Meta-Learning GNN Initializations for Low-Resource Molecular Property Prediction
This work addresses low-resource drug discovery by improving deep learning performance for chemical property prediction, though it is incremental as it adapts existing meta-learning methods to a specific domain.
The paper tackled the problem of limited labeled data in molecular property prediction by applying meta-learning to graph neural network initializations, resulting in meta-initializations outperforming baselines on most tasks with average AUPRC improvements of 11.2% in-distribution and 26.9% out-of-distribution.
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.