{"id":1532,"date":"2024-10-10T15:25:58","date_gmt":"2024-10-10T13:25:58","guid":{"rendered":"https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/?p=1532"},"modified":"2024-10-24T16:50:48","modified_gmt":"2024-10-24T14:50:48","slug":"exploring-the-similarities-of-a-large-bank-of-protein-pockets-in-a-perspective-of-multiple-protein-ligand-interactions-prediction","status":"publish","type":"post","link":"https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/exploring-the-similarities-of-a-large-bank-of-protein-pockets-in-a-perspective-of-multiple-protein-ligand-interactions-prediction\/","title":{"rendered":"Exploring the similarities of a large bank of protein pockets in a perspective of multiple protein-ligand interactions prediction"},"content":{"rendered":"

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2024 <\/em><\/p>\n

Master’s Projects<\/p>\n

@Biology<\/span><\/p>\n

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#Drug design in silico<\/p>\n

#Protein pocket mining<\/p>\n

#Ppocket druggability prediction<\/p>\n

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Project Summary<\/strong><\/h3>\n

With the growing availability of data in the field of drug discovery, AI methods are promising for guiding and accelerating the search process for therapeutic molecules.<\/p>\n

One challenge in drug design is to understand and predict the polypharmacology of molecules, i.e. their ability to interact with multiple protein targets.<\/p>\n

We are developping an AI model to identify molecules capable of binding to a therapeutic target protein of interest but also to predict the proteins likely to bind the same molecules in order to prevent side effects or “off-targets”.<\/p>\n

 <\/p>\n

Anne-Claude Camproux
<\/strong>anne-claude.Camproux@wordpress-test.app.u-pariscite.fr<\/h3>\n