Lead Inventors: Lixia Yao, M.S.;
Andrey Rzhetsky, Ph.D.
Software for indetifying promising candidates in drug discovery and drug development:
Modern drug discovery starts with identifying effective gene or protein targets to which drug molecules can be made to interact. This is due to the fact that there are often many possible targets to consider for any particular disease, and screening of these targets to assess those which are likely to be successful is difficult, time consuming and expensive. Computational methods that can help identify successful drug targets can be used to greatly reduce the number of targets that undergo testing while reducing the overall cost of drug research.
Computational methods used to identify properties of the human genome for drug development:
This technology describes quantitative methods that identify common sequence-, tissue-, and pathway-level properties of the targets of FDA-approved drugs. By using analysis of specific systems-level properties for the whole human genome, this technology helps suggest optimal drug targets. This novel technology can assist pharmaceutical research and drug design by narrowing the prospective set of drug target candidates at the earliest stage of a drug development project.
Applications:
• Pharmaceutical drug target identification and drug design
• Basic research on drug targets
Advantages:
• Greatly reduces number of drug targets to test
• Low cost – computational methods are less expensive than laboratory methods
Patent Status: Software/Copyright
Licensing Status: Available for Licensing and Sponsored Research Support
Publications:
Yao, L. and Rzhetsky, A. Quantitative systems-level determinants of human genes targeted by successful drugs. Genome Res. 2008. 18:206-213.