TOPS-MODE based QSARs derived from heterogeneous series of compounds. Applications to the design of new anti-inflammatory compounds

Pérez González, Maykel, Dias, Luiz Carlos, Helguera, Aliuska Morales, Rodrı́guez, Yanisleidy Morales, Gonzaga De Oliveira, Luciana, Gomez, Luis Torres and Diaz, Humberto Gonzalez 2004. TOPS-MODE based QSARs derived from heterogeneous series of compounds. Applications to the design of new anti-inflammatory compounds. Bioorganic and Medicinal Chemistry 12 (16) , pp. 4467-4475. 10.1016/j.bmc.2004.05.035

Full text not available from this repository.

Abstract

A new application of TOPological Sub-structural MOlecular DEsign (TOPS-MODE) was carried out in anti-inflammatory compounds using computer-aided molecular design. Two series of compounds, one containing anti-inflammatory and the other containing nonanti-inflammatory compounds were processed by a k-means cluster analysis in order to design the training and prediction sets. A linear classification function to discriminate the anti-inflammatory from the inactive compounds was developed. The model correctly and clearly classified 88% of active and 91% of inactive compounds in the training set. More specifically, the model showed a good global classification of 90%, that is, (399 cases out of 441). While in the prediction set, they showed an overall predictability of 88% and 84% for active and inactive compounds, being the global percentage of good classification of 85%. Furthermore this paper describes a fragment analysis in order to determine the contribution of several fragments towards anti-inflammatory property, also the present of halogens in the selected fragments were analyzed. It seems that the present TOPS-MODE based QSAR is the first alternate general `in silico' technique to experimentation in anti-inflammatory discovery. The TOPological Sub-Structural Molecular Design (TOPS-MODE) approach has been applied to the study of the anti-inflammatory compounds. The model correctly and clearly classified 88% of active and 91% of inactive compounds in the training set. More specifically, the model showed a good global classification of 90%, that is, (399 cases out of 441).

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Chemistry
Publisher:	Elsevier
ISSN:	0968-0896
Date of Acceptance:	26 May 2004
Last Modified:	14 Nov 2022 16:04
URI:	https://orca.cardiff.ac.uk/id/eprint/153687

Citation Data

Cited 38 times in Scopus. View in Scopus. Powered By Scopus® Data

Actions (repository staff only)

Edit Item