Learning to Make Generalizable and Diverse Predictions for Retrosynthesis
We propose a new model for making generalizable and diverse retrosynthetic reaction predictions. Given a target compound, the task is to predict the likely chemical reactants to produce the target. This generative task can be framed as a sequence-to-sequence problem by using the SMILES representations of the molecules. Building on top of the popular Transformer architecture, we propose two novel pre-training methods that construct relevant auxiliary tasks (plausible reactions) for our problem. Furthermore, we incorporate a discrete latent variable model into the architecture to encourage the model to produce a diverse set of alternative predictions. On the 50k subset of reaction examples from the United States patent literature (USPTO-50k) benchmark dataset, our model greatly improves performance over the baseline, while also generating predictions that are more diverse.
This paper is very well-written and combines the state-of-the-art NLP model and the domain knowledge in retrosynthetic reaction predictions. The authors propose pre-training models to help improve the model*s generation to rare reactions. In addition, a discrete latent variable model is used in the model to encourage the model to produce a diverse set of alternative predictions. The experiments in the paper also show the effectiveness of the two main contributions. The main contribution of this paper is to apply the state-of-the-art Transformer model and other techniques in NLP to address the specific issues in retrosynthesis. Both the pre-training model and the mixture model are combining the specific domain knowledge to improve the generalization and diversity of retrosynthesis. There is not a huge algorithm novelty for the methods proposed in this paper, but they can well address the domain issues and improve the performance. My only concern is that the baseline model compared in the paper is Schwaller*s work. I am not pretty sure if this baseline achieves state-of-the-art performance. It would be very interesting to see more comparisons with other state-of-the-art work.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Learning to Make Generalizable and Diverse Predictions for Retrosynthesis
We propose a new model for making generalizable and diverse retrosynthetic reaction predictions. Given a target compound, the task is to predict the likely chemical reactants to produce the target. This generative task can be framed as a sequence-to-sequence problem by using the SMILES representations of the molecules. Building on top of the popular Transformer architecture, we propose two novel pre-training methods that construct relevant auxiliary tasks (plausible reactions) for our problem. Furthermore, we incorporate a discrete latent variable model into the architecture to encourage the model to produce a diverse set of alternative predictions. On the 50k subset of reaction examples from the United States patent literature (USPTO-50k) benchmark dataset, our model greatly improves performance over the baseline, while also generating predictions that are more diverse.
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 is very well-written and combines the state-of-the-art NLP model and the domain knowledge in retrosynthetic reaction predictions. The authors propose pre-training models to help improve the model*s generation to rare reactions. In addition, a discrete latent variable model is used in the model to encourage the model to produce a diverse set of alternative predictions. The experiments in the paper also show the effectiveness of the two main contributions. The main contribution of this paper is to apply the state-of-the-art Transformer model and other techniques in NLP to address the specific issues in retrosynthesis. Both the pre-training model and the mixture model are combining the specific domain knowledge to improve the generalization and diversity of retrosynthesis. There is not a huge algorithm novelty for the methods proposed in this paper, but they can well address the domain issues and improve the performance. My only concern is that the baseline model compared in the paper is Schwaller*s work. I am not pretty sure if this baseline achieves state-of-the-art performance. It would be very interesting to see more comparisons with other state-of-the-art work.
assistant
This paper is very well-written and combines the state-of-the-art NLP model and the domain knowledge in retrosynthetic reaction predictions. The authors propose pre-training models to help improve the model*s generation to rare reactions. In addition, a discrete latent variable model is used in the model to encourage the model to produce a diverse set of alternative predictions. The experiments in the paper also show the effectiveness of the two main contributions. The main contribution of this paper is to apply the state-of-the-art Transformer model and other techniques in NLP to address the specific issues in retrosynthesis. Both the pre-training model and the mixture model are combining the specific domain knowledge to improve the generalization and diversity of retrosynthesis. There is not a huge algorithm novelty for the methods proposed in this paper, but they can well address the domain issues and improve the performance. My only concern is that the baseline model compared in the paper is Schwaller*s work. I am not pretty sure if this baseline achieves state-of-the-art performance. It would be very interesting to see more comparisons with other state-of-the-art work.