The usefulness of supervised molecular property prediction is well-recognized in many applications. However, inadequacy and imbalance of labeled data make the learning problem difficult. Moreover, the reliability of the predictions is also a huddle in the distribution of supervised molecular property prediction in the cost and safety-critical application fields, such as drug discovery. We propose an EM3P2 model as a supervised molecular property prediction method that addresses the problem of data insufficiency and reliability. Our proposed method trains an evidential graph isomorphism network classifier using multi-task molecular property datasets on top of a model-agnostic meta-learning (MAML) scheme.
<br>Our model is a well-orchestrated combination of evidential neural networks for estimating model prediction uncertainty, graph isomorphism networks for embedding vector input molecular graphs, and data balance-aware model-agnostic meta-learning for generating a meta-model that adapts to new tasks with little labeled data.
