Obesity has grown to epidemic proportions, with over 4 million people dying each year as a result of suffering from this health issue in recent years, according to the global burden of disease. Moreover, it is estimated that by 2030, approximately one billion adults globally will develop obesity. These facts demonstrate the importance of creating a completely new and innovative method for the fast development of novel anti-obesity compounds. In silico prediction methods represent an efficient approach for the prediction of drugs targeting obesity, aiming to explain preclinical drug development and therefore enabling the reduction of associated time, costs and experiments.
We here present OBE-DB, a web server for the prediction of obesity drugs that uses two different approaches:
a) Comparison by similarity with a curated database of anti-obesity 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 obesity.
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 obesity drugs.
