Computational analysis and predictive modeling of small molecule modulators of microRNA
IR@IGIB: CSIR-Institute of Genomics & Integrative Biology, New Delhi
View Archive Info| Field | Value | |
| Title |
Computational analysis and predictive modeling
of small molecule modulators of microRNA
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| Creator |
Jamal, Salma
Periwal, Vinita Open Source Drug Discovery Consortium, Open Source Drug Discovery Consortium Scaria, Vinod |
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| Subject |
BI1 Bioinformatics (General)
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| Description |
Abstract
Background: MicroRNAs (miRNA) are small endogenously transcribed regulatory RNA which modulates gene
expression at a post transcriptional level. These small RNAs have now been shown to be critical regulators in a
number of biological processes in the cell including pathophysiology of diseases like cancers. The increasingly
evident roles of microRNA in disease processes have also motivated attempts to target them therapeutically.
Recently there has been immense interest in understanding small molecule mediated regulation of RNA, including
microRNA.
Results: We have used publicly available datasets of high throughput screens on small molecules with potential to
inhibit microRNA. We employed computational methods based on chemical descriptors and machine learning to
create predictive computational models for biological activity of small molecules. We further used a substructure
based approach to understand common substructures potentially contributing to the activity.
Conclusion: We generated computational models based on Naïve Bayes and Random Forest towards mining small
RNA binding molecules from large molecular datasets. We complement this with substructure based approach to
identify and understand potentially enriched substructures in the active dataset. We use this approach to identify
miRNA binding potential of a set of approved drugs, suggesting a probable novel mechanism of off-target activity
of these drugs. To the best of our knowledge, this is the first and most comprehensive computational analysis
towards understanding RNA binding activities of small molecules and predictive modeling of these activities.
Keywords: microRNA, Machine learning, Maximum common substructure (MCS)
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| Date |
2012
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| Type |
Article
NonPeerReviewed |
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| Format |
application/pdf
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| Identifier |
http://openaccess.igib.res.in/147/1/1758%2D2946%2D4%2D16.pdf
Jamal, Salma and Periwal, Vinita and Open Source Drug Discovery Consortium, Open Source Drug Discovery Consortium and Scaria, Vinod (2012) Computational analysis and predictive modeling of small molecule modulators of microRNA. Computational analysis and predictive modeling of small molecule modulators of microRNA, 4 (1). |
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| Relation |
http://openaccess.igib.res.in/147/
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