Target-directed Atomic Importance Estimation via Reverse Self-attention
Estimating the importance of each atom in a molecule is one of the most appealing and challenging problems in chemistry, physics, and material engineering. The most common way to estimate the atomic importance is to compute the electronic structure using density-functional theory (DFT), and then to interpret it using domain knowledge of human experts. However, this conventional approach is impractical to the large molecular database because DFT calculation requires huge computation, specifically, O(n^4) time complexity w.r.t. the number of electrons in a molecule. Furthermore, the calculation results should be interpreted by the human experts to estimate the atomic importance in terms of the target molecular property. To tackle this problem, we first exploit machine learning-based approach for the atomic importance estimation. To this end, we propose reverse self-attention on graph neural networks and integrate it with graph-based molecular description. Our method provides an efficiently-automated and target-directed way to estimate the atomic importance without any domain knowledge on chemistry and physics.
This paper proposes a reverse self-attention on graph neural networks and integrate it with graph-based molecular description. It provides an efficient-automated and target-directed way to estimate the atomic importance without any domain knowledge on chemistry and physics. In particular, it trains graph attention network to predict the molecular property using the molecular network and calculate the atomic importance scores based on the sum of the incoming attention weights. Pros: This paper presents an interesting application of the graph attention network to other scientific areas. And the developed reverse self-attention score, although simple, is useful in estimating the importance of atoms in a molecular network. Cons: However, the paper also needs some improvement in its evaluation (see detailed comments below). Detailed comments: • In addition to the evaluation metric (7), it is also important to hire human experts to evaluate the performance. For example, just sample some test samples and annotate them by human experts so that they can be compared with the predicted results. • In order to further justify the metric (7), it is also important to report how consistent (7) is with the human evaluation. • The method is not compared with any of the previous methods/baselines. At least, it should evaluate how accurate of the proposed automatic ML-based method is when compared to previous (more expensive) human-based method. • It would be better to mention earlier what are the molecular property to be predicted by GAT during training. Just a few simple examples would be sufficient.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Target-directed Atomic Importance Estimation via Reverse Self-attention
Estimating the importance of each atom in a molecule is one of the most appealing and challenging problems in chemistry, physics, and material engineering. The most common way to estimate the atomic importance is to compute the electronic structure using density-functional theory (DFT), and then to interpret it using domain knowledge of human experts. However, this conventional approach is impractical to the large molecular database because DFT calculation requires huge computation, specifically, O(n^4) time complexity w.r.t. the number of electrons in a molecule. Furthermore, the calculation results should be interpreted by the human experts to estimate the atomic importance in terms of the target molecular property. To tackle this problem, we first exploit machine learning-based approach for the atomic importance estimation. To this end, we propose reverse self-attention on graph neural networks and integrate it with graph-based molecular description. Our method provides an efficiently-automated and target-directed way to estimate the atomic importance without any domain knowledge on chemistry and physics.
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 proposes a reverse self-attention on graph neural networks and integrate it with graph-based molecular description. It provides an efficient-automated and target-directed way to estimate the atomic importance without any domain knowledge on chemistry and physics. In particular, it trains graph attention network to predict the molecular property using the molecular network and calculate the atomic importance scores based on the sum of the incoming attention weights. Pros: This paper presents an interesting application of the graph attention network to other scientific areas. And the developed reverse self-attention score, although simple, is useful in estimating the importance of atoms in a molecular network. Cons: However, the paper also needs some improvement in its evaluation (see detailed comments below). Detailed comments: • In addition to the evaluation metric (7), it is also important to hire human experts to evaluate the performance. For example, just sample some test samples and annotate them by human experts so that they can be compared with the predicted results. • In order to further justify the metric (7), it is also important to report how consistent (7) is with the human evaluation. • The method is not compared with any of the previous methods/baselines. At least, it should evaluate how accurate of the proposed automatic ML-based method is when compared to previous (more expensive) human-based method. • It would be better to mention earlier what are the molecular property to be predicted by GAT during training. Just a few simple examples would be sufficient.
assistant
This paper proposes a reverse self-attention on graph neural networks and integrate it with graph-based molecular description. It provides an efficient-automated and target-directed way to estimate the atomic importance without any domain knowledge on chemistry and physics. In particular, it trains graph attention network to predict the molecular property using the molecular network and calculate the atomic importance scores based on the sum of the incoming attention weights. Pros: This paper presents an interesting application of the graph attention network to other scientific areas. And the developed reverse self-attention score, although simple, is useful in estimating the importance of atoms in a molecular network. Cons: However, the paper also needs some improvement in its evaluation (see detailed comments below). Detailed comments: • In addition to the evaluation metric (7), it is also important to hire human experts to evaluate the performance. For example, just sample some test samples and annotate them by human experts so that they can be compared with the predicted results. • In order to further justify the metric (7), it is also important to report how consistent (7) is with the human evaluation. • The method is not compared with any of the previous methods/baselines. At least, it should evaluate how accurate of the proposed automatic ML-based method is when compared to previous (more expensive) human-based method. • It would be better to mention earlier what are the molecular property to be predicted by GAT during training. Just a few simple examples would be sufficient.