tetano
Editor, Senior Moderator
Mol Inform. 2016 Apr;35(3-4):116-24. doi: 10.1002/minf.201500107. Epub 2016 Jan 15.
[h=1]Using Support Vector Machine (SVM) for Classification of Selectivity of H1N1 Neuraminidase Inhibitors.[/h] Li Y[SUP]1[/SUP], Kong Y[SUP]1[/SUP], Zhang M[SUP]1[/SUP], Yan A[SUP]2,[/SUP][SUP]3[/SUP], Liu Z[SUP]4[/SUP].
[h=3]Author information[/h]
[h=3]Abstract[/h] Inhibition of the neuraminidase is one of the most promising strategies for preventing influenza virus spreading. 479 neuraminidase inhibitors are collected for dataset 1 and 208 neuraminidase inhibitors for A/P/8/34 are collected for dataset 2. Using support vector machine (SVM), four computational models were built to predict whether a compound is an active or weakly active inhibitor of neuraminidase. Each compound is represented by MASSC fingerprints and ADRIANA.Code descriptors. The predication accuracies for the test sets of all the models are over 78 %. Model 2B, which is the best model, obtains a prediction accuracy and a Matthews Correlation Coefficient (MCC) of 89.71 % and 0.81 on test set, respectively. The molecular polarizability, molecular shape, molecular size and hydrogen bonding are related to the activities of neuraminidase inhibitors. The models can be obtained from the authors.
? 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
[h=4]KEYWORDS:[/h] Classification models; Extended connectivity fingerprints (ECFP_4); MASSC fingerprints; Neuraminidase inhibitors (NAIs); Support vector machine (SVM)
PMID: 27491921 DOI: 10.1002/minf.201500107
[PubMed - in process]
[h=1]Using Support Vector Machine (SVM) for Classification of Selectivity of H1N1 Neuraminidase Inhibitors.[/h] Li Y[SUP]1[/SUP], Kong Y[SUP]1[/SUP], Zhang M[SUP]1[/SUP], Yan A[SUP]2,[/SUP][SUP]3[/SUP], Liu Z[SUP]4[/SUP].
[h=3]Author information[/h]
[h=3]Abstract[/h] Inhibition of the neuraminidase is one of the most promising strategies for preventing influenza virus spreading. 479 neuraminidase inhibitors are collected for dataset 1 and 208 neuraminidase inhibitors for A/P/8/34 are collected for dataset 2. Using support vector machine (SVM), four computational models were built to predict whether a compound is an active or weakly active inhibitor of neuraminidase. Each compound is represented by MASSC fingerprints and ADRIANA.Code descriptors. The predication accuracies for the test sets of all the models are over 78 %. Model 2B, which is the best model, obtains a prediction accuracy and a Matthews Correlation Coefficient (MCC) of 89.71 % and 0.81 on test set, respectively. The molecular polarizability, molecular shape, molecular size and hydrogen bonding are related to the activities of neuraminidase inhibitors. The models can be obtained from the authors.
? 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
[h=4]KEYWORDS:[/h] Classification models; Extended connectivity fingerprints (ECFP_4); MASSC fingerprints; Neuraminidase inhibitors (NAIs); Support vector machine (SVM)
PMID: 27491921 DOI: 10.1002/minf.201500107
[PubMed - in process]