Molecular Design Based on Artificial Neural Networks, Integer Programming and Grid Neighbor Search
This work addresses molecular design for chemists and materials scientists, but it is incremental as it adds a new building block to an existing framework.
The paper tackles the problem of designing molecular structures with desired chemical properties by proposing a procedure that generates additional feasible solutions through neighbor search in a mixed integer linear programming framework, resulting in the ability to generate new chemical graphs with up to 50 non-hydrogen atoms.
A novel framework has recently been proposed for designing the molecular structure of chemical compounds with a desired chemical property using both artificial neural networks and mixed integer linear programming. In the framework, a chemical graph with a target chemical value is inferred as a feasible solution of a mixed integer linear program that represents a prediction function and other requirements on the structure of graphs. In this paper, we propose a procedure for generating other feasible solutions of the mixed integer linear program by searching the neighbor of output chemical graph in a search space. The procedure is combined in the framework as a new building block. The results of our computational experiments suggest that the proposed method can generate an additional number of new chemical graphs with up to 50 non-hydrogen atoms.