Ith AMA (green) depicting (a) hydrogen bond just before MD simulations and (b) hydrophobic interactions ahead of MD simulations. (c) Hydrogen bond following MD simulations and (d) hydrophobic interactions following MD simulationsThe Author(s) BMC Bioinformatics 2016, 17(Suppl 19):Web page 248 ofFig. 7 RMSD plot of molecular dynamics simulations of lead compound against NA of (a) H1N1 (b) H3NConclusion The objective with the present work was to obtain insight into structural features of zanamivir derivatives for prediction of anti-influenza activity making use of GQSAR method. This study demonstrates a correlation among structure and inhibitory activity of these molecules. Two models were generated targeting NA of each H1N1 and H3N2 influenza strains. The created model generated numerous descriptors namely R1_SdOE_index, R1_6ChainCount, R1_SssSE-index, R1_SaaaCE_index, R1_SdsCHcount and R1_schiV4 in which two descriptors SssSE-index and SdsCHcount showed unfavorable contribution while rest all showed positive contribution. A optimistic contribution suggests boost in contribution of that descriptor could possibly be beneficial for inhibitory activity when a adverse contribution indicates that these descriptors are detrimental for inhibitory activity. Thus, these contributions offer insights into design and style of novel molecule with enhanced inhibitory activity. We also developed one novel inhibitor (AMA) making use of the combinatorial library approach which displayed substantial binding affinity forNA in both H1N1 and H3N2 pandemic influenza strains. AMA was docked against the active web site of NA and a satisfactory docking score of -8.26 Kcal/ mol and -7.00 Kcal/mol was observed for H1N1and H3N2 respectively. MD simulations of AMA stabilized the ligand bound protein complex which resulted within a steady trajectory for satisfactory time. Complicated structure of ligand and protein was identified to be energetically steady post MD Simulations. As a result this gives evidence that the novel compound could serve as potent anti-influenza drugs with enhanced binding properties and low IC50 values than standard drugs.IL-17A, Human (Biotinylated, 132a.a, HEK293, His-Avi) More fileAdditional file 1: Figure S1. Graph depicting number of hydrogen bonds involving H1N1 and AMA across simulations. Figure S2. Figure comparing the conformation of AMA and Zanamivir in (a) H1N1 and (b) H3N2. Figure S3. Interacting residues of (a) H1N1 and (b) H3N2 with Zanamivir. Table S1: Structures and anti-influenza activity of acylguanidine zanamivir derivatives. Table S2. Table showing correlation in between ICThe Author(s) BMC Bioinformatics 2016, 17(Suppl 19):Web page 249 ofand docking scores of most and least active dataset compounds.NOTCH1 Protein Molecular Weight (DOCX 1691 kb) Acknowledgements AG would prefer to thank University Grants Commission, India for the Faculty Recharge position.PMID:23539298 AG can also be thankful to Jawaharlal Nehru University for usage of all computational facilities. Declarations This short article has been published as a part of BMC Bioinformatics Volume 17 Supplement 19, 2016. 15th International Conference On Bioinformatics (INCOB 2016): bioinformatics. The full contents on the supplement are readily available on-line https://bmcbioinformatics.biomedcentral/articles/ supplements/volume-17-supplement-19. Funding Publication charges for this article have already been funded by Jawaharlal Nehru University. Availability of information and material The datasets supporting the conclusions of this short article are included inside the article and its extra files as More file 1. Authors’ contributions DD, SG, AD and AG created th.