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Title |
Distinguishing compounds with anticancer activity by ANN using inductive QSAR descriptors
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Authors |
Kunal Jaiswal1 and Pradeep Kumar Naik1, *
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Affiliation |
1Department of Biotechnology and Bioinformatics, Jaypee University of Information Technology, Waknaghat, Distt.-Solan, Himachal Pradesh, India-173215
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pradeep.naik@juit.ac.in; * Corresponding author
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91-1792-239227
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Article Type |
Hypothesis
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Date |
received May 19, 2008; revised June 17, 2008; accepted July 06, 2008; published July 30, 2008
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Abstract |
This article describes a method developed for predicting anticancer/non-anticancer drugs using artificial neural network (ANN). The ANN used in this study is a feed-forward neural network with a standard back-propagation training algorithm. Using 30 ‘inductive’ QSAR descriptors alone, we have been able to achieve 84.28% accuracy for correct separation of compounds with- and without anticancer activity. For the complete set of 30 inductive QSAR descriptors, ANN based method reveals a superior model (accuracy = 84.28%, Qpred = 74.28%, sensitivity = 0.9285, specificity = 0.7857, Matthews correlation coefficient (MCC) = 0.6998). The method was trained and tested on a non redundant data set of 380 drugs (122 anticancer and 258 non-anticancer). The elaborated QSAR model based on the Artificial Neural Networks approach has been extensively validated and has confidently assigned anticancer character to a number of trial anticancer drugs from the literature.
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Keywords |
artificial neural network; inductive QSAR descriptors; anticancer drugs; non-anticancer drugs
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Citation |
Jaiswal and Naik, Bioinformation 2(10): 441-451 (2008)
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Edited by |
P. Kangueane
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ISSN |
0973-2063
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Publisher |
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License |
This is an Open Access article which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. This is distributed under the terms of the Creative Commons Attribution License. |