  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.
