论文标题
分子设计基于整数编程和两层模型中的二次描述符
Molecular Design Based on Integer Programming and Quadratic Descriptors in a Two-layered Model
论文作者
论文摘要
最近提出了一个新型框架，用于设计具有所需化学特性的化学化合物的分子结构，其中新型药物的设计是生物信息学和化学信息学的重要主题。该框架通过求解混合整数线性程序（MILP）来侵犯所需的化学图，该程序模拟由化学图上的两层模型定义的特征函数的计算过程以及通过机器学习方法构建的预测函数。特征函数中的一组图理论描述符起着推导这种MILP的紧凑配方的关键作用。为了提高预测功能在维持MILP紧凑性的框架中的学习绩效，本文利用了两个描述符的产品作为新的描述符，然后设计了一种减少描述符数量的方法。我们的计算实验的结果表明，所提出的方法改善了许多化学特性的学习性能，并可以推断出具有多达50个非氢原子的化学结构。
A novel framework has recently been proposed for designing the molecular structure of chemical compounds with a desired chemical property, where design of novel drugs is an important topic in bioinformatics and chemo-informatics. The framework infers a desired chemical graph by solving a mixed integer linear program (MILP) that simulates the computation process of a feature function defined by a two-layered model on chemical graphs and a prediction function constructed by a machine learning method. A set of graph theoretical descriptors in the feature function plays a key role to derive a compact formulation of such an MILP. To improve the learning performance of prediction functions in the framework maintaining the compactness of the MILP, this paper utilizes the product of two of those descriptors as a new descriptor and then designs a method of reducing the number of descriptors. The results of our computational experiments suggest that the proposed method improved the learning performance for many chemical properties and can infer a chemical structure with up to 50 non-hydrogen atoms.
