A Novel Method for Inferring Chemical Compounds With Prescribed Topological Substructures Based on Integer Programming

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 an...

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Vydáno v:IEEE/ACM transactions on computational biology and bioinformatics Ročník 19; číslo 6; s. 3233 - 3245
Hlavní autoři: Zhu, Jianshen, Azam, Naveed Ahmed, Zhang, Fan, Shurbevski, Aleksandar, Haraguchi, Kazuya, Zhao, Liang, Nagamochi, Hiroshi, Akutsu, Tatsuya
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States IEEE 01.11.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Algorithms
artificial neural network
Artificial neural networks
Bioinformatics
Chemical bonds
Chemical compounds
Chemical elements
chemical graph
Chemicals
Compounds
Computational biology
Computational Biology - methods
Computational modeling
Computer applications
Drug Discovery
Enumeration
enumeration of graphs
Exact solutions
Graphs
Hydrogen atoms
Integer programming
Linear programming
Mixed integer
mixed integer linear programming
molecular design
Neural networks
Neural Networks, Computer
Polymers
Programming, Linear
QSAR/QSPR
Topology
Trees (mathematics)
Vegetation
ISSN:1545-5963, 1557-9964, 1557-9964
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Popis
Shrnutí: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.
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ISSN:1545-5963
1557-9964
1557-9964
DOI:10.1109/TCBB.2021.3112598