Interactive data mining for molecular graphs.

Designing new medical drugs for a specific disease requires extensive analysis of many molecules that have an activity for the disease. The main goal of these extensive analyses is to discover substructures (fragments) that account for the activity of these molecules. Once they are discovered, these fragments are used to understand the structure of new drugs and design new medicines for the disease. In this paper, we propose an interactive approach for visual molecule mining to discover fragments of molecules that are responsible for the desired activity with respect to a specific disease. Our approach visualizes molecular data in a form that can be interpreted by a human expert. Using a pipelining structure, it enables experts to contribute to the solution with their expertise at different levels. In order to derive desired fragments, it combines histogram-based filtering and clustering methods in a novel way. This combination enables a flexible determination of frequent fragments that repeat in molecules exactly or with some variations.