Title: Binary classification of chalcone derivatives with LDA or KNN based on their antileishmanial activity and molecular descriptors selected using the Successive Projections Algorithm feature-selection technique
Authors: Goodarzi, Mohammad ×
Saeys, Wouter
Ugulino de Araujo, Mario Cesar
Harrop Galvao, Roberto Kawakami
Heyden, Yvan Vander #
Issue Date: 2014
Publisher: Elsevier
Series Title: European Journal of Pharmaceutical Sciences vol:51 pages:189-195
Abstract: Chalcones are naturally occurring aromatic ketones, which consist of an α-, β-unsaturated carbonyl system joining two aryl rings. These compounds are reported to exhibit several pharmacological activities, including antiparasitic, antibacterial, antifungal, anticancer, immunomodulatory, nitric oxide inhibition and anti-inflammatory effects. In the present work, a Quantitative Structure–Activity Relationship (QSAR) study is carried out to classify chalcone derivatives with respect to their antileishmanial activity (active/inactive) on the basis of molecular descriptors. For this purpose, two techniques to select descriptors are employed, the Successive Projections Algorithm (SPA) and the Genetic Algorithm (GA). The selected descriptors are initially employed to build Linear Discriminant Analysis (LDA) models. An additional investigation is then carried out to determine whether the results can be improved by using a non-parametric classification technique (One Nearest Neighbour, 1NN). In a case study involving 100 chalcone derivatives, the 1NN models were found to provide better rates of correct classification than LDA, both in the training and test sets. The best result was achieved by a SPA–1NN model with six molecular descriptors, which provided correct classification rates of 97% and 84% for the training and test sets, respectively.
ISSN: 0928-0987
Publication status: published
KU Leuven publication type: IT
Appears in Collections:Division of Mechatronics, Biostatistics and Sensors (MeBioS)
× corresponding author
# (joint) last author

Files in This Item:
File Description Status SizeFormat
PHASCI2886.pdfPublished article Published 411KbAdobe PDFView/Open Request a copy

These files are only available to some KU Leuven Association staff members


All items in Lirias are protected by copyright, with all rights reserved.

© Web of science