Cheminformatics Models for Inhibitors of Schistosoma mansoni Thioredoxin Glutathione Reductase
Schistosomiasis is a neglected tropical disease caused by a parasite Schistosoma mansoni and affects over 200 million annually. There is an urgent need to discover novel therapeutic options to control the disease with the recent emergence of drug resistance. The multifunctional protein, thioredoxin...
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| Format: | Article |
| Language: | English |
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Wiley
2014-01-01
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| Series: | The Scientific World Journal |
| Online Access: | http://dx.doi.org/10.1155/2014/957107 |
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| author | Sonam Gaba Salma Jamal Open Source Drug Discovery Consortium Vinod Scaria |
| author_facet | Sonam Gaba Salma Jamal Open Source Drug Discovery Consortium Vinod Scaria |
| author_sort | Sonam Gaba |
| collection | DOAJ |
| description | Schistosomiasis is a neglected tropical disease caused by a parasite Schistosoma mansoni and affects over 200 million annually. There is an urgent need to discover novel therapeutic options to control the disease with the recent emergence of drug resistance. The multifunctional protein, thioredoxin glutathione reductase (TGR), an essential enzyme for the survival of the pathogen in the redox environment has been actively explored as a potential drug target. The recent availability of small-molecule screening datasets against this target provides a unique opportunity to learn molecular properties and apply computational models for discovery of activities in large molecular libraries. Such a prioritisation approach could have the potential to reduce the cost of failures in lead discovery. A supervised learning approach was employed to develop a cost sensitive classification model to evaluate the biological activity of the molecules. Random forest was identified to be the best classifier among all the classifiers with an accuracy of around 80 percent. Independent analysis using a maximally occurring substructure analysis revealed 10 highly enriched scaffolds in the actives dataset and their docking against was also performed. We show that a combined approach of machine learning and other cheminformatics approaches such as substructure comparison and molecular docking is efficient to prioritise molecules from large molecular datasets. |
| format | Article |
| id | doaj-art-64e599cfca6041c0961e5422f6ee6287 |
| institution | Kabale University |
| issn | 2356-6140 1537-744X |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Scientific World Journal |
| spelling | doaj-art-64e599cfca6041c0961e5422f6ee62872025-08-20T03:34:36ZengWileyThe Scientific World Journal2356-61401537-744X2014-01-01201410.1155/2014/957107957107Cheminformatics Models for Inhibitors of Schistosoma mansoni Thioredoxin Glutathione ReductaseSonam Gaba0Salma Jamal1Open Source Drug Discovery Consortium2Vinod Scaria3GN Ramachandran Knowledge Center for Genome Informatics, CSIR Institute of Genomics and Integrative Biology, Mall Road, Delhi 110007, IndiaCSIR Open Source Drug Discovery Unit, Anusandhan Bhawan, 2 Rafi Marg, Delhi 110001, IndiaCSIR Open Source Drug Discovery Unit, Anusandhan Bhawan, 2 Rafi Marg, Delhi 110001, IndiaGN Ramachandran Knowledge Center for Genome Informatics, CSIR Institute of Genomics and Integrative Biology, Mall Road, Delhi 110007, IndiaSchistosomiasis is a neglected tropical disease caused by a parasite Schistosoma mansoni and affects over 200 million annually. There is an urgent need to discover novel therapeutic options to control the disease with the recent emergence of drug resistance. The multifunctional protein, thioredoxin glutathione reductase (TGR), an essential enzyme for the survival of the pathogen in the redox environment has been actively explored as a potential drug target. The recent availability of small-molecule screening datasets against this target provides a unique opportunity to learn molecular properties and apply computational models for discovery of activities in large molecular libraries. Such a prioritisation approach could have the potential to reduce the cost of failures in lead discovery. A supervised learning approach was employed to develop a cost sensitive classification model to evaluate the biological activity of the molecules. Random forest was identified to be the best classifier among all the classifiers with an accuracy of around 80 percent. Independent analysis using a maximally occurring substructure analysis revealed 10 highly enriched scaffolds in the actives dataset and their docking against was also performed. We show that a combined approach of machine learning and other cheminformatics approaches such as substructure comparison and molecular docking is efficient to prioritise molecules from large molecular datasets.http://dx.doi.org/10.1155/2014/957107 |
| spellingShingle | Sonam Gaba Salma Jamal Open Source Drug Discovery Consortium Vinod Scaria Cheminformatics Models for Inhibitors of Schistosoma mansoni Thioredoxin Glutathione Reductase The Scientific World Journal |
| title | Cheminformatics Models for Inhibitors of Schistosoma mansoni Thioredoxin Glutathione Reductase |
| title_full | Cheminformatics Models for Inhibitors of Schistosoma mansoni Thioredoxin Glutathione Reductase |
| title_fullStr | Cheminformatics Models for Inhibitors of Schistosoma mansoni Thioredoxin Glutathione Reductase |
| title_full_unstemmed | Cheminformatics Models for Inhibitors of Schistosoma mansoni Thioredoxin Glutathione Reductase |
| title_short | Cheminformatics Models for Inhibitors of Schistosoma mansoni Thioredoxin Glutathione Reductase |
| title_sort | cheminformatics models for inhibitors of schistosoma mansoni thioredoxin glutathione reductase |
| url | http://dx.doi.org/10.1155/2014/957107 |
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