A Novel Method for Inferring Chemical Compounds With Prescribed Topological Substructures Based on Integer Programming
A Novel Method for Inferring Chemical Compounds With Prescribed Topological Substructures Based on Integer Programming
复制标题
DOI：
10.1109/tcbb.2021.3112598
复制
发表时间：
2022-11-01
影响因子：
4.5
通讯作者：
Akutsu，Tatsuya
中科院分区：
文献类型：
--
作者：
Zhu，Jianshen;Azam，Naveed Ahmed;Akutsu，Tatsuya
药物发现是计算生物学和生物信息学的主要目标之一。最近提出了一种利用人工神经网络（ann）和混合整数线性规划（MILP）设计化学图的新框架。该方法包括预测阶段和逆预测阶段。在第一阶段，使用现有化合物的数据训练人工神经网络。在第二阶段，给定目标化学性质，通过求解训练后的神经网络形成的MILP来推断特征向量，然后通过图枚举算法枚举一组化学结构。尽管该框架保证了精确解，但化学图的类型仅限于树图、单环图和具有特定聚合物拓扑且循环指数最高为2的图。为了克服拓扑结构的限制，我们提出了一种新的灵活的框架建模方法，以便我们可以指定图的拓扑子结构、化学元素的部分分配和键的多重性到目标图。计算实验结果表明，所提出的系统可以推断出大约50个非氢原子的化学图。
Drug discovery is one of the major goals of computational biology and bioinformatics. A novel framework has recently been proposed for the design of chemical graphs using both artificial neural networks (ANNs) and mixed integer linear programming (MILP). This method consists of a prediction phase and an inverse prediction phase. In the first phase, an ANN is trained using data on existing chemical compounds. In the second phase, given a target chemical property, a feature vector is inferred by solving an MILP formulated from the trained ANN and then a set of chemical structures is enumerated by a graph enumeration algorithm. Although exact solutions are guaranteed by this framework, the types of chemical graphs have been restricted to such classes as trees, monocyclic graphs, and graphs with a specified polymer topology with cycle index up to 2. To overcome the limitation on the topological structure, 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. The results of computational experiments suggest that the proposed system can infer chemical graphs with around up to 50 non-hydrogen atoms.