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|dc.title||Identification of small molecule aggregators from large compound libraries by support vector machines|
|dc.identifier.citation||Rao, H., Li, Z., Li, X., Ma, X., Ung, C., Li, H., Liu, X., Chen, Y. (2010-03). Identification of small molecule aggregators from large compound libraries by support vector machines. Journal of Computational Chemistry 31 (4) : 752-763. ScholarBank@NUS Repository. https://doi.org/10.1002/jcc.21347|
|dc.description.abstract||Small molecule aggregators non-specifically inhibit multiple unrelated proteins, rendering them therapeutically useless. They frequently appear as false hits and thus need to be eliminated in high-throughput screening campaigns. Computational methods have been explored for identifying aggregators, which have not been tested in screening large compound libraries. We used 1319 aggregators and 128,325 non-aggregators to develop a support vector machines (SVM) aggregator identification model, which was tested by four methods. The first is five fold cross-validation, which showed comparable aggregator and significantly improved non-aggregator identification rates against earlier studies. The second is the independent test of .17 aggregators discovered independently from the training aggregators, 71% of which were correctly identified. The third is retrospective screening of 13M PUBCHEM and 168K MDDR. compounds, which predicted 97.9% and 98.7% of the PUBCHEM and MDDR compounds as non-aggregators. The fourth is retrospective screening of 5527 MDDR compounds similar to the known aggregators, 1,14% of which were predicted as aggregators. SVM showed slightly better overall performance against two other machine learning methods based on five fold cross-validation studies of the same settings. Molecular features of aggregation, extracted by a feature selection method, are consistent with published profiles. SVM showed substantial capability in identifying aggregators from large libraries at low false-hit rates. © 2009 Wiley Periodicals, Inc.|
|dc.subject||High throughput screening|
|dc.subject||Machine learning method|
|dc.subject||Recursive feature elimination|
|dc.subject||Support vector machine|
|dc.description.sourcetitle||Journal of Computational Chemistry|
|Appears in Collections:||Staff Publications|
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