Galati Salvatore

Contacts:

salvatore.galati@phd.unipi.it

Supervisor(s):

Professor Tiziano Tuccinardi

PhD project Title:

Development of a reliable target fishing platform based on a consensus artifical intelligence approach

Abstract of the PhD project:

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 “training data”, 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.

Publications link:

Oral communications at conferences:

  • “Use and rationalization of machine learning approach for predicting isoform-selective inhibitors of carbonic anhydrase enzyme” CFF 2021- Chemistry for the Future International Conference, University of Pisa (Italy); 30/06/2021 – 02/07/2021.
  • “Structure-based analysis of machine learning predictions for isoform-selective carbonic anhydrase inhibitors” AMYC-BIOMED 2021, online, 03/11/2021 – 05/11/2021.
  • “VenomPred: A Machine Learning Based Platform for Molecular Toxicity Predictions”; 3rd MMCS: Shaping Medicinal Chemistry for the New Decade, La Sapienza University of Rome, Rome (Italy);27/07/2022 – 29/07/2022.
  • “MolBook: Compose, Manage and Analyze your chemical-based database easily and for free” XII Paul Ehrlich PhD NetWork in Medicinal Chemistry, University of Thessaloniki (Greece) 16/06/2023 – 18/07/2023.

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