STM Journals Peer-reviewed research from Consortium e-Learning Network

Research & Reviews: A Journal of Drug Design & Discovery

2D-QSAR (Quantitative Structure Activity Relationship) Study of Probable Inhibitors for Oral and Cervical Cancers, Generation of Model for Pharmacophore Prediction

  1. Krishna Misra
  2. Ajay Kumar Singh

Abstract

Twenty-four analogs, conjugates and congeners of curcumin reported as inhibitors against HPV 16 E6 and E7 protein targets have been selected for generating QSAR model. The 46 descriptors were calculated using Schrodinger program version quikprop 9.2. Two alternative methods have been used to generate QSAR models. The first model generation was built on 5 descriptors namely glob, QPlogS, CIQPlogS, IP (eV), human oral absorption which were subjected to multiple linear regression analysis generating a linear model having correlation coefficient (r2) as 0.5368. A second method was based on Waikato Environment for Knowledge Analysis Program. The multiple linear regression equation (r2) was raised by approximately 0.8% or 80%. This study is likely to lead to discovery of more potential drugs for therapy of oral and cervical cancers. Keywords: QSAR; Curcumin, oral cancer, cervical cancer, weka, descriptor Cite this ArticleA.K. Singh, K. Misra. 2D - QSAR (Quantitative Structure Activity Relationship) Study of Probable Inhibitors for Oral and Cervical Cancers, Generation of Model for Pharmacophore Prediction. Research & Reviews: A Journal of Drug Design & Discovery. 2018; 5(1): 1–13p
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