Context-Aware Visual Exploration of Molecular Databases

Abstract

Facilitating the visual exploration of scientific data has received increasing attention in the past decade or so. Es pecially in life science related application areas the amount of available data has grown at a breath taking pace. In this paper we describe an approach that allows for visual inspection of large collections of molecular compounds. In contrast to classical visualizations of such spaces we in corporate a specific focus of analysis, for example the out come of a biological experiment such as high throughout screening results. The presented method uses this experi mental data to select molecular fragments of the underlying molecules that have interesting properties and uses the resulting space to generate a two dimensional map based on a singular value decomposition algorithm and a self organizing map. Experiments on real datasets show that the resulting visual landscape groups molecules of similar chemical properties in densely connected regions.