Department of Applied Mathematics and Physics, Kyoto University, Kyoto 606-8501, Japan.
Graduate School of Advanced Integrated Studies in Human Survivability (Shishu-Kan), Kyoto University, Kyoto 606-8306, Japan.
Int J Mol Sci. 2021 Mar 11;22(6):2847. doi: 10.3390/ijms22062847.
A novel framework for inverse quantitative structure-activity relationships (inverse QSAR) has recently been proposed and developed using both artificial neural networks and mixed integer linear programming. However, classes of chemical graphs treated by the framework are limited. In order to deal with an arbitrary graph in the framework, we introduce a new model, called a two-layered model, and develop a corresponding method. In this model, each chemical graph is regarded as two parts: the exterior and the interior. The exterior consists of maximal acyclic induced subgraphs with bounded height, the interior is the connected subgraph obtained by ignoring the exterior, and the feature vector consists of the frequency of adjacent atom pairs in the interior and the frequency of chemical acyclic graphs in the exterior. Our method is more flexible than the existing method in the sense that any type of graphs can be inferred. We compared the proposed method with an existing method using several data sets obtained from PubChem database. The new method could infer more general chemical graphs with up to 50 non-hydrogen atoms. The proposed inverse QSAR method can be applied to the inference of more general chemical graphs than before.
最近提出并开发了一种新的用于反定量构效关系（Inverse QSAR）的框架，该框架同时使用人工神经网络和混合整数线性规划。然而，该框架处理的化学图类是有限的。为了在框架中处理任意图形，我们引入了一个新的模型，称为双层模型，并开发了相应的方法。在这个模型中，每个化学图形都被视为两部分：外部和内部。外部由具有有限高度的最大非循环诱导子图组成，内部是忽略外部后的连接子图，特征向量由内部的相邻原子对频率和外部的化学非循环图的频率组成。我们的方法比现有的方法更灵活，因为可以推断任何类型的图形。我们使用从 PubChem 数据库获得的几个数据集比较了所提出的方法和现有的方法。新方法可以推断出多达 50 个非氢原子的更通用的化学图形。与以前相比，所提出的逆 QSAR 方法可以应用于更通用的化学图形推断。
