Ichikawa H, Aoyama T
Hoshi College of Pharmacy, Tokyo, Japan.
SAR QSAR Environ Res. 1993;1(2-3):115-30. doi: 10.1080/10629369308028823.
In addition to its outstanding abilities in both classification and fitting, the neural network can also accurately predict the values of the untrained region. To rationalize this ability of prediction, the authors mathematically discussed the valid region of prediction. Based on such a background, the authors proposed "descriptor mapping" in the QSAR analysis, which visualizes the nonlinear dependencies between structural parameters. A variable of the linear multiple regression analysis in the QSAR study is supposed to be linear to the biological intensity and is independent of other variables. Analysis by the descriptor mapping method discloses the reality.
除了在分类和拟合方面具有出色能力外，神经网络还能准确预测未训练区域的值。为了使这种预测能力合理化，作者从数学角度讨论了预测的有效区域。基于这样的背景，作者在定量构效关系（QSAR）分析中提出了“描述符映射”，它可视化了结构参数之间的非线性依赖性。QSAR研究中的线性多元回归分析变量被认为与生物活性强度呈线性关系，且与其他变量无关。通过描述符映射方法进行的分析揭示了这一实际情况。
