在17个生物目标中,建立了化合物的抗焦虑活性对其对接能量的依赖性的分类共识集成多目标神经网络模型。训练集包括已经测试过抗焦虑活性的化合物,其结构与所研究的15种含氮杂环化学型相似。选择了与抗焦虑活性相关的17个生物目标,考虑到这些化学型衍生物对它们的可能影响。生成的模型由三个人工神经网络集合组成,用于预测三种水平的抗焦虑活性,每个集成有七个神经网络。对一组神经网络中的神经元进行灵敏的分析,以获得高水平的活动,从而有可能识别出四个生物目标ADRA1B,ADRA2A,AGTR1和NMDA-Glut,这对抗焦虑作用的表现最为显著。对于2,3,4,5-四氢-11H-[1,3]二氮杂[1,2-a]苯并咪唑和[1,2,4]三唑并[3,4-a][2,3]苯并二氮杂衍生物的四个关键生物目标,建立了八个具有高抗焦虑活性的单目标药效团。单目标药效团的叠加建立了两个具有高抗焦虑活性的多目标药效团,反映了相互作用的普遍特征2,3,4,5-四氢-11H-[1,3]二氮杂二[1,2-a]苯并咪唑和[1,2,4]三唑并[3,4-a][2,3]苯二氮卓衍生物与最重要的生物目标ADRA1B,ADRA2A,AGTR1和NMDA-Glut。
A classification
consensus ensemble multitarget neural network model of the dependence of the anxiolytic activity of chemical compounds on the energy of their docking in 17 biotargets was developed. The training set included compounds thathadalready been tested for anxiolytic activity and were structurally similar to the 15 studied nitrogen-containing heterocyclic chemotypes. Seventeen biotargets relevant to anxiolytic activity were selected, taking into account the possible effect on them of the derivatives of these chemotypes. The generated model consistedof three ensembles of artificial neural networks for predicting three levels of anxiolytic activity, with sevenneural networks in each ensemble. A sensitive analysis of neurons in an ensemble of neural networks for a high level of activity made it possible to identify four biotargets ADRA1B, ADRA2A, AGTR1, and NMDA-Glut, which were the most significant for the manifestation of the anxiolytic effect. For these four key biotargets for 2,3,4,5-tetrahydro-11H-[1,3]diazepino[1,2-a]benzimidazole and [1,2,4]triazolo[3,4-a][2,3]benzodiazepine derivatives, eight monotarget pharmacophores of high anxiolytic activity were built. Superposition of monotarget pharmacophores built two multitarget pharmacophores of high anxiolytic activity, reflecting the universal features of interaction 2,3,4,5-tetrahydro-11H-[1,3]diazepino[1,2-a]benzimidazole and [1,2,4]triazolo[3,4-a][2,3]benzodiazepine derivatives with the most significant biotargets ADRA1B, ADRA2A, AGTR1, and NMDA-Glut.