Predictive modeling of anti-malarial molecules inhibiting apicoplast formation
IR@IGIB: CSIR-Institute of Genomics & Integrative Biology, New Delhi
View Archive Info| Field | Value | |
| Title |
Predictive modeling of anti-malarial molecules
inhibiting apicoplast formation
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| Creator |
Jamal, Salma
Pariwal, Vinita Open Source Drug Discovery, Open Source Drug Discovery Scaria, Vinod |
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| Subject |
BI1 Bioinformatics (General)
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| Description |
Abstract
Background: Malaria is a major healthcare problem worldwide resulting in an estimated 0.65 million deaths every
year. It is caused by the members of the parasite genus Plasmodium. The current therapeutic options for malaria are
limited to a few classes of molecules, and are fast shrinking due to the emergence of widespread resistance to
drugs in the pathogen. The recent availability of high-throughput phenotypic screen datasets for antimalarial
activity offers a possibility to create computational models for bioactivity based on chemical descriptors of
molecules with potential to accelerate drug discovery for malaria.
Results: In the present study, we have used high-throughput screen datasets for the discovery of apicoplast
inhibitors of the malarial pathogen as assayed from the delayed death response. We employed machine learning
approach and developed computational predictive models to predict the biological activity of new antimalarial
compounds. The molecules were further evaluated for common substructures using a Maximum Common
Substructure (MCS) based approach.
Conclusions: We created computational models using state-of-the-art machine learning algorithms. The models
were evaluated based on multiple statistical criteria. We found Random Forest based approach provides for better
accuracy as assessed from ROC curve analysis. We further evaluated the active molecules using a substructure
based approach to identify common substructures enriched in the active set. We argue that the computational
models generated could be effectively used to screen large molecular datasets to prioritize them for phenotypic
screens, drastically reducing cost while improving the hit rate.
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| Date |
2013
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| Type |
Article
PeerReviewed |
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| Format |
application/pdf
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| Identifier |
http://openaccess.igib.res.in/146/1/1471%2D2105%2D14%2D55.pdf
Jamal, Salma and Pariwal, Vinita and Open Source Drug Discovery, Open Source Drug Discovery and Scaria, Vinod (2013) Predictive modeling of anti-malarial molecules inhibiting apicoplast formation. Predictive modeling of anti-malarial molecules inhibiting apicoplast formation, 14 (55). pp. 1471-2105. |
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| Relation |
http://openaccess.igib.res.in/146/
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