is the 8th leading cause of death worldwide, with 1.5 million deaths in 2012, and medical costs that reached 245$ billion in the US. Moreover, it is estimated that roughly 387 million people worldwide
suffer from diabetes mellitus, with numbers growing rapidly. These facts demonstrate the importance of creating a completely new and innovative method for the fast development of novel anti-diabetic compounds
. In silico prediction methods represent an efficient approach for the prediction of diabetes drugs, aiming to explaining preclinical drug development and therefore enabling the reduction of associated time, costs and experiments.
We present here DIA-DB, a web server for the prediction of diabetes drugs that uses two different approaches:
a) comparison by similarity with a curated database of anti-diabetic drugs and experimental compounds
b) inverse virtual screening of the input molecules chosen by the users against a set of protein targets identified as key elements in di-abetes.
The server is open to all users. registration is not necessary, and a detailed report with the prediction results are sent to the user by email. This is the first public domain database for diabetes drugs.
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