Weekes Dana, Fogel Gary B
Natural Selection, Inc., 3333 N. Torrey Pines Ct., Suite 200, La Jolla, CA 92037, USA.
Biosystems. 2003 Nov;72(1-2):149-58. doi: 10.1016/s0303-2647(03)00140-0.
Artificial neural networks (ANNs) can be utilized to generate predictive models of quantitative structure-activity relationships between a set of molecular descriptors and activity. Evolutionary computation provides a means to appropriately search for the set of weights and bias terms associated with artificial neural networks that minimize selected functions of the error between the actual and desired outputs. This method is demonstrated by evolutionary training of artificial neural networks capable of predicting anti-HIV activity for a set of 1-[(2-hydroxyethoxy)methyl]-6-(phenylthio)thymine (HEPT) derivatives. The results of this work further confirm the growing indication that evolutionary computation can outperform backpropagation as a method of artificial neural network training. The results also indicate the degree to which bias in the initial training and testing data can affect performance and the importance of bootstrapping.
人工神经网络（ANNs）可用于生成一组分子描述符与活性之间定量构效关系的预测模型。进化计算提供了一种方法，可适当地搜索与人工神经网络相关的权重和偏差项集，从而使实际输出与期望输出之间的误差的选定函数最小化。通过对能够预测一组1-[(2-羟基乙氧基)甲基]-6-(苯硫基)胸腺嘧啶（HEPT）衍生物的抗HIV活性的人工神经网络进行进化训练，证明了该方法。这项工作的结果进一步证实了越来越多的迹象表明，进化计算作为一种人工神经网络训练方法可以优于反向传播。结果还表明了初始训练和测试数据中的偏差对性能的影响程度以及自助法的重要性。
