Exploring the chemical space of influenza neuraminidase inhibitors

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 their bioactivity. In particular, a large set of 347 and 175 NAIs against influenza A and B, respectively, was compiled from the 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 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, respectively. Interpretable decision rules were derived from a quantitative structure-activity relationship (QSAR) model established using a set of 13 descriptors via decision tree analysis. Good predictive performance was achieved as deduced from 10-fold cross-validation, in which an accuracy and MCC of 82.46% and 0.649, respectively, were obtained for influenza A NAIs while values of 80.00% and 0.553 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 acceptors, molecular energy 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 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. Moreover, novel NAIs with robust binding fitness towards influenza A and B neuraminidase were generated via combinatorial library enumeration and their binding fitness was on par or better than FDA-approved drugs. The results from this study are anticipated to be beneficial for guiding the rational drug design of novel NAIs for treating influenza infections.