Title |
Predictive models for anti-tubercular molecules using machine learning on high-throughput biological screening datasets
|
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Published in |
BMC Research Notes, November 2011
|
DOI | 10.1186/1756-0500-4-504 |
Pubmed ID | |
Authors |
Vinita Periwal, Jinuraj K Rajappan, Open Source Drug Discovery Consortium, Abdul UC Jaleel, Vinod Scaria |
Abstract |
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. |
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Geographical breakdown
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India | 3 | 3% |
Canada | 2 | 2% |
Germany | 1 | 1% |
Hungary | 1 | 1% |
Unknown | 84 | 92% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 25 | 27% |
Student > Master | 13 | 14% |
Researcher | 12 | 13% |
Student > Bachelor | 10 | 11% |
Student > Postgraduate | 6 | 7% |
Other | 12 | 13% |
Unknown | 13 | 14% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 18 | 20% |
Chemistry | 15 | 16% |
Computer Science | 14 | 15% |
Biochemistry, Genetics and Molecular Biology | 9 | 10% |
Medicine and Dentistry | 7 | 8% |
Other | 13 | 14% |
Unknown | 15 | 16% |