Applications of
Cheminformatics & Chemical Modelling
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Austin, C



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About Christopher Austin (NIH)
Christopher Austin is Director of the NIH Chemical Genomics Center (NCGC), and Senior Advisor to the Director for Translational Research at the National Human Genome Research Institute (NHGRI). The NCGC, part of the NIH Roadmap Molecular Libraries initiative, develops small molecule probes for biological functions and new paradigms for high-throughput screening, chemistry, and cheminformatics. In his role as Senior Advisor for Translational Research, he initiated the Knockout Mouse Project, which is producing knockout mice for all mouse genes, and an in-depth transcriptome map of the mouse. Dr. Austin came to NIH from Merck, where he directed research programs in genomics-based target discovery, pharmacogenomics, and DNA microarray technologies, with a focus on neuropsychiatric diseases. Dr. Austin received his A.B. from Princeton and M.D. from Harvard. He did clinical training in neurology at Massachusetts General Hospital, followed by a fellowship in genetics at Harvard.
Abstract
Generation and Analysis of Large Quantitative High Throughput Screening Datasets

Christopher P. Austin, Chemical Genomics Center, National Institutes of Health, Bethesda, Maryland, USA

The NIH Chemical Genomics Center (NCGC) is an ultrahigh-throughput screening and chemistry center which discovers chemical probes of gene and cell functions across the genome using titration-based quantitative high throughput screening (qHTS), and develops new paradigms for chemical genomics and downstream drug development. The novel chemical probe series produced have activity, potency, and solubility adequate for in vitro study of gene, protein, pathway, and cell functions in health and disease. All of the data produced at the NCGC are made freely available via PubChem. Since qHTS produces concentration-response data on every compound screened and is therefore both more reliable and more informative than traditional single-concentration screening, these data are well suited for computational modeling and testing of virtual algorithms. Concurrent experimental and docking screens have revealed insights into the mechanisms of active compounds and the chemical space tested by these two methodologies. The public availability for the first time of such large high-quality small molecule activity datasets is explicitly intended to enable computational scientists to develop novel cheminformatic analyses, algorithms, and models that will accelerate the process of chemical probe and drug development.

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