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