Drug discovery is rapidly becoming an information-driven science. Harnessing the power of large volumes of data for the rapid optimization of drug-like compounds requires new chemoinformatics approaches that work effectively on a massive scale. This talk will highlight key algorithmic advances that expand, by several orders of magnitude, the number of compounds that can be assessed as potential drugs, and offer a preview of an informatics platform that has been developed at J&J PRD for the effective delivery and visualization of structure-activity relationships. Particular emphasis will be placed on a novel self-organizing algorithm for extracting the intrinsic structure and dimensionality of large experimental observation spaces, and its application on some challenging problems in computational chemistry and biology.
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