Directional Message Passing for Molecular Graphs
Graph neural networks have recently achieved great successes in predicting quantum mechanical properties of molecules. These models represent a molecule as a graph using only the distance between atoms (nodes) and not the spatial direction from one atom to another. However, directional information plays a central role in empirical potentials for molecules, e.g. in angular potentials. To alleviate this limitation we propose directional message passing, in which we embed the messages passed between atoms instead of the atoms themselves. Each message is associated with a direction in coordinate space. These directional message embeddings are rotationally equivariant since the associated directions rotate with the molecule. We propose a message passing scheme analogous to belief propagation, which uses the directional information by transforming messages based on the angle between them. Additionally, we use spherical Bessel functions to construct a theoretically well-founded, orthogonal radial basis that achieves better performance than the currently prevalent Gaussian radial basis functions while using more than 4x fewer parameters. We leverage these innovations to construct the directional message passing neural network (DimeNet). DimeNet outperforms previous GNNs on average by 77% on MD17 and by 41% on QM9.
This paper beneficially incorporates directional information into graph neural networks for molecular modeling while preserving equivariance. This paper is a tour de force of architecture engineering. Continuous equivariance, potential field representation, and bandwidth limited basis functions are synthesized in a compelling manner. I found the exposition extremely intelligible despite my lack of familiarity with molecular modeling. The contribution is clear, although applicability beyond the specific domain of molecular modeling is possibly limited. Being continuously equivariant with respect to rotations is interesting, but seems to require problems where the input is encoded as a point cloud in a vector space; I*m not familiar with such problems. Nonetheless, the domain of molecular modeling is sufficiently important in isolation. I recommend acceptance, because the contribution is strong, the writing is excellent, the ideas are well-motivated, and the experiments support the empirical claims.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Directional Message Passing for Molecular Graphs
Graph neural networks have recently achieved great successes in predicting quantum mechanical properties of molecules. These models represent a molecule as a graph using only the distance between atoms (nodes) and not the spatial direction from one atom to another. However, directional information plays a central role in empirical potentials for molecules, e.g. in angular potentials. To alleviate this limitation we propose directional message passing, in which we embed the messages passed between atoms instead of the atoms themselves. Each message is associated with a direction in coordinate space. These directional message embeddings are rotationally equivariant since the associated directions rotate with the molecule. We propose a message passing scheme analogous to belief propagation, which uses the directional information by transforming messages based on the angle between them. Additionally, we use spherical Bessel functions to construct a theoretically well-founded, orthogonal radial basis that achieves better performance than the currently prevalent Gaussian radial basis functions while using more than 4x fewer parameters. We leverage these innovations to construct the directional message passing neural network (DimeNet). DimeNet outperforms previous GNNs on average by 77% on MD17 and by 41% on QM9.
user
<think>Okay, to do a decent peer review, I should start with summarizing what the work is about. This includes novelty, contributions, clarity, and impact. I might include suggestions to issues that I find critical to the usefulness of the work. Starting with novelty, there are different types of novelty. The first aspect of novelty is scope and scale. Is the work covering a broader perspective than previous works? The second aspect is methodology. Is the proposed methodology novel compared to previous methods? Do they provide any significant advantage over previous methodologies? The third aspect is results. Are the results different compared previous work? Does this lead to any novel theories or an extension of current theories? Next is clarity, I will be reading the work in depth and analyze if any sentences can be potentially ambiguous or misleading. If any sentences are ambiguous, I will state them and give a suggestion on how to rephrase such sentence or how to make more clear to the audience. Lastly, impact is important. I will attempt to predict how such work will impact the scientific community. This might include the applications of such work and how the work might transform the way we study a certain topic. Finally, I will might only mention issues that are critical. This is to keep the review as concise and helpful as possible.</think>



This paper beneficially incorporates directional information into graph neural networks for molecular modeling while preserving equivariance. This paper is a tour de force of architecture engineering. Continuous equivariance, potential field representation, and bandwidth limited basis functions are synthesized in a compelling manner. I found the exposition extremely intelligible despite my lack of familiarity with molecular modeling. The contribution is clear, although applicability beyond the specific domain of molecular modeling is possibly limited. Being continuously equivariant with respect to rotations is interesting, but seems to require problems where the input is encoded as a point cloud in a vector space; I*m not familiar with such problems. Nonetheless, the domain of molecular modeling is sufficiently important in isolation. I recommend acceptance, because the contribution is strong, the writing is excellent, the ideas are well-motivated, and the experiments support the empirical claims.
assistant
This paper beneficially incorporates directional information into graph neural networks for molecular modeling while preserving equivariance. This paper is a tour de force of architecture engineering. Continuous equivariance, potential field representation, and bandwidth limited basis functions are synthesized in a compelling manner. I found the exposition extremely intelligible despite my lack of familiarity with molecular modeling. The contribution is clear, although applicability beyond the specific domain of molecular modeling is possibly limited. Being continuously equivariant with respect to rotations is interesting, but seems to require problems where the input is encoded as a point cloud in a vector space; I*m not familiar with such problems. Nonetheless, the domain of molecular modeling is sufficiently important in isolation. I recommend acceptance, because the contribution is strong, the writing is excellent, the ideas are well-motivated, and the experiments support the empirical claims.