Abstract:

<p>The current issue of the Biomedical Data Journal (BMDJ) on <em>Computational Drug Discovery</em> focuses on the publication of data sets that will allow researchers to investigate deeper computational techniques for drug design and drug repositioning for pancreatic cancer and cardiovascular disorders. Drug repositioning is the task of finding new targets for old drugs and has been in the spotlight for the past few years. The average cost for launching a new drug into the market is estimated to ~1.8 billion dollars. Apart from that, the drugs that make it to the market are very few. Notably, from 1999 to 2008, only 50 compounds were <em>FDA </em>approved in the U.S., out of which 17 were identified as arising from target-based discovery methods. This stresses the importance of drug repositioning in the process of drug development, since it accelerates the process, minimizes the associated costs, and, in parallel, contributes to the prevention of noxious adverse events and toxicological liabilities.</p><p>Especially for the case studies of pancreatic cancer and cardiovascular disorders, there is a dire need of datasets which would allow researchers to examine drug repositioning opportunities, and certainly could act as an important curating or guiding factor for any future drug design efforts. Given this need, the current issue focuses on datasets in these two cases.</p>

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Benchmark Datasets for Computational Drug Discovery