  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.
