Pre-training Molecular Graph Representation with 3D Geometry (2110.07728v2)

Abstract: Molecular graph representation learning is a fundamental problem in modern drug and material discovery. Molecular graphs are typically modeled by their 2D topological structures, but it has been recently discovered that 3D geometric information plays a more vital role in predicting molecular functionalities. However, the lack of 3D information in real-world scenarios has significantly impeded the learning of geometric graph representation. To cope with this challenge, we propose the Graph Multi-View Pre-training (GraphMVP) framework where self-supervised learning (SSL) is performed by leveraging the correspondence and consistency between 2D topological structures and 3D geometric views. GraphMVP effectively learns a 2D molecular graph encoder that is enhanced by richer and more discriminative 3D geometry. We further provide theoretical insights to justify the effectiveness of GraphMVP. Finally, comprehensive experiments show that GraphMVP can consistently outperform existing graph SSL methods.

• The paper demonstrates integrating 3D geometric data with 2D molecular graphs to improve representations for drug and material discovery.
• The method, GraphMVP, uses contrastive and generative self-supervised learning with InfoNCE and VRR to align 2D and 3D views.
• Empirical results confirm GraphMVP's robust performance and scalability across diverse molecular property prediction tasks.

Pre-training Molecular Graph Representation with 3D Geometry: An Expert Perspective

The paper "Pre-training Molecular Graph Representation with 3D Geometry" addresses a pivotal challenge in molecular representation learning, which is foundational for drug and material discovery. Traditional approaches primarily rely on the 2D topological structure of molecular graphs, but recent advancements underscore the significant predictive power of incorporating 3D geometric information. The proposed Graph Multi-View Pre-training (GraphMVP) framework introduces a self-supervised learning (SSL) approach to effectively align and integrate 2D and 3D molecular data, enhancing downstream performance even when 3D data is not available.

Theoretical and Methodological Insights

GraphMVP innovatively leverages the success of pretraining-finetuning pipelines akin to BERT and applies it to molecular graph representations. The framework is based on the consistency between 2D and 3D geometric views. GraphMVP consists of two main self-supervised tasks: contrastive SSL and generative SSL.

• Contrastive SSL: This task creates supervised signals by treating 2D-3D pairs from the same molecule as positive and others as negative, using both InfoNCE and a novel EBM-NCE objective. EBM-NCE applies energy-based models and noise-contrastive estimation to model the data distribution, aligning with the mutual information maximization principle.
• Generative SSL: Unlike traditional reconstruction in data space, GraphMVP introduces a surrogate loss, Variational Representation Reconstruction (VRR), to perform reconstruction in representation space. This approach approximates mutual information, making it suitable for handling the continuous nature of 3D geometric data.

Robust Experimental Validation

The empirical results notably demonstrate GraphMVP's consistent superiority over existing 2D-only graph SSL methods across multiple molecular property prediction datasets. By effectively utilizing conformer ensembles, GraphMVP achieves a significant enhancement in 2D molecular graph representation. Detailed ablation studies reveal the influence of masking ratios and the number of conformers used, indicating optimal configurations that balance efficacy and computational costs.

Implications and Future Developments

GraphMVP represents an advancement in integrating 3D geometric information into molecular graph pretraining frameworks, expanding the methodological toolkit for chemoinformatics. The framework's robust performance across diverse downstream tasks underscores its practical applicability, suggesting potential adaptations in related domains like material science and biology. Future work could explore more sophisticated 2D and 3D neural architectures, adapt the framework for large-scale molecular datasets, and consider its application to other graph-based domains such as protein-ligand interactions.

Conclusion

This paper presents a well-rounded and methodologically sound advancement in molecular graph representation learning. GraphMVP not only advances our understanding by highlighting the importance of 3D geometry in molecular representation but also provides a scalable framework adaptable to various datasets and domains. This work lays the foundation for further exploration of pre-training strategies in molecular sciences, with promising implications for accelerating discoveries in drug and material research.