Evidential meta-model for molecular property prediction

MOTIVATION: The usefulness of supervised molecular property prediction (MPP) is well-recognized in many applications. However, the insufficiency and the imbalance of labeled data make the learning problem difficult. Moreover, the reliability of the predictions is also a huddle in the deployment of MPP models in safety-critical fields. RESULTS: We propose the Evidential Meta-model for Molecular Property Prediction (EM3P2) method that returns uncertainty estimates along with its predictions. Our EM3P2 trains an evidential graph isomorphism network classifier using multi-task molecular property datasets under the model-agnostic meta-learning (MAML) framework while addressing the problem of data imbalance. : Our results showed better prediction performances compared to existing meta-MPP models. Furthermore, we showed that the uncertainty estimates returned by our EM3P2 can be used to reject uncertain predictions for applications that require higher confidence. AVAILABILITY AND IMPLEMENTATION: Source code available for download at https://github.com/Ajou-DILab/EM3P2.