Does GNN Pretraining Help Molecular Representation? (2207.06010v2)

Abstract: Extracting informative representations of molecules using Graph neural networks (GNNs) is crucial in AI-driven drug discovery. Recently, the graph research community has been trying to replicate the success of self-supervised pretraining in natural language processing, with several successes claimed. However, we find the benefit brought by self-supervised pretraining on small molecular data can be negligible in many cases. We conduct thorough ablation studies on the key components of GNN pretraining, including pretraining objectives, data splitting methods, input features, pretraining dataset scales, and GNN architectures, to see how they affect the accuracy of the downstream tasks. Our first important finding is, self-supervised graph pretraining do not always have statistically significant advantages over non-pretraining methods in many settings. Secondly, although noticeable improvement can be observed with additional supervised pretraining, the improvement may diminish with richer features or more balanced data splits. Thirdly, hyper-parameters could have larger impacts on accuracy of downstream tasks than the choice of pretraining tasks, especially when the scales of downstream tasks are small. Finally, we provide our conjectures where the complexity of some pretraining methods on small molecules might be insufficient, followed by empirical evidences on different pretraining datasets.

• The paper demonstrates that supervised GNN pretraining aligned with specific molecular tasks significantly improves performance compared to self-supervised methods.
• The study evaluates various pretraining objectives, data splits, and hyperparameters, revealing that robust feature sets and dataset saturation can limit the benefits of self-supervised pretraining.
• The paper suggests that future research should develop more complex pretraining tasks, incorporate 3D structural insights, and explore diverse GNN architectures to enhance molecular modeling.

Analyzing the Impact of GNN Pretraining on Molecular Representation

The paper "Does GNN Pretraining Help Molecular Representation?" provides a comprehensive investigation into the effectiveness of pretraining Graph Neural Networks (GNNs) specifically for the task of molecular representation. Recent advancements in natural language processing and computer vision have demonstrated substantial performance gains from self-supervised pretraining methods. This paper evaluates whether similar benefits can be accrued in the domain of molecular modeling with GNNs.

The investigation scrutinizes a range of pretraining methodologies, including self-supervised and supervised objectives, to determine their impact on downstream molecular tasks. The paper presents an empirical analysis encompassing different pretraining objectives, dataset splits, model architectures, and hyperparameter settings to ascertain the actual influence of pretraining on task-specific performance.

Key Findings

1. Self-supervised Pretraining:
   • The paper highlights that self-supervised GNN pretraining does not consistently yield statistically significant improvements over training from scratch across commonly used molecular datasets.
   • Various objectives such as node prediction, context prediction, motif prediction, and graph-level contrastive learning were evaluated, and none demonstrated significant advantages in improving downstream task performance conclusively.
2. Supervised Pretraining:
   • Unlike self-supervised methods, supervised pretraining yielded notable performance gains, especially when pretraining tasks were closely aligned with downstream molecular tasks.
   • The paper infers that the alignment between pretraining labels (like those in the ChEMBL dataset) and downstream objectives contributes substantially to observed improvements.
3. Impact of Data Splits and Features:
   • The scaffold data splitting technique, which aims to bridge the gap between train and test distributions and focuses on out-of-distribution generalization, exhibited divergent results.
   • Rich feature sets diminished the impact of pretraining, suggesting that well-crafted features could compensate for the lack of pretraining gains in some instances.
4. Hyperparameters and Data Scale:
   • Hyperparameter optimization was found to play a critical role in determining the effect of pretraining, often influencing conclusions about its utility.
   • Surprisingly, increasing the scale of the pretraining dataset with significantly larger sets like SAVI did not improve performance, possibly due to already quick saturation and high accuracy during pretraining on smaller sets like ZINC15.
5. Architectural Consistency:
   • Similar findings were replicated across different GNN architectures, indicating consistency in the conclusions regardless of specific model choices.

Implications and Future Directions

The research suggests that current self-supervised pretraining tasks may be insufficiently complex or relevant to benefit tasks in molecular representation, posing a challenge for researchers aspiring to utilize pretraining for improved molecular graph learning. Future endeavors may need to focus on designing more challenging pretraining objectives that can leverage richer structural and contextual information inherent in molecules, possibly incorporating 3D structural insights.

Additionally, there is further scope to explore diverse GNN architectures, learning objectives, and other aligned molecular datasets that were not covered in this paper. Such explorations could provide more nuanced insights into conditions under which GNN pretraining might become beneficial in the molecular domain.

Researchers in the field must also account for the potential impact of data diversity and complexity, as well as the nature of downstream generalization tasks, which can substantially influence the relative utility of pretraining. Overall, while pretraining methodologies have transformed several domains, their efficacy in molecular representation warrants careful assessment and targeted innovation to unlock its full potential.