Title: "An Open Discussion: Big Data Predictive Bioanalytics for Drug Discovery"
Speaker: Gaurav Chopra; Department of Chemistry, Purdue University

Place: LILLY Hall G126
Date: February 2, 2016; Tuesday
Time: 4:30pm

Dr. Chopra will lead a discussion on modeling and "big data" using as an example, his collaboration on an integration pipeline that generates an interaction network between 'all' drugs (currently 3,733 human ingestible drugs) and 'all' proteins (currently 48,278) as a representative of the protein universe. This work uses hierarchical chem- and bio-informatic fragment-based dynamics protocol (~ 1 billion predicted interactions evaluated, considering multiple binding sites per protein).

Living systems and their biomolecules are well understood by atomic modeling of their structural chemistry, which has led to a profound revolution in the digitalization of biological systems. These digitized systems are being catalogued in online databases, analyzed and modeled computationally primarily by inference of homology with the known experimental counterparts. Such digitalization of biology is likely to have an immediate and dramatic impact in the area of drug discovery and development. Atomic level predictive bioanalytics that integrate heterogeneous data sources, systems structural biology algorithms and bioinformatics approaches to identify multiscale biological relationships of compound proteome interactions foreshadows a new era of faster, safer, better and cheaper drug repurposing and discovery. The use of predictive bioanalytics at the molecular level has several advantages allowing for a mechanism- and hypothesis-free exploration of potential drug interactions and, furthermore, makes possible the discovery of more complex and nuanced drug-target interactions. (click here for complete abstract)

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