Predictive models for anti-tubercular molecules using machine learning on high-throughput biological screening datasets
IR@IGIB: CSIR-Institute of Genomics & Integrative Biology, New Delhi
View Archive Info| Field | Value | |
| Title |
Predictive models for anti-tubercular molecules
using machine learning on high-throughput
biological screening datasets
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| Creator |
Periwal, Vinita
Rajappan, Jinuraj K Open Source Drug Discovery Consortium, Open Source Drug Discovery Consortium Jaleel, Abdul UC Scaria, Vinod |
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| Subject |
G1 Genome informatics (General)
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| Description |
Tuberculosis is a contagious disease caused by Mycobacterium tuberculosis (Mtb), affecting more than two billion people around the globe and is one of the major causes of morbidity and mortality in the developing world. Recent reports suggest that Mtb has been developing resistance to the widely used anti-tubercular drugs resulting in the emergence and spread of multi drug-resistant (MDR) and extensively drug-resistant (XDR) strains throughout the world. In view of this global epidemic, there is an urgent need to facilitate fast and efficient lead identification methodologies. Target based screening of large compound libraries has been widely used as a fast and efficient approach for lead identification, but is restricted by the knowledge about the target structure. Whole organism screens on the other hand are target-agnostic and have been now widely employed as an alternative for lead identification but they are limited by the time and cost involved in running the screens for large compound libraries. This could be possibly be circumvented by using computational approaches to prioritize molecules for screening programmes.
RESULTS:
We utilized physicochemical properties of compounds to train four supervised classifiers (Naïve Bayes, Random Forest, J48 and SMO) on three publicly available bioassay screens of Mtb inhibitors and validated the robustness of the predictive models using various statistical measures.
CONCLUSIONS:
This study is a comprehensive analysis of high-throughput bioassay data for anti-tubercular activity and the application of machine learning approaches to create target-agnostic predictive models for anti-tubercular agents.
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| Date |
2011-11-18
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| Type |
Article
PeerReviewed |
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| Format |
application/pdf
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| Identifier |
http://openaccess.igib.res.in/132/1/1756%2D0500%2D4%2D504.pdf
Periwal, Vinita and Rajappan, Jinuraj K and Open Source Drug Discovery Consortium, Open Source Drug Discovery Consortium and Jaleel, Abdul UC and Scaria, Vinod (2011) Predictive models for anti-tubercular molecules using machine learning on high-throughput biological screening datasets. Predictive models for anti-tubercular molecules using machine learning on high-throughput biological screening datasets, 4 (504). |
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| Relation |
http://openaccess.igib.res.in/132/
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