Molecular Design Based on Integer Programming and Quadratic Descriptors in a Two-layered Model
Molecular Design Based on Integer Programming and Quadratic Descriptors in a Two-layered Model
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DOI：
10.48550/arxiv.2209.13527
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发表时间：
2022-09
期刊：
影响因子：
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通讯作者：
Jianshen Zhu;Naveed Ahmed Azam;Shengjuan Cao;Ryota Ido;Kazuya Haraguchi;Liang Zhao;H. Nagamochi
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文献类型：
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作者：
Jianshen Zhu;Naveed Ahmed Azam;Shengjuan Cao;Ryota Ido;Kazuya Haraguchi;Liang Zhao;H. Nagamochi
最近提出了一种新的框架来设计具有所需化学性质的化合物的分子结构，其中新药物的设计是生物信息学和化学信息学的一个重要课题。该框架通过求解混合整数线性程序（MILP）来推断所需的化学图，该混合整数线性程序模拟由化学图上的两层模型定义的特征函数和由机器学习方法构建的预测函数的计算过程。特征函数中的一组图论描述符对于导出此类 MILP 的紧凑公式起着关键作用。为了在保持 MILP 紧凑性的框架中提高预测函数的学习性能，本文利用其中两个描述符的乘积作为新的描述符，然后设计一种减少描述符数量的方法。我们的计算实验结果表明，所提出的方法提高了许多化学性质的学习性能，并且可以推断出最多 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.