ニューラルネットワークを用いた構造活性相関

抄録

We developed a neural network simulator for structure-activity correlation of molecules: Neco. A self-organized network model for high-speed learning was included in Neco, a perceptron type with the three layers. In the hidden layer the neurons is self-organized by using Mahalanobis generalized distance. Using this simulator, hydrophobic parameter, logP, of perillartine derivatives was predicted. We used for inputs the set of six parameters: five STERIMOL (L, Wl, Wu, Wr, and Wd) parameters and the sweet/bitter activity. The 22 sampled data is used for training. Our neural network accurately can predicted hydrophobic parameter, logP. Compared with a normal perceptron network, the learning ability of our network is quite higher and its convergence speed is greatly larger than it.