Pretrained Graph Neural Networks have been widely adopted for various
molecular property prediction tasks. Despite their ability to encode structural
and relational features of molecules, traditional fine-tuning of such
pretrained GNNs on the target task can lead to poor generalization. To address
this, we explore the adaptation of pretrained GNNs to the target task by
jointly training them with multiple auxiliary tasks. This could enable the GNNs
to learn both general and task-specific features, which may benefit the target
task. However, a major challenge is to determine the relatedness of auxiliary
tasks with the target task. To address this, we investigate multiple strategies
to measure the relevance of auxiliary tasks and integrate such tasks by
adaptively combining task gradients or by learning task weights via bi-level
optimization. Additionally, we propose a novel gradient surgery-based approach,
Rotation of Conflicting Gradients ($\mathtt{RCGrad}$), that learns to align
conflicting auxiliary task gradients through rotation. Our experiments with
state-of-the-art pretrained GNNs demonstrate the efficacy of our proposed
methods, with improvements of up to 7.7% over fine-tuning. This suggests that
incorporating auxiliary tasks along with target task fine-tuning can be an
effective way to improve the generalizability of pretrained GNNs for molecular
property prediction.

既训练的图神经网络（Pretrained Graph Neural Networks）广泛应用于各种分子属性预测任务，但由于传统的精调对目标任务的训练会导致较差的泛化性能，因此本文探讨了通过与多个辅助任务共同训练的方式来适应目标任务的预训练图神经网络。实验证明了我们提出的方法的有效性，在最先进的预训练图神经网络上的改进效果达到了 7.7％，这表明在分子属性预测中，将辅助任务与目标任务的精调结合起来可以有效提高预训练图神经网络的泛化能力。