A Method for Molecular Design Based on Linear Regression and Integer Programming
A Method for Molecular Design Based on Linear Regression and Integer Programming
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DOI：
10.1145/3510427.3510431
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发表时间：
2022-01
期刊：
影响因子：
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通讯作者：
Jianshen Zhu;Naveed Ahmed Azam;Kazuya Haraguchi;Liang Zhao;H. Nagamochi;T. Akutsu
中科院分区：
文献类型：
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作者：
Jianshen Zhu;Naveed Ahmed Azam;Kazuya Haraguchi;Liang Zhao;H. Nagamochi;T. Akutsu
最近，一个新的框架已被提出用于设计化合物的分子结构，使用人工神经网络（ANN）和混合整数线性规划（MILP）。在该框架中，我们首先定义了一个化学图的特征向量，并构建了一个映射到化学性质π的预测值η（x）的ANN。在此之后，我们制定了一个MILP，它模拟了从x和从x的η（x）的计算过程。给定化学性质π的目标值y*，我们推断化学图，使得通过求解MILP。本文使用线性回归代替人工神经网络来构造预测函数η。为此，我们推导出一个MILP公式，它模拟了线性回归预测函数的计算过程。计算实验的结果表明，我们的方法可以推断出大约多达50个非氢原子的化学图。
Recently a novel framework has been proposed for designing the molecular structure of chemical compounds using both artificial neural networks (ANNs) and mixed integer linear programming (MILP). In the framework, we first define a feature vector of a chemical graph and construct an ANN that maps to a predicted value η(x) of a chemical property π to . After this, we formulate an MILP that simulates the computation process of from and that of η(x) from x. Given a target value y* of the chemical property π, we infer a chemical graph such that by solving the MILP. In this paper, we use linear regression to construct a prediction function η instead of ANNs. For this, we derive an MILP formulation that simulates the computation process of a prediction function by linear regression. The results of computational experiments suggest our method can infer chemical graphs with around up to 50 non-hydrogen atoms.