This study presents a rigorous framework for investigating Molecular Out-Of-Distribution (MOOD) generalization in drug discovery. The concept of MOOD is first clarified through a problem specification that demonstrates how the covariate shifts encountered during real-world deployment can be characterized by the distribution of sample distances to the training set. We find that these shifts can cause performance to drop by up to 60% and uncertainty calibration by up to 40%. This leads us to propose a splitting protocol that aims to close the gap between deployment and testing. Then, using this protocol, a thorough investigation is conducted to assess the impact of model design, model selection and dataset characteristics on MOOD performance and uncertainty calibration. We find that appropriate representations and algorithms with built-in uncertainty estimation are crucial to improve performance and uncertainty calibration. This study sets itself apart by its exhaustiveness and opens an exciting avenue to benchmark meaningful, algorithmic progress in molecular scoring. All related code can be found on Github at https://github.com/cwognum/mood-experiments.
MOOD: Supplementary Material
Provides a variety of additional figures to support the results from the main text.