We propose to create a flexible but comprehensive software environment for quantitative chemical biology research. A primary goal of this environment will be to aid chemists in synthetic planning, and biologists in interpreting screening results and selecting molecules, using concepts and tools familiar to these researchers. By designing software with these researchers in mind, we will provide both novel analytical processes and access to critical tools that have previously been accessible only to computational scientists. Simultaneously, by building tight collaborations between chemical biologists, computational scientists, statisticians, and software architects, we will provide higher-end functionality and interoperability with other software and data sources that more sophisticated users will ultimately demand. Our proposed information model and software environment will allow experimental scientists to work within the same conceptual framework as their computational counterparts, bridging critical gaps between theory and experiment, and applying the strengths of each approach to common problems. Easy-to-use software for diversity analysis, synthetic reagent selection, and determination of structural features relevant to measured outcomes, will place important decision-making tools into the hands of the research community, and be especially useful for chemists synthesizing molecules, and biologists selecting molecules, pursuant to the goals of the NIH Road Map Molecular Libraries Initiative (MLI). ? ? We have assembled a multidisciplinary team that is uniquely qualified to provide such software for three reasons. First, we have considerable experience in the field of experimental chemical genetics, including planning, synthesis, and relating chemical structure to biological outcomes. Second, we have experience developing web-enabled software for both chemistry and bioinformatics. Third, we reside within a rich intellectual environment (The Broad Institute of Harvard and MIT) that is a center of excellence in bioinformatics and genomic medicine, and that will provide access during software development to a wealth of experimental design strategies and data analysis methods. By deploying key software tools to experimentalists, we will,develop practical use-cases and interface requirements that will guide further development of software central to present and future NIH Road Map MLI activities. A primary goal of our effort will be to integrate new tools for chemistry and chemical biology with existing analysis tools for genomic medicine, expanding the reach of chemistry tools to enable bioinformatics and clinical researchers to have increasing impact on public health. ? ?
