Exploring the chemical space of influenza neuraminidase inhibitors [post]

Nuttapat Anuwongcharoen, Watshara Shoombuatong, Tanawut Tantimongcolwat, Virapong Prachayasittikul, Chanin Nantasenamat
2015 unpublished
The fight against the emergence of mutant influenza strains has led to the screening of an increasing number of compounds for inhibitory activity against influenza neuraminidase. This study explores the chemical space of neuraminidase inhibitors (NAIs), which provides an opportunity to obtain further molecular insights regarding the underlying basis of the bioactivity. In particular, a large set of 347 and 175 NAIs against influenza A and B, respectively, was compiled from the literature.
more » ... e literature. Molecular and quantum chemical descriptors were obtained from low-energy conformational structures geometrically optimized at the B3LYP/6-31G(d) level. The bioactivities of the NAIs were classified as active or inactive according to their half maximum inhibitory concentration (IC50) value, in which IC50 <1 µM and >10 µM were defined as active and inactive compounds against influenza neuraminidase, respectively. Interpretable decision rules were derived from a quantitative structure-activity relationship (QSAR) model established using 13 descriptors via decision tree analysis. Good predictive performance was achieved, as deduced from ten-fold cross-validation, in which an accuracy and MCC of 87.5% and 0.731, respectively, were obtained for influenza A NAIs, while values of 89.78% and 0.786 were obtained for influenza B NAIs. Both univariate and multivariate analyses revealed the importance of the lowest unoccupied molecular orbital, number of hydrogen bond donors and number of hydrogen bond acceptors in the predictive model of NAIs against influenza A, while the number of hydrogen bond donors, number of hydrogen bond acceptors and the energy gap between the highest occupied and lowest unoccupied molecular orbitals were important in the predictive model for influenza B NAIs. Molecular scaffold analysis was performed on both data sets in combination with functional group analysis for discriminating important structural features among active and inactive NAIs. Furthermore, molecular docking was employed to investigate the binding modes and their moiety preferences of active NAIs against both influenza A and B neuraminidase. The results from this study are anticipated to be beneficial for guiding the rational drug design of novel NAIs for treating influenza infections.
doi:10.7287/peerj.preprints.1554v1 fatcat:lprju6a4rzfwbnffj3s6tkcble