My research interests involve developing robust machine learning methods for early drug-discovery problems. Recently, I’ve been working on developing structure based scoring functions that work better in an out-of-distribution (OOD) setting, i.e. when the training data occupies a different area in chemical space than the test data. Additionally, I’ve been exploring different ways to benchmark the OOD performance of binding affinity predictors, and different ways of featurising bound protein-ligand complexes.