A Method for Molecular Design Based on Linear Regression and Integer Programming

Jianshen Zhu, Naveed A. Azam, Kazuya Haraguchi, Liang Zhao, Hiroshi Nagamochi, Tatsuya Akutsu
2022 2022 12th International Conference on Bioscience, Biochemistry and Bioinformatics  
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 f (C) of a chemical graph C and construct an ANN that maps x = f (C) to a predicted value η(x) of a chemical property π to C. After this, we formulate an MILP that simulates the computation process of f (C) from C and that of η(x) from x. Given a target
more » ... value y * of the chemical property π , we infer a chemical graph C † such that η(f (C † )) = y * 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. CCS CONCEPTS • Computing methodologies → Machine learning; • Mathematics of computing → Integer programming; • Applied computing → Bioinformatics.
doi:10.1145/3510427.3510431 fatcat:i4rf6be7g5gnrltpt4ahhhmifi