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首页> 外文期刊>Medicinal chemistry >The structure-AChE inhibitory activity relationships study in a series of pyridazine analogues.
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The structure-AChE inhibitory activity relationships study in a series of pyridazine analogues.

机译：在一系列哒嗪类似物中研究了结构-AChE抑制活性关系。

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The structure-activity relationships (SAR) are investigated by means of the Electronic-Topological Method (ETM) followed by the Neural Networks application (ETM-NN) for a class of anti-cholinesterase inhibitors (AChE, 53 molecules) being pyridazine derivatives. AChE activities of the series were measured in IC(50) units, and relative to the activity levels, the series was partitioned into classes of active and inactive compounds. Based on pharmacophores and antipharmacophores calculated by the ETM-software as sub-matrices containing important spatial and electronic characteristics, a system for the activity prognostication is developed. Input data for the ETM were taken as the results of conformational and quantum-mechanics calculations. To predict the activity, we used one of the most well known neural networks, namely, the feed-forward neural networks (FFNNs) trained with the back propagation algorithm. The supervised learning was performed using a variant of FFNN known as the Associative Neural Networks (ASNN). The result of the testing revealed that the high ETM's ability of predicting both activity and inactivity of potential AChE inhibitors. Analysis of HOMOs for the compounds containing Ph1 and APh1 has shown that atoms with the highest values of the atomic orbital coefficients are mainly those atoms that enter into the pharmacophores. Thus, the set of pharmacophores and antipharmacophores found as the result of this study forms a basis for a system of the anti-cholinesterase activity prediction.
机译：通过电子拓扑方法（ETM），然后通过神经网络应用程序（ETM-NN）研究一类抗胆碱酯酶抑制剂（AChE，53个分子）为哒嗪衍生物的结构-活性关系（SAR）。该系列的AChE活性以IC（50）单位测量，相对于活性水平，该系列分为活性和非活性化合物两类。基于ETM软件作为包含重要空间和电子特征的子矩阵计算的药效基团和抗药效基团，开发了一种活动预测系统。 ETM的输入数据被用作构象和量子力学计算的结果。为了预测活动，我们使用了最著名的神经网络之一，即使用反向传播算法训练的前馈神经网络（FFNN）。监督学习是使用FFNN的变体（称为关联神经网络（ASNN））进行的。测试结果表明，高ETM能够预测潜在AChE抑制剂的活性和无活性。对含有Ph1和APh1的化合物的HOMO分析表明，原子轨道系数最高的原子主要是进入药效团的那些原子。因此，作为该研究结果发现的一组药效基团和抗药效基团构成了抗胆碱酯酶活性预测系统的基础。

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