Prediction of the Inhibition of Influenza Virus Neuraminidase Various Strains by Means of a Generalized Model Constructed Using the Data on the Position of Known Ligands

Several variants of models for predicting the IC(50) values of inhibitors of influenza virus neuraminidase are presented for both individual strains and also for combinations of data for neuraminidases of several strains. They are based on the use of calculated energy contributions to the amount of change in the free energy of enzyme-inhibitor complexes. In contrast to previous works, aimed at the complex modeling, we added a procedure of comparison of the docking variants with one of the neuraminidase inhibitors, for which the structure of the complexes was determined experimentally. Selection of reference molecules for the comparison of structure similarity was made using the Tanimoto metrics and the limit of the RMSD value for a similar part of the structure was no more than 2 Å. Using this limitation and filtering datasets for a particular strain by the Q(2) value obtained in the leave-one-out control procedure it was possible to construct equations for predicting the IC(50) value with a Q(2) value close to the minimum confidence threshold (0.57 in this work). Taking into consideration that in this version of the prediction models, a minimum set of energy contributions is used, which does not employ expensive calculations of entropy contributions, the result obtained supports the correctness of using a generalized model based on the data on the position of known ligands to predict the inhibition of neuraminidase of the influenza virus of various strains.