Calibration and generalizability of probabilistic models on low-data chemical datasets with DIONYSUS

Understand

Deep learning models that leverage large datasets are often the state of the art for modelling molecular properties.

• When the datasets are smaller (< 2000 molecules), it is not clear that deep learning approaches are the right modelling tool.
• In this work we perform an extensive study of the calibration and generalizability of probabilistic machine learning models on small chemical datasets.
• Using different molecular representations and models, we analyse the quality of their predictions and uncertainties in a variety of tasks (binary, regression) and datasets.