Cheminformatics is often focused on managing and analyzing large amounts of data and their application to solve chemical problem.   The meaningful extraction and curation of knowledge from existing, often un-integrated, data repositories requires better tools that address data management, analysis and, importantly, characterization and visualization of chemical space. We developed a new framework called SMARTNames to describe and formalize chemical information based on chemical-functional-groups (CFGs).  In contrast to other approaches, our system integrates structure and function using a semantic knowledge representation of chemistry that captures chemists' insights to complex problems; yet it makes that knowledge accessible even to non-experts.  We will present an analysis of several chemistry databases.  To process the large datasets we developed a novel approach to compute and compare very large chemical libraries based on any combination of descriptors and to visualize unique and overlapping chemical spaces.  We contrast this with classical approaches such as BCUTS.

Knowledge-based characterization of chemistry space and visualization

244th National Meeting (2012)

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