Abstract
Given the rapidly growing body of data being generated by automated synthesis and screening technologies, analysis and decision-making processes are becoming over-whelmed. One approach to the analysis, reasoning, and visualization of such large amounts of data is the use of homogeneous structural classes as the basis for analysis. This approach enables the characterization and prioritization of groups of compounds rather than individual compounds. Methods to generate and use such classes will be presented. Benefits resulting from class-based analysis, including noise detection, predictive modeling, and similarity screening will be described.