Analysis of chemical graphs is becoming a major research topic in computational molecular biology due to its potential applications to drug design. One of the major approaches in such a study is inverse quantitative structure activity/property relationships (inverse QSAR/QSPR) analysis, which is to infer chemical structures from given chemical activities/properties. Recently, a novel framework has been proposed for inverse QSAR/QSPR using both artificial neural networks (ANN) and mixed integer linear programming (MILP). This method consists of a prediction phase and an inverse prediction phase. In the first phase, a feature vector $f(G)$ of a chemical graph $G$ is introduced and a prediction function $\psi_{\mathcal{N}}$ on a chemical property $\pi$ is constructed with an ANN $\mathcal{N}$. In the second phase, given a target value $y^*$ of the chemical property $\pi$, a feature vector $x^*$ is inferred by solving an MILP formulated from the trained ANN $\mathcal{N}$ so that $\psi_{\mathcal{N}}(x^*)$ is equal to $y^*$ and then a set of chemical structures $G^*$ such that $f(G^*)= x^*$ is enumerated by a graph enumeration algorithm. The framework has been applied to chemical compounds with a rather abstract topological structure such as acyclic or monocyclic graphs and graphs with a specified polymer topology with cycle index up to 2. In this paper, we propose a new flexible modeling method to the framework so that we can specify a topological substructure of graphs and a partial assignment of chemical elements and bond-multiplicity to a target graph.
翻译:化学图解分析正在成为计算分子生物学中的一个主要研究课题,因为它有可能应用于药物设计。这种研究的主要方法之一是反定量结构活动/财产关系(QSAR/QSPR)分析(QSAR/QSPR)反向定量结构活动/财产关系(QSAR/QSPR)分析,即从特定的化学活动/财产中推断化学结构。最近,为反向 QSAR/QSPR提出了一个新框架,使用人工神经网络(ANN)和混合直线编程序(MILP) 。这种方法包括一个预测阶段和一个反向预测阶段。在第一阶段,引入了一个化学图表解(G$G$)的特性矢量矢量(G),在化学属性上方结构上方($=G_QAR_BAR_BAR_BAR_美元)的预测值值值值。在第二阶段,考虑到一个目标值$@$美元化学特性模型值的值值,一个特性矢量矢量矢量单位(xx) 和一个指数,在经过训练的ANM$的A_N_cal_Ial 结构中, 美元向美元的上方(美元, 美元和美元的上方值的基值的值值值的值值值值的值值值值向一个等值的值的值向一个基值向一个基值的值的值的值的值向一个基值向一个基值向一个基值向一个基值的基值的基值的基值向一个基值向一个基值向一个基值向一个基值的基值的基值的基值的基值的基值的基值的基值的基值的基值的基值的基的基的基的基的基的基的基的基的基的基的基的基的基的基的基的基的基的基的基的基的基的基的值。