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首页> 外文期刊>Journal of Medicinal Chemistry >Artificial neural network applied to prediction of fluorquinolone antibacterial activity by topological methods.
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Artificial neural network applied to prediction of fluorquinolone antibacterial activity by topological methods.

机译：人工神经网络用于通过拓扑方法预测氟喹诺酮的抗菌活性。

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摘要

A new topological method that makes it possible to predict the properties of molecules on the basis of their chemical structures is applied in the present study to quinolone antimicrobial agents. This method uses neural networks in which training algorithms are used as well as different concepts and methods of artificial intelligence with a suitable set of topological descriptors. This makes it possible to determine the minimal inhibitory concentration (MIC) of quinolones. Analysis of the results shows that the experimental and calculated values are highly similar. It is possible to obtain a QSAR interpretation of the information contained in the network after the training has been carried out.
机译：在这项研究中，一种新的拓扑方法可以根据其化学结构预测分子的性质，该方法用于喹诺酮类抗菌剂。该方法使用了其中使用训练算法的神经网络，以及具有合适的拓扑描述符集的人工智能的不同概念和方法。这使得可以确定喹诺酮的最小抑制浓度（MIC）。结果分析表明，实验值和计算值非常相似。进行培训后，可以获得对网络中包含的信息的QSAR解释。

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