Literature DB >> 25297377

QSAR and 3D-QSAR studies applied to compounds with anticonvulsant activity.

Juan C Garro Martinez1, Esteban G Vega-Hissi, Matías F Andrada, Mario R Estrada.   

Abstract

INTRODUCTION: Quantitative structure-activity relationships (QSAR and 3D-QSAR) have been applied in the last decade to obtain a reliable statistical model for the prediction of the anticonvulsant activities of new chemical entities. However, despite the large amount of information on QSAR, no recent review has published and discussed this data in detail. AREAS COVERED: In this review, the authors provide a detailed discussion of QSAR studies that have been applied to compounds with anticonvulsant activity published between the years 2003 and 2013. They also evaluate the mathematical approaches and the main software used to develop the QSAR and 3D-QSAR model. EXPERT OPINION: QSAR methodologies continue to attract the attention of researchers and provide valuable information for the development of new potentially active compounds including those with anticonvulsant activity. This has been helped in part by improvements in the size and performance of computers; the development of specific software and the development of novel molecular descriptors, which have given rise to new and more predictive QSAR models. The extensive development of descriptors, and the way by which descriptor values are derived, have allowed the evolution of the QSAR methods. This evolution could strengthen the QSAR methods as an important tool in research and development of new and more potent anticonvulsant agents.

Keywords:  anticonvulsant activity; quantitative structure–activity relationship and 3D-quantitative structure–activity relationship; review

Mesh:

Substances:

Year:  2014        PMID: 25297377     DOI: 10.1517/17460441.2015.968123

Source DB:  PubMed          Journal:  Expert Opin Drug Discov        ISSN: 1746-0441            Impact factor:   6.098


  1 in total

1.  QSAR based virtual screening derived identification of a novel hit as a SARS CoV-229E 3CLpro Inhibitor: GA-MLR QSAR modeling supported by molecular Docking, molecular dynamics simulation and MMGBSA calculation approaches.

Authors:  R D Jawarkar; Ravindrakumar L Bakal; Magdi E A Zaki; Sami Al-Hussain; Arabinda Ghosh; Ajaykumar Gandhi; Nobendu Mukerjee; Abdul Samad; Vijay H Masand; Israa Lewaa
Journal:  Arab J Chem       Date:  2021-10-19       Impact factor: 6.212

  1 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.