Fragment-based drug discovery is an effective and efficient strategy for generating target-optimized drugs. Its success rests in part on careful design of the underlying fragment library. My project aim is to formulate design rules of what constitutes a “good” fragment by harnessing information across diverse target groups. In doing so, I am developing statistical methods that combine structural modelling, machine learning and statistical filtering to identify relevant patterns in binding data, while making optimal use of ligand structure. The resulting algorithms and models will provide deeper insight into molecular reactivity, improve the composition of fragment libraries, and boost the in-silico aspects of fragment-based drug discovery.