Leo Klarner
PhD Student in AI for Drug Discovery at the University of Oxford
leo.klarner [at] stats.ox.ac.uk
I’m a final-year PhD student in the Oxford Protein Informatics (OPIG) and CompStats & Machine Learning (OxCSML) groups. My research focuses on developing more effective deep learning algorithms that are able to leverage scientific prior knowledge and constraints to improve their robustness and generalisation in practical drug discovery settings. Specifically, I’ve worked on using the compositional structure of drug-like molecules to improve the OOD generalisation of molecular property predictors (ICML 2023), as well as generative modelling under domain-informed constraints (NeurIPS 2023).
I feel strongly about understanding the data I work with and have spent many weeks in ChEMBL and PubChem. I am also interested in creating better and more useful datasets and benchmarks (ICML 2023, AI for Chemistry Best Poster Award) and am a main contributor of the open-source Gauche package (NeurIPS 2023).
This work requires familiarity with both modern deep learning research and the realities of practical drug discovery, which I have been fortunate to pick up from my supervisors Yee Whye Teh (Oxford/DeepMind), Charlotte Deane (Oxford) and Garrett Morris (Oxford), as well as Torsten Schindler and Michael Reutlinger (Roche). My work is funded by a Clarendon Scholarship (Oxford’s flagship academic merit scholarship for graduate students) and additional partnership awards from Brasenose College, Oxford and Roche.
Before starting my PhD, I completed a BSc. in Interdisciplinary Sciences (chemistry, biology and CS) at ETH Zürich. During this time, I had the opportunity to design and synthesise antimicrobial peptides with Prof Gisbert Schneider and engineer bacteria for targeted cancer therapy with Prof Simone Schürle-Finke.