Abstract—

Several variants of models for predicting the IC₅₀ 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 Q² value obtained in the leave-one-out control procedure it was possible to construct equations for predicting the IC₅₀ value with a Q² 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.

中文翻译：

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通过使用已知配体位置数据构建的广义模型预测流感病毒神经氨酸酶各种菌株的抑制作用

摘要—

预测IC ₅₀的模型的几种变体流感病毒神经氨酸酶抑制剂的值既针对单个菌株，也针对几种菌株的神经氨酸酶的数据组合。它们基于对酶抑制剂复合物自由能变化量的计算出的能量贡献。与针对复杂模型的先前工作相反，我们添加了一种将对接变异体与一种神经氨酸酶抑制剂进行比较的程序，为此，通过实验确定了复合物的结构。使用Tanimoto度量标准选择用于比较结构相似性的参考分子，并且结构相似部分的RMSD值限制不超过2Å。使用此限制并通过Q ²过滤特定应变的数据集在留一法控制程序中获得的最大数值，可以构建方程来预测Q ²值接近最小置信度阈值（本工作中为0.57）的IC ₅₀值。考虑到在此版本的预测模型中，使用了最小的能量贡献集，而没有使用昂贵的熵贡献计算，因此获得的结果支持使用基于位置信息的广义模型的正确性。已知的配体可预测各种菌株对流感病毒的神经氨酸酶的抑制作用。