ComENet: Towards Complete and Efficient Message Passing for 3D Molecular Graphs (2206.08515v3)

Abstract: Many real-world data can be modeled as 3D graphs, but learning representations that incorporates 3D information completely and efficiently is challenging. Existing methods either use partial 3D information, or suffer from excessive computational cost. To incorporate 3D information completely and efficiently, we propose a novel message passing scheme that operates within 1-hop neighborhood. Our method guarantees full completeness of 3D information on 3D graphs by achieving global and local completeness. Notably, we propose the important rotation angles to fulfill global completeness. Additionally, we show that our method is orders of magnitude faster than prior methods. We provide rigorous proof of completeness and analysis of time complexity for our methods. As molecules are in essence quantum systems, we build the \underline{com}plete and \underline{e}fficient graph neural network (ComENet) by combing quantum inspired basis functions and the proposed message passing scheme. Experimental results demonstrate the capability and efficiency of ComENet, especially on real-world datasets that are large in both numbers and sizes of graphs. Our code is publicly available as part of the DIG library (\url{https://github.com/divelab/DIG}).

ComENet: Towards Complete and Efficient Message Passing for 3D Molecular Graphs

The paper "ComENet: Towards Complete and Efficient Message Passing for 3D Molecular Graphs" introduces a novel approach to 3D molecular graph representation learning through a designed message passing framework. This work addresses the need for incorporating three-dimensional (3D) information into graph neural networks (GNNs) to effectively model molecular structures, a challenge due to either incomplete integration of 3D information or high computational complexity in previous methods.

Key Contributions

1. Complete 3D Representation: ComENet aims to achieve complete 3D molecular graph representations by proposing a message passing scheme that ensures both global and local completeness. This is accomplished through the introduction of rotation angles crucial for distinguishing different molecular conformers.
2. Efficient Computation: The proposed method significantly reduces computational overhead with a complexity of $O(nk)$, compared to existing models that often incur $O(nk^2)$ or $O(nk^3)$, enabling more scalable application to large datasets.
3. Geometric Completeness Proof: The authors provide a rigorous mathematical validation of the completeness of their geometric transformation, ensuring that the model can differentiate any two distinct 3D graphs.
4. Integration with Quantum-Inspired Features: The network structure is enhanced with quantum-inspired basis functions, which fortifies the model’s ability to capture the essential quantum properties of molecules.

Results and Comparisons

ComENet demonstrates substantial improvements in efficiency and competitive performance on benchmark datasets such as OC20, Molecule3D, and QM9. The model achieves energy predictions with superior accuracy while reducing training times by 6-10 times compared to leading baselines like SphereNet and DimeNet++.

The experimental results highlight ComENet’s effectiveness in achieving complete 3D graph representations, evidenced by its robust performance on large-scale datasets and its ability to generalize to previously unseen data domains.

Implications and Speculation on Future Developments

The development of ComENet presents significant implications for molecular machine learning and associated fields like drug and materials discovery. Its ability to provide accurate and efficient 3D molecular representations could facilitate enhanced predictive models for molecular properties and behaviors. The model’s scalability can potentially lead to its application in real-world datasets where the size and complexity pose challenges to traditional methods.

Looking forward, the integration of 3D information in molecular graphs through efficient and complete methods like ComENet could propel further advancements in areas such as quantum chemistry simulations and molecular dynamics. Future research might explore extending these concepts to more complex systems, possibly incorporating more varied and dynamic environmental variables.

ComENet sets a precedent for addressing the dual challenge of completeness and efficiency, paving the way for further research into optimizing message passing schemes in high-dimensional graph spaces.