Few-shot learning has recently attracted significant interest in drug
discovery, with a recent, fast-growing literature mostly involving convoluted
meta-learning strategies. We revisit the more straightforward fine-tuning
approach for molecular data, and propose a regularized quadratic-probe loss
based on the the Mahalanobis distance. We design a dedicated block-coordinate
descent optimizer, which avoid the degenerate solutions of our loss.
Interestingly, our simple fine-tuning approach achieves highly competitive
performances in comparison to state-of-the-art methods, while being applicable
to black-box settings and removing the need for specific episodic pre-training
strategies. Furthermore, we introduce a new benchmark to assess the robustness
of the competing methods to domain shifts. In this setting, our fine-tuning
baseline obtains consistently better results than meta-learning methods.

我们重新审视了更简单的精调方法，基于马氏距离提出了一种正则化的二次探针损失，设计了一种专用的块坐标下降优化器，达到了与现有方法相媲美的竞争性能，并且适用于黑盒设置，消除了特定的情节预训练策略的需求。此外，我们引入了一个新的基准来评估竞争方法对领域变化的健壮性，在这个设置下，我们的精调基准模型始终获得比元学习方法更好的结果。