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A novel framework has recently been proposed for designing the molecularstructure of chemical compounds with a desired chemical property using bothartificial neural networks and mixed integer linear programming. In theframework, a chemical graph with a target chemical value is inferred as afeasible solution of a mixed integer linear program that represents aprediction function and other requirements on the structure of graphs. In thispaper, we propose a procedure for generating other feasible solutions of themixed integer linear program by searching the neighbor of output chemical graphin a search space. The procedure is combined in the framework as a new buildingblock. The results of our computational experiments suggest that the proposedmethod can generate an additional number of new chemical graphs with up to 50non-hydrogen atoms.

Molecular Design Based on Artificial Neural Networks, Integer Programming and Grid Neighbor Search