Predicting dual-targeting anti-influenza agents using multi-models.

Abstract

Influenza is an acute respiratory infectious disease caused by influenza viruses. Its subtype can be distinguished based on the antigenicity of two surface glycoproteins, hemagglutinin (HA) and neuraminidase (NA). One of the main challenges in anti-influenza drug development is the quick evolution of drug resistance due to virus mutations. One solution to this problem is to develop dual-targeting anti-influenza agents. In this paper, a new rationally designed virtual screening protocol that combines structure-based approaches (molecular docking and molecular dynamic simulations) and ligand-based approaches (support vector machines and 3D shape & electrostatic similarity algorithms) is reported for the virtual screening of dual-targeting agents against HA and NA. The final hits came from the consensus of the ligand- and receptor-based knowledge of HA and NA and were tested using ADMET predictions. Evidence from the binding energy calculations and binding mode analyses suggested that several of the hits are promising as dual-targeting anti-influenza agents. The virtual screening protocol may also lead to the identification of innovative drugs in other fields.