Using Deep Reinforcement Learning to Generate Rationales for Molecules
Deep learning algorithms are increasingly used in modeling chemical processes. However, black box predictions without rationales have limited used in practical applications, such as drug design. To this end, we learn to identify molecular substructures -- rationales -- that are associated with the target chemical property (e.g., toxicity). The rationales are learned in an unsupervised fashion, requiring no additional information beyond the end-to-end task. We formulate this problem as a reinforcement learning problem over the molecular graph, parametrized by two convolution networks corresponding to the rationale selection and prediction based on it, where the latter induces the reward function. We evaluate the approach on two benchmark toxicity datasets. We demonstrate that our model sustains high performance under the additional constraint that predictions strictly follow the rationales. Additionally, we validate the extracted rationales through comparison against those described in chemical literature and through synthetic experiments.
 In this manuscript, the authors propose an interesting deep reinforcement learning approach via CNNs to learn the rationales associated to target chemical properties. The paper has merit, but in its current form does not match the acceptance criteria for ICLR. In particular, the main issue lies in the poor performance reached by the systems, both overall and in comparison with baseline methods, which at the moment hardly justifies the effort required in setting up the DL framework. Moreover, the fact that test performances are sometimes (much) better than training results are quite suspicious in methodological terms. Finally, the experimental part is quite limited (two small datasets), making it hard to evaluate the scalability (in all sense) of the proposed solution to much larger data.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Using Deep Reinforcement Learning to Generate Rationales for Molecules
Deep learning algorithms are increasingly used in modeling chemical processes. However, black box predictions without rationales have limited used in practical applications, such as drug design. To this end, we learn to identify molecular substructures -- rationales -- that are associated with the target chemical property (e.g., toxicity). The rationales are learned in an unsupervised fashion, requiring no additional information beyond the end-to-end task. We formulate this problem as a reinforcement learning problem over the molecular graph, parametrized by two convolution networks corresponding to the rationale selection and prediction based on it, where the latter induces the reward function. We evaluate the approach on two benchmark toxicity datasets. We demonstrate that our model sustains high performance under the additional constraint that predictions strictly follow the rationales. Additionally, we validate the extracted rationales through comparison against those described in chemical literature and through synthetic experiments.
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>



 In this manuscript, the authors propose an interesting deep reinforcement learning approach via CNNs to learn the rationales associated to target chemical properties. The paper has merit, but in its current form does not match the acceptance criteria for ICLR. In particular, the main issue lies in the poor performance reached by the systems, both overall and in comparison with baseline methods, which at the moment hardly justifies the effort required in setting up the DL framework. Moreover, the fact that test performances are sometimes (much) better than training results are quite suspicious in methodological terms. Finally, the experimental part is quite limited (two small datasets), making it hard to evaluate the scalability (in all sense) of the proposed solution to much larger data.
assistant
 In this manuscript, the authors propose an interesting deep reinforcement learning approach via CNNs to learn the rationales associated to target chemical properties. The paper has merit, but in its current form does not match the acceptance criteria for ICLR. In particular, the main issue lies in the poor performance reached by the systems, both overall and in comparison with baseline methods, which at the moment hardly justifies the effort required in setting up the DL framework. Moreover, the fact that test performances are sometimes (much) better than training results are quite suspicious in methodological terms. Finally, the experimental part is quite limited (two small datasets), making it hard to evaluate the scalability (in all sense) of the proposed solution to much larger data.