Searching for just a few words should be enough to get started. If you need to make more complex queries, use the tips below to guide you.
Article type: Research Article
Authors: Dutt, Rohita | Dureja, Harishb | Madan, A.K.b; *
Affiliations: [a] Guru Gobind Singh College of Pharmacy, Yamunanagar-135001, India | [b] Faculty of Pharmaceutical Sciences, M. D. University, Rohtak-124001, India
Correspondence: [*] Corresponding author. Tel.: +91 98963 46211; E-mail: madan_ak@yahoo.com.
Abstract: In the present study, an in silico approach using decision tree, random forest and moving average analysis has been applied to a data set comprising of 53 analogues of 5-alkyl-2-alkylamino-6-(2,6-difluorophenylalkyl)-3,4-dihydropyrimidin-4(3$H$)-one for development of models for prediction of anti-HIV-1 activity. A total of 46 2D and 3D molecular descriptors of diverse nature, have been used for decision tree and random forest analysis. The value of majority of these descriptors for each analogue in the dataset was computed using E-Dragon software (version 1.0). An in-house computer program was also employed to calculate additional topological descriptors which were not included in E-Dragon software. Random forest correctly classified the analogues into active and inactive with an accuracy of 85%. A decision tree was also employed for determining the importance of molecular descriptors. The decision tree learned the information from the input data with an accuracy of 98% and correctly predicted the cross-validated (10 fold) data with accuracy up to 77%. The best five descriptors identified by decision tree analysis were subsequently used to build suitable models using moving average analysis. The use of models based upon these non-correlating molecular descriptors resulted in the prediction of anti-HIV-1 activity with an overall accuracy of 83–96%. Moreover, active ranges of the proposed models not only revealed high potency but also exhibited improved safety as indicated by relatively high values of selectivity index. The statistical significance of models/ indices was assessed through intercorrelation analysis, sensitivity, specificity and Matthew's correlation coefficient. High predictability offer proposed models a vast potential for providing lead structures for development of potent but safe anti- HIV-1 agents.
Keywords: Molecular descriptors, topochemical descriptors, E-Dragon software, Dihydro-alkoxy-benzyl-oxopyrimidines, anti- HIV-1 agents, random forest, decision tree, moving average analysis
DOI: 10.3233/JCM-2009-0256
Journal: Journal of Computational Methods in Sciences and Engineering, vol. 9, no. 3, pp. 95-112, 2009
IOS Press, Inc.
6751 Tepper Drive
Clifton, VA 20124
USA
Tel: +1 703 830 6300
Fax: +1 703 830 2300
sales@iospress.com
For editorial issues, like the status of your submitted paper or proposals, write to editorial@iospress.nl
IOS Press
Nieuwe Hemweg 6B
1013 BG Amsterdam
The Netherlands
Tel: +31 20 688 3355
Fax: +31 20 687 0091
info@iospress.nl
For editorial issues, permissions, book requests, submissions and proceedings, contact the Amsterdam office info@iospress.nl
Inspirees International (China Office)
Ciyunsi Beili 207(CapitaLand), Bld 1, 7-901
100025, Beijing
China
Free service line: 400 661 8717
Fax: +86 10 8446 7947
china@iospress.cn
For editorial issues, like the status of your submitted paper or proposals, write to editorial@iospress.nl
如果您在出版方面需要帮助或有任何建, 件至: editorial@iospress.nl