Meta-Learning GNN Initializations for Low-Resource Molecular Property Prediction
| Autor: | Nguyen, Cuong Q., Kreatsoulas, Constantine, Branson, Kim M. |
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| Rok vydání: | 2020 |
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| Druh dokumentu: | Working Paper |
| Popis: | Building in silico models to predict chemical properties and activities is a crucial step in drug discovery. However, limited labeled data often hinders the application of deep learning in this setting. Meanwhile advances in meta-learning have enabled state-of-the-art performances in few-shot learning benchmarks, naturally prompting the question: Can meta-learning improve deep learning performance in low-resource drug discovery projects? In this work, we assess the transferability of graph neural networks initializations learned by the Model-Agnostic Meta-Learning (MAML) algorithm - and its variants FO-MAML and ANIL - for chemical properties and activities tasks. Using the ChEMBL20 dataset to emulate low-resource settings, our benchmark shows that meta-initializations perform comparably to or outperform multi-task pre-training baselines on 16 out of 20 in-distribution tasks and on all out-of-distribution tasks, providing an average improvement in AUPRC of 11.2% and 26.9% respectively. Finally, we observe that meta-initializations consistently result in the best performing models across fine-tuning sets with $k \in \{16, 32, 64, 128, 256\}$ instances. Comment: ICML 2020 Workshop on Graph Representation Learning and Beyond (GRL+) |
| Databáze: | arXiv |
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