Simple Mining for Molecular Graphs Based on Path Fragments Labeled by Hetero Atoms

Abstract

We have investigated chemical structure data mining on the basis of graph mining techniques. This paper proposes a quick approach to structure data mining of chemicals using path fragments that both of the terminal atoms are hetero atoms. Those path fragments can be mined from individual molecular graphs by using their topological distance matrices. The path fragment is described with two labeled terminal atoms and the number of bonds in the shortest path. For the explicit representation, it is expressed by a short text string that can be interpreted intuitively and easily. Then they are subject to statistical analysis. To test the usage of our approach, the dopamine agonists of 370 chemicals which interact with different type of receptors (D1, D2 and autoreceptor) were used for a computational experiment. The result suggests, for example, that the fragments of N6O and N7O are frequent features for the D1 agonists, N5O for the D2 agonists, and N3O and N4O for autoreceptor agonists. It is easy for us to understand the features on their chemical structures. We believe that it could be useful for the knowledge discovery in structure-activity studies of chemicals.