Using Deep Siamese Neural Networks to Speed up Natural Products Research
Natural products (NPs, compounds derived from plants and animals) are an important source of novel disease treatments. A bottleneck in the search for new NPs is structure determination. One method is to use 2D Nuclear Magnetic Resonance (NMR) imaging, which indicates bonds between nuclei in the compound, and hence is the "fingerprint" of the compound. Computing a similarity score between 2D NMR spectra for a novel compound and a compound whose structure is known helps determine the structure of the novel compound. Standard approaches to this problem do not appear to scale to larger databases of compounds. Here we use deep convolutional Siamese networks to map NMR spectra to a cluster space, where similarity is given by the distance in the space. This approach results in an AUC score that is more than four times better than an approach using Latent Dirichlet Allocation.
 The paper proposes a Siamese network model for mapping the space of natural compounds to a latent representation space; furthermore, it utilizes this representation to compute a similarity score between an already known compound and a newly discovered one. Some comments: - Comparison with LDA requires more details: have you used the same number of topics for both small and large datasets? How are you training it? - Page 4 the definition of micro-averaging is missing: “Micro-averaging for precision and recall is formally defined as: “ - Figure 3 does not show a well defined clusters of various compound families because it’s using same color for different families in some cases (according to the caption of Figure 3). I wonder if you can somehow show for which compound families the colors have been repeated or maybe show fewer compound families. The problem of finding similar compounds to a novel compound from NMR spectra is an interesting applied problem; however, technical novelty of the paper is not significant. Given the level of technical novelty, I believe the paper is more suitable for a more applied conference/journal in the fields of chemistry or bioengineering.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Using Deep Siamese Neural Networks to Speed up Natural Products Research
Natural products (NPs, compounds derived from plants and animals) are an important source of novel disease treatments. A bottleneck in the search for new NPs is structure determination. One method is to use 2D Nuclear Magnetic Resonance (NMR) imaging, which indicates bonds between nuclei in the compound, and hence is the "fingerprint" of the compound. Computing a similarity score between 2D NMR spectra for a novel compound and a compound whose structure is known helps determine the structure of the novel compound. Standard approaches to this problem do not appear to scale to larger databases of compounds. Here we use deep convolutional Siamese networks to map NMR spectra to a cluster space, where similarity is given by the distance in the space. This approach results in an AUC score that is more than four times better than an approach using Latent Dirichlet Allocation.
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>



 The paper proposes a Siamese network model for mapping the space of natural compounds to a latent representation space; furthermore, it utilizes this representation to compute a similarity score between an already known compound and a newly discovered one. Some comments: - Comparison with LDA requires more details: have you used the same number of topics for both small and large datasets? How are you training it? - Page 4 the definition of micro-averaging is missing: “Micro-averaging for precision and recall is formally defined as: “ - Figure 3 does not show a well defined clusters of various compound families because it’s using same color for different families in some cases (according to the caption of Figure 3). I wonder if you can somehow show for which compound families the colors have been repeated or maybe show fewer compound families. The problem of finding similar compounds to a novel compound from NMR spectra is an interesting applied problem; however, technical novelty of the paper is not significant. Given the level of technical novelty, I believe the paper is more suitable for a more applied conference/journal in the fields of chemistry or bioengineering.
assistant
 The paper proposes a Siamese network model for mapping the space of natural compounds to a latent representation space; furthermore, it utilizes this representation to compute a similarity score between an already known compound and a newly discovered one. Some comments: - Comparison with LDA requires more details: have you used the same number of topics for both small and large datasets? How are you training it? - Page 4 the definition of micro-averaging is missing: “Micro-averaging for precision and recall is formally defined as: “ - Figure 3 does not show a well defined clusters of various compound families because it’s using same color for different families in some cases (according to the caption of Figure 3). I wonder if you can somehow show for which compound families the colors have been repeated or maybe show fewer compound families. The problem of finding similar compounds to a novel compound from NMR spectra is an interesting applied problem; however, technical novelty of the paper is not significant. Given the level of technical novelty, I believe the paper is more suitable for a more applied conference/journal in the fields of chemistry or bioengineering.