Machine Learning for Molecules
John Bradshaw (Ph.D. Student)
Machine Learning has enormous potential in augmenting scientists' capabilities in discovering novel drugs or new materials. To achieve this we need to develop ML models that can accurately predict properties of molecules and their interactions, as well as techniques that enable intelligent searching of discrete and complicated molecular spaces. In addition, I believe it is important for such models to have well calibrated uncertainties as well as providing interpretability to any end-user. I therefore wish to research how this can be done, in particular by exploring how we can build into our models pre-existing scientific knowledge.
|Primary Host:||José Miguel Hernández-Lobato (University of Cambridge)|
|Exchange Host:||Bernhard Schölkopf (ELLIS Institute Tübingen & Max Planck Institute for Intelligent Systems)|
|PhD Duration:||01 October 2016 - 23 July 2021|
|Exchange Duration:||01 November 2018 - 31 December 2019 - Ongoing|