoline-4(3H)-one analogs as EGFR inhibitors. The best model was then selected based on statistical parameters and was subsequently used to design more potent therapeutic agents. Molecular docking simulation was executed using the data set and the designed compounds to identify lead compounds which were further screened by pharmacokinetic profiling by applying SwissADME and pkCSM software.ResultsInternal validations of the best CoMFA and CoMSIA models (R2 = 0.855 and 0.895; Q2 = 0.570 and 0.599) passed the threshold values for the establishment of a consistent QSAR model. The constructed models were further validated externally using six compounds as a test set, thus revealing a satisfactory predicted correlation coefficient (R2 pred = 0.657 and 0.681). The CoMSIA_SHE models with the best statistical parameters were further subjected to applicability domain checks and only three influentials were detected. These were then utilized to design five novel compounds with activities ranging from 5.62 to 6.03. Molecular docking studies confirmed that compounds 20 to 26, with docking scores ranging from -163.729 to -169.796, represented lead compounds with higher docking scores compared to Gefitinib (-127.495). Furthermore, the designed compounds exhibited better docking scores ranging from -171.379 to -179.138.ConclusionsPharmacological studies identified compounds 20, 24 26 and the designed compounds 2, 3, 5 as feasible drug candidates. However, these theoretical findings should now be validated experimentally.

Dataset Information

QSAR study and molecular design of open-chain enaminones as anticonvulsant agents.

Publications

QSAR study and molecular design of open-chain enaminones as anticonvulsant agents.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets