A neural relaxation technique for chemical graph matching

We develop a binary relaxation scheme for graph matching in chemical databases. The technique works by iteratively pruning the list of matching possibilities for individual atoms based upon contextual information. Its key features include delayed decision-making, robustness to noise, and fast and efficient neural implementation. We illustrate the utility of the technique by comparing it with probabilistic relaxation for a small database of 2D structures, and suggest that it may be applicable to matching in large databases of both 2D and 3D chemical graphs