Tang Y, Wang H W, Chen K X, Ji R Y
Shanghai Institute of Materia Medica, Chinese Academy of Sciences.
Zhongguo Yao Li Xue Bao. 1995 Jan;16(1):26-32.
To use neural networks, which simulate the functions of living nervous systems, in QSAR studies;
Using the back-propagation neural networks program devised by us, combining with partial least squares (PLS) method, we studied the relationships of quantum chemical indices and analgesic activities of 25 3-methylfentanyl derivatives;
Through learning process, a good QSAR model was established, and the activities of these compounds were predicted; the correlation between the activities and quantum chemical indices: the net charge of the atom N1, the net charge of the atom O16, the torsional angle of atoms C10-C9-N8-C4, the interatomic distance between atom C7 and the center of phenyl plane C9-14 (PhA), is quite well-matched. Based on these results, an interactive pattern between 3-methylfentanyl derivatives and opioid receptors was suggested;
Not only are the results of neural networks superior to those of PLS method but they also provide accurate predictions of the activity of the compounds and also combine the PLS method with neural networks.
在定量构效关系（QSAR）研究中使用模拟活体神经系统功能的神经网络；
使用我们设计的反向传播神经网络程序，结合偏最小二乘法（PLS），研究了25种3 - 甲基芬太尼衍生物的量子化学指标与镇痛活性之间的关系；
通过学习过程，建立了良好的QSAR模型，并预测了这些化合物的活性；活性与量子化学指标之间的相关性：原子N1的净电荷、原子O16的净电荷、原子C10 - C9 - N8 - C4的扭转角、原子C7与苯平面C9 - 14中心（PhA）之间的原子间距离，匹配良好。基于这些结果，提出了3 - 甲基芬太尼衍生物与阿片受体之间的相互作用模式；
神经网络的结果不仅优于PLS方法，而且能准确预测化合物的活性，还将PLS方法与神经网络相结合。
