﻿<?xml version="1.0" encoding="UTF-8"?>
<records>
  <record>
    <language>eng</language>
    <publisher>Science and Education Publishing</publisher>
    <journalTitle>American Journal of Pharmacological Sciences</journalTitle>
    <publicationDate>2013-10-22</publicationDate>
    <volume>1</volume>
    <issue>1</issue>
    <startPage>80</startPage>
    <endPage>83</endPage>
    <doi>10.12691/ajps-1-5-2</doi>
    <publisherRecordId>AJPS2013152</publisherRecordId>
    <documentType>article</documentType>
    <title language="eng">QSAR Study of Methionine Aminopeptidase Inhibitors as Anti-cancer Agents Using MLR Approach</title>
    <authors>
      <author>
        <name>Ajeet</name>
        <email>ajeet_pharma111@rediffmail.com</email>
        <affiliationId>1</affiliationId>
      </author>
    </authors>
    <affiliationsList>
      <affiliationName affiliationId="1">Department of Pharmaceutical Chemistry, S. D. College of Pharmacy and Vocational Studies, Muzaffarnagar, India</affiliationName>
    </affiliationsList>
    <abstract language="eng">Here Benzimidazole analogues have been used to correlate the inhibition activity with the Eccentric Connectivity index (ECI), Fragment Complexity (FC) and McGowan Volumes (MG) descriptors for studying the quantitative structure activity relationship (QSAR) against methionine aminopeptidases for the development and evaluation of anti-cancer agents. Correlation may be an adequate predictive model which can help to provide guidance in designing and subsequently yielding greatly specific compounds that may have reduced side effects and improved pharmacological activities. We have used Multiple Linear Regression (MLR) for developing QSAR model. For the validation of the developed QSAR model, statistical analysis such as cross validation test (LOO-CV), quality factor, fischers test, root mean square deviation (RMSD), variance, standard deviation etc.; have been performed and all the tests validated this QSAR model with fraction of variance r<SUP>2</SUP> = 0.8906 and LOO-CV q<SUP>2 </SUP>= 0.8904.</abstract>
    <fullTextUrl format="pdf">http://pubs.sciepub.com/ajps/1/5/2/ajps-1-5-2.pdf</fullTextUrl>
    <keywords language="eng">
      <keyword>QSAR</keyword>
      <keyword>Multiple Linear Regression</keyword>
      <keyword>benzimidazole analogues</keyword>
      <keyword>methionine aminopeptidases (MetAPs)</keyword>
    </keywords>
  </record>
</records>