{"id":170,"date":"2024-04-09T01:26:58","date_gmt":"2024-04-08T23:26:58","guid":{"rendered":"https:\/\/sfsb.farm.unipi.it\/?page_id=170"},"modified":"2026-03-04T13:39:44","modified_gmt":"2026-03-04T12:39:44","slug":"galati-salvatore","status":"publish","type":"page","link":"https:\/\/sfsb.farm.unipi.it\/en\/cicli-conclusi\/galati-salvatore\/","title":{"rendered":"Galati Salvatore"},"content":{"rendered":"
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Contacts<\/strong>: <\/p>\n\n\n\n

salvatore.galati@phd.unipi.it<\/a><\/p>\n\n\n\n

Supervisor(s)<\/strong>:<\/p>\n\n\n\n

Professor Tiziano Tuccinardi<\/p>\n\n\n\n

PhD project Title<\/strong>:<\/p>\n\n\n\n

Development of a reliable target fishing platform based on a consensus artifical intelligence approach<\/p>\n\n\n\n

Abstract of the PhD project<\/strong>:<\/p>\n\n\n\n

My PhD research areas majorly involve computational drug discovery using an artificial intelligence approach. Machine learning (ML) is the study of computer algorithms that improve automatically through experience. Machine learning algorithms build a mathematical model based on sample data, known as \u201ctraining data\u201d, in order to make predictions or decisions without being explicitly programmed to do so. Recently, there has been increasing interest in ML in many areas of science including pharmaceutical research. In the context of compound activity prediction, which is a core task in computational medicinal chemistry, this principle implies that some structural features and\/or molecular properties should be common to active compounds, regardless of their targets. The aim of my project is to develop an innovative approach for virtual screening that can improve the limitation of current QSAR methods that fail to build a model for targets with few data.<\/p>\n\n\n\n

Publications link: <\/strong><\/p>\n\n\n\n

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Oral communications at conferences:<\/strong><\/p>\n\n\n\n