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|Title:||Consensus QSAR model for identifying novel H5N1 inhibitors|
In silico screening
|Citation:||Sharma, N., Yap, C.W. (2012-08). Consensus QSAR model for identifying novel H5N1 inhibitors. Molecular Diversity 16 (3) : 513-524. ScholarBank@NUS Repository. https://doi.org/10.1007/s11030-012-9384-z|
|Abstract:||Due to the importance of neuraminidase in the pathogenesis of influenza virus infection, it has been regarded as the most important drug target for the treatment of influenza. Resistance to currently available drugs and new findings related to structure of the protein requires novel neuraminidase 1 (N1) inhibitors. In this study, a consensus QSAR model with defined applicability domain (AD) was developed using published N1 inhibitors. The consensus model was validated using an external validation set. The model achieved high sensitivity, specificity, and overall accuracy along with low false positive rate (FPR) and false discovery rate (FDR). The performance of model on the external validation set and training setwere comparable, thus itwas unlikely to be overfitted. The low FPR and low FDR will increase its accuracy in screening large chemical libraries. Screening of ZINC library resulted in 64,772 compounds as probable N1 inhibitors, while 173, 674 compounds were defined to be outside the AD of the consensus model. The advantage of the current model is that it was developed using a large and diverse dataset and has a defined AD which prevents its use on compounds that it is not capable of predicting. The consensus model developed in this study is made available via the free software, PaDEL-DDPredictor. © Springer Science+Business Media B.V. 2012.|
|Source Title:||Molecular Diversity|
|Appears in Collections:||Staff Publications|
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