alexa Data Set Analysis For The Calculation Of The QSAR Models Predictive Efficiency Based On Activity Cliffs
ISSN: 2161-0444

Medicinal Chemistry
Open Access

Like us on:
OMICS International organises 3000+ Global Conferenceseries Events every year across USA, Europe & Asia with support from 1000 more scientific Societies and Publishes 700+ Open Access Journals which contains over 50000 eminent personalities, reputed scientists as editorial board members.

Open Access Journals gaining more Readers and Citations

700 Journals and 15,000,000 Readers Each Journal is getting 25,000+ Readers

This Readership is 10 times more when compared to other Subscription Journals (Source: Google Analytics)

Share This Page

Additional Info

Loading
Loading Please wait..
 

5th International Conference on Medicinal Chemistry & Computer Aided Drug Designing and Drug Delivery
December 05-07, 2016 Phoenix, USA

Fatima Adilova and Alisher Ikramov
National University of Uzbekistan, Uzbekistan
Posters & Accepted Abstracts: Med chem
DOI: 10.4172/2161-0444.C1.028
Abstract
The activity cliff concept is of high relevance for medicinal chemistry. Herein, we explore a concept of “data set modelability”, i.e., a priori estimate of the feasibility to obtain externally predictive QSAR models for a data set of bioactive compounds. This concept has emerged from analyzing the effect of so-called “activity cliffs” on the overall performance of QSAR models. Some indexes of “modelability” (SALI, ISAC, and MODI) are known already. We extended the version of MODI to data sets of compounds with real activity values. We chose out of 5231 compounds from CHEMBL database, for which activity regarding CA2 protein (Inhibitory activity against human recombinant carbonic anhydrase II) was calculated. The data set divided into some samples, containing 100 and 50 compounds in each. There are 19 real-valued descriptors for each compound in CHEMBL that we used in the calculations. The predictive efficiency of QSAR models is expressed as the correct classification rate by SVM algorithm, which compared with the results of the other two algorithms: algorithm MODI and Voronin algorithm modified by the authors. Comparative analysis of the results performed using Pearson’s correlation coefficient square. Our study showed an extreme lack of evaluation of predictive efficiency of data set only based on “activity cliffs”. In the development of more accurate methods that allow to evaluate the possibility of building of effective models on the data samples, it is necessary to take into account other properties of the sample, and not only the presence (and number) of “activity cliffs”.
Biography

Email: [email protected]

image PDF   |   image HTML
 
Peer Reviewed Journals
 
Make the best use of Scientific Research and information from our 700 + peer reviewed, Open Access Journals
International Conferences 2017-18
 
Meet Inspiring Speakers and Experts at our 3000+ Global Annual Meetings

Contact Us

 
© 2008-2017 OMICS International - Open Access Publisher. Best viewed in Mozilla Firefox | Google Chrome | Above IE 7.0 version
adwords