David Wild wins $120k Grant from Lilly for Web Service Devlelopment

Informatics Faculty Member Receives $120K Grant from Eli Lilly & Company for Research Project

Eli Lilly and Company has awarded a one-year, $120,000 grant to Professor David Wild of the Indiana University School of Informatics to research ways to data mine the ever-increasing amount of publicly available information about chemical compounds and their biological activities.

David Wild Wild, Assistant Professor and Associate Director of the Chemical Informatics Program for the School, is developing a software tool that will aggregate data from a multitude of online databases and computation tools using a web service infrastructure that was previously developed through funding from the NIH. The tool will be a "one-stop-shop" for understanding the properties and behavior of chemical compounds, in particular existing and potential drug molecules.

"The creation of this software is significant because it will enable a comprehensive picture of a potential drug's behavior to be assessed, not just using static information from databases, but also using active on-the-fly predictions and calculations from state-of-the-art tools," said Professor Wild. "Drug researchers will have a single tool that can give them needed information from the public arena, and we hope it will help to speed up the drug discovery pipeline."

More information at http://djwild.info

Two students awarded Symyx PhD Fellowships in Informatics

Dazhi (David) Jiao and Jae Hong Shin at the IU School of Informatics will be continuing their studies in a doctoral program thanks to a partnership between the School and Symyx Technologies, Inc.

For full story, see http://www.informatics.indiana.edu/headline.asp.

Cheminformatics student Dazhi Jiao is accepted into the Google Summer of Code

From http://code.google.com/soc/2008/genmapp/appinfo.html?csaid=4236C0DBB86F4A10

Title	Integration of Cheminformatics Tools in Cytoscape
Student	Dazhi Jiao
Mentor	John "Scooter" Morris
Abstract
Biological networks that contain small molecules are of special interests in biological and chemical informatics researches. For example, there are some recent research on the relationships of protein networks and ligands networks, based on the facts that proteins with similar binding sites bind similar ligands. This project proposes an addition of cheminformatics tools to Cytoscape to facilitate such research. 1.2D depiction is a basic requirement for chemistry research. I will integrate the 2D depiction functionality of the Chemistry Development Kit (CDK) (http://cdk.sourceforge.net) into Cytoscape, so that users can view the 2D structure of any molecule that has annotation of either SMILES string or other commonly used structure annotations. CDK is an open source Java library for structural cheminformatics and bioinformatics. 2.Linkout to chemical resources that focuses on biological activities of small molecules, sush as PubChem, MACiE, and ChEBI. PubChem (http://pubchem.ncbi.nlm.nih.gov/) is one of the largest chemical databases that provide information on the biological activities of small molecules. MACiE (Machanism, Annotation and Classification in Enzymes) (http://www.ebi.ac.uk/thornton-srv/databases/MACiE/) contains fully annotated enzyme reaction mechanisms, including information on substrates. Chemical Entities of Biological Interest (ChEBI) (http://www.ebi.ac.uk/chebi/) is a dictionary of molecular entities focusing on small molecules. 3.Implementation of a plugin to calculate the Tanimoto similarity between molecules in a network. This will use the implementation of Tanimoto similarity implementation in CDK, which calculates the Tanimoto coefficients based on fingerprints of compounds.(http://www.daylight.com/dayhtml/doc/theory/theory.finger.html) 4.Extend the plugin mentioned in 3 to clustering nodes in a network based on the Tanimoto similarity of molecules that associated with each node. For example, nodes in a protein network can be used to generate a new network based on the Tanimoto similarities between the ligands that bind to the proteins. The mapping of this generated network to the original network might be of interests to researchers.