Escola de Artes, Ciências e Humanidades, Universidade de São Paulo, 03828-0000 São Paulo, SP, Brazil.
Chem Biol Drug Des. 2010 Jun;75(6):632-40. doi: 10.1111/j.1747-0285.2010.00966.x.
Cannabinoid compounds have widely been employed because of its medicinal and psychotropic properties. These compounds are isolated from Cannabis sativa (or marijuana) and are used in several medical treatments, such as glaucoma, nausea associated to chemotherapy, pain and many other situations. More recently, its use as appetite stimulant has been indicated in patients with cachexia or AIDS. In this work, the influence of several molecular descriptors on the psychoactivity of 50 cannabinoid compounds is analyzed aiming one obtain a model able to predict the psychoactivity of new cannabinoids. For this purpose, initially, the selection of descriptors was carried out using the Fisher's weight, the correlation matrix among the calculated variables and principal component analysis. From these analyses, the following descriptors have been considered more relevant: E(LUMO) (energy of the lowest unoccupied molecular orbital), Log P (logarithm of the partition coefficient), VC4 (volume of the substituent at the C4 position) and LP1 (Lovasz-Pelikan index, a molecular branching index). To follow, two neural network models were used to construct a more adequate model for classifying new cannabinoid compounds. The first model employed was multi-layer perceptrons, with algorithm back-propagation, and the second model used was the Kohonen network. The results obtained from both networks were compared and showed that both techniques presented a high percentage of correctness to discriminate psychoactive and psychoinactive compounds. However, the Kohonen network was superior to multi-layer perceptrons.
大麻素化合物因其药用和致幻特性而被广泛应用。这些化合物是从大麻植物（或大麻）中分离出来的，用于多种医疗治疗，如青光眼、化疗相关的恶心、疼痛和许多其他情况。最近，它作为食欲刺激剂已被用于恶病质或艾滋病患者。在这项工作中，分析了几个分子描述符对 50 种大麻素化合物的致幻活性的影响，目的是获得一个能够预测新大麻素化合物致幻活性的模型。为此，最初使用 Fisher 权重、计算变量之间的相关矩阵和主成分分析对描述符进行了选择。从这些分析中，考虑了以下描述符更相关：E（LUMO）（最低未占据分子轨道的能量）、Log P（分配系数的对数）、VC4（C4 位取代基的体积）和 LP1（Lovász-Pelikan 指数，分子分支指数）。接下来，使用了两种神经网络模型来构建一个更合适的模型来对新的大麻素化合物进行分类。使用的第一个模型是多层感知器，带有反向传播算法，第二个模型是 Kohonen 网络。比较了这两种网络的结果，结果表明这两种技术都具有很高的正确率来区分致幻和非致幻化合物。然而，Kohonen 网络优于多层感知器。
