Facing serious challenges, the industry is looking for ways to shorten the drug discovery cycle without taking additional risks. Techniques such as high throughput screening, genomics, and proteomics generate enormous amounts of data. However, they impose an even bigger challenge to process that flood of information without delaying the drug development process. If both context-based models and data-driven methods are used for the knowledge discovery process then these new scientific techniques could be utilized in a much more structured way. By definition, these technologies require close cooperation between information technology experts and research scientists, enabling more creative project management. This will lead directly to a scenario in which leads, targets, and drug candidates are selected with much higher quality.
This is a preview of subscription content, log in to check access.
Buy single article
Instant unlimited access to the full article PDF.
Price includes VAT for USA
Subscribe to journal
Immediate online access to all issues from 2019. Subscription will auto renew annually.
This is the net price. Taxes to be calculated in checkout.
Hawkins DM, Young SS, Rusinko A. Analysis of a large structure-activity data set using recursive partitioning. Quantitative Structure Activity Relationship. 1997;16:296–302.
Engels MFM, Knapen K, Tollenaere JP. Approaches for mining HTS data sets. 13th Symposium on QSAR, Proceedings, Düsseldorf, Germany, 2000.
Lowell J, Kluger J. Apollo 13. New York, NY: Pocket Books; 1994.
Leighton R, Feynman RP. What Do You Care What Other People Think? Further Adventures of a Curious Character. New York, NY: Bantam Doubleday Dell; 1992.
About this article
Cite this article
Meyer, H.F. Streamlining the Research and Development Pipeline by Coupling of Information Technology and Biology. Ther Innov Regul Sci 36, 169–178 (2002) doi:10.1177/009286150203600122
- Research and development
- Information technology