Visualization of large-scale aqueous solubility data using a novel hierarchical data visualization technique

It is a difficult task to recognize the trends in molecular physical properties relevant to a specific chemical class and find a way to optimize potential compounds. We present here a novel hierarchical data visualization technique, named "HeiankyoView", to visualize large-scale multidimensional chemical information. HeiankyoView represents hierarchically organized data objects by mapping leaf nodes as colored square icons and nonleaf nodes as rectangular borders. In this way, data objects can be expressed as equishaped icons without overlapping one another in the two-dimensional display space. HeiankyoView has been applied to visualize aqueous solubility data for 908 compounds collected from the published literature. When the results of a recursive partitioning analysis and hierarchical clustering analysis were visualized, the trends hidden in the solubility data could be effectively displayed as intuitively understandable visual images. Most interestingly, the data visualization technique, without any statistical computations, was able to assist us in extracting from such large-scale data meaningful information establishing that ClogP and the molecular weight are critical factors in determining aqueous solubility. Thus, HeiankyoView is a powerful tool to help us understand structure- activity relationships intuitively from a large-scale data set.