Evidential Meta-Learning for Molecular Property Prediction

Author(s)
함경표
Advisor
이슬
Department
일반대학원 인공지능학과
Publisher
The Graduate School, Ajou University
Publication Year
2023-08
Language
eng
Keyword
Evidential Neural NetworkFew-shot LearningMeta-LearningMolecular 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.
URI
https://dspace.ajou.ac.kr/handle/2018.oak/24711
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Graduate School of Ajou University > Department of Artificial Intelligence > 3. Theses(Master)
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