Fragment based-drug screening is a popular approach for drug discovery in which a set of small chemical compounds is assayed against biological targets in order to identify weekly binding hits that can be later exploited to produce lead compounds. My research focuses on the development of machine learning tools that exploit 3D information about hits and protein targets with the aim of facilitating drug discovery. I am particularly interested in automatic fragment merging and compound scoring using non-supervised machine learning techniques. In collaboration with the XChem team, we pursue to implement ready-to-use applications that can help experimentalist with no computational skills to perform better experiments.