National Library of Medicine Awards Phase I SBIR Grant to Collaborative Drug Discovery, Inc. to Develop Computational Tools for Biocomputation across Distributed Private Datasets to Enhance Drug Discovery
BURLINGAME, Calif., Sept. 13, 2011 /PRNewswire/ — Collaborative Drug Discovery (CDD), a provider of a web-based drug discovery software platform, announced they have been awarded a grant for a software development project focused on ADME/Tox and tuberculosis drug discovery data sharing.
The Phase I Small Business Innovation Research (SBIR) grant from the National Library of Medicine, part of the National Institutes of Health, is part of a program to enable sharing of biological data.
“Following our valuable collaboration with Pfizer, which resulted in the paper “Using Open Source Computational Tools for Predicting Human Metabolic Stability and Additional Absorption, Distribution, Metabolism, Excretion, and Toxicity Properties” (1) and several publications on tuberculosis computational models (2-6), we realized there was an opportunity to use CDD to host and selectively share computational models” said Sean Ekins, Ph.D., D.Sc., Collaborations Director, CDD. “Our collaborators at Pfizer have demonstrated that computational models generated for very large datasets with open cheminformatics tools were equivalent to those generated with commercial tools and thus provides a way to potentially share models in a platform agnostic manner”.
“The grant will fund proof of concept work using Open source cheminformatics tools in collaboration with pharmaceutical and not for profit groups and will build on our pioneering work of secure selective sharing of data on the cloud,” said Barry Bunin, president & CEO of CDD. “This project extends our work on TB and is complementary with our goal bringing groups together to collaborate and share their data when desirable. The technology will extend the existing innovations selectively sharing data, to selectively sharing models, even without sharing or even uploading sensitive data.” “It has the potential to remove barriers between precompetitive and competitive markets and this grant will enable us to further enhance how the cloud is used and develop a new product in the future”.
The project described was supported by Award Number 1R43LM011152-01 from the National Library of Medicine. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Library of Medicine or the National Institutes of Health.
About Collaborative Drug Discovery, Inc.
CDD (www.collaborativedrug.com) provides the most widely used web-based drug discovery software platform on the market. “CDD Vault(TM)” is the secure, private industrial-strength database combining traditional drug discovery informatics (registration and SAR) with social networking capabilities. “CDD Collaborate(TM)” enables real-time collaboration by securely exchanging selected confidential data with external researchers. “CDD Public(TM)” enables researchers to mine a unique aggregation of information from a variety of scientific data providers.
References Rishi R. Gupta, Gifford, EM, Liston T, Waller CL, Hohman M, Bunin BA and Ekins S, Using open source computational tools for predicting human metabolic stability and additional ADME/ (1) Tox properties, Drug Metab Dispos, 38: 2083-2090, 2010. Ekins S and Freundlich JS, Validating new tuberculosis computational models with public whole cell screening aerobic (2) activity datasets, Pharm Res, 28, 1859-1869, 2011. Lamichhane G, Freundlich JS, Ekins S , Wickramaratne N, Nolan, S and Bishai WR, Essential Metabolites of M. tuberculosis and (3) their small molecule mimics, Mbio, 2: e00301-10, 2011. Ekins S, Freundlich JS, Choi I, Sarker M and Talcott C, Computational Databases, Pathway and Cheminformatics Tools for Tuberculosis Drug Discovery, Trends In Microbiology, 19: (4) 65-74, 2011. Ekins S, Kaneko T, Lipinski CA, Bradford J, Dole K, Spektor A, Gregory K, Blondeau D, Ernst S, Yang J, Goncharoff N, Hohman M and Bunin BA, Analysis and hit filtering of a very large library of compounds screened against Mycobacterium (5) tuberculosis, Mol Biosyst, 6: 2316-2324, 2010. Ekins S, Bradford J, Dole K, Spektor A, Gregory K, Blondeau D, Hohman M and Bunin BA, A Collaborative Database and Computational Models for Tuberculosis Drug Discovery, Mol (6) BioSyst, 6: 840-851, 2010.
SOURCE Collaborative Drug Discovery, Inc.