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Title
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A comparative study on the molecular descriptors for predicting drug-likeness of small molecules
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Authors
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Hrishikesh Mishra, Nitya Singh, Tapobrata Lahiri, Krishna Misra
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Affiliation
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Bioinformatics Division, Indian Institute of Information Technology, Allahabad, India
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Article Type
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Hypothesis
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Date
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received February 22, 2009; accepted April 14, 2009; published June 13, 2009
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Abstract |
Screening of “drug-like” molecule from the molecular database produced through high throughput techniques and their large repositories requires robust classification. In our work, a set of heuristically chosen nine molecular descriptors including four from Lipinski’s rule, were used as classification parameter for screening “drug-like” molecules. The robustness of classification was compared with four fundamental descriptors of Lipinski. Back propagation neural network based classifier was applied on a database of 60000 molecules for classification of, “drug-like” and “non drug-like” molecules. Classification result using nine descriptors showed high classification accuracy of 96.1% in comparison to that using four Lipinski’s descriptors which yielded an accuracy of 82.48%. Also a significant decrease of false positives resulted while using nine descriptors causing a sharp 18% increase of specificity of classification. From this study it appeared that Lipinski’s descriptors which mainly deal with pharmacokinetic properties of molecules form the basis for identification of “drug-like” molecules that can be substantially improved by adding more descriptors representing pharmaco-dynamics properties of molecules.
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Keywords |
machine learning, small molecules, molecular descriptors, drug-likeness, non drug-likeness.
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Citation |
Mishra et al, Bioinformation 3(9): 384-388 (2009)
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Edited by |
P. Kangueane
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ISSN |
0973-2063
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Publisher |
Biomedical Informatics | |
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License
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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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