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Title |
QSAR based predictive modeling for anti-malarial molecules
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Authors |
Deepak R. Bharti & Andrew M. Lynn*
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Affiliation |
School of Computational and Integrative Sciences, Jawaharlal Nehru University, New Delhi-67;
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Article Type |
Hypothesis
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Date |
Received March 17, 2017; Accepted April 21, 2017, Published May 31, 2017
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Abstract |
Malaria is a predominant infectious disease, with a global footprint, but especially severe in developing countries in the African subcontinent. In recent years, drug-resistant malaria has become an alarming factor, and hence the requirement of new and improved drugs is more crucial than ever before. One of the promising locations for antimalarial drug target is the apicoplast, as this organelle does not occur in humans. The apicoplast is associated with many unique and essential pathways in many Apicomplexan pathogens, including Plasmodium. The use of machine learning methods is now commonly available through open source programs. In the present work, we describe a standard protocol to develop molecular descriptor based predictive models (QSAR models), which can be further utilized for the screening of large chemical libraries. This protocol is used to build models using training data sourced from apicoplast specific bioassays. Multiple model building methods are used including Generalized Linear Models (GLM), Random Forest (RF), C5.0 implementation of a decision tree, Support Vector Machines (SVM), K-Nearest Neighbour and Naive Bayes. Methods to evaluate the accuracy of the model building method are included in the protocol. For the given dataset, the C5.0, SVM and RF perform better than other methods, with comparable accuracy over the test data.
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Keywords |
Malaria, apicoplast, predictive model building, R statistical package
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Citation |
Bharti & Lynn, Bioinformation 13(5): 154-159 (2017)
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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.
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