AJOU Central Library Repository: Evidential Meta-Learning for Molecular Property Prediction

Graduate School of Ajou University Department of Artificial Intelligence 3. Theses(Master)

Evidential Meta-Learning for Molecular Property Prediction

Keyword: Evidential Neural Network; Few-shot Learning; Meta-Learning; Molecular Property Prediction

Alternative Abstract: 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.

Appears in Collections:: Graduate School of Ajou University > Department of Artificial Intelligence > 3. Theses(Master)

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