Venue: NeurIPS 2022 Conference

Reviews:

final_score: rating: 5: Borderline accept: Technically solid paper where reasons to accept outweigh reasons to reject, e.g., limited evaluation. Please use sparingly.
review_content: summary: This paper describes a method called CLSVAE that generates combinatorial libraries of small molecules using a continuous underlying representation. The authors demonstrate the forward and reverse query in such a library.

strengths_and_weaknesses: 
This work is original, is of reasonable quality, of mixed clarity, and of unclear significance.  
The high level idea behind the paper appears reasonable, although the code is currently not provided so it is hard to judge the reproducibility or generalizability of this work in practice (the authors say that they would provide code if this work was accepted).  The description of the method is clear and the math makes sense to me.  The main weakness of this work, however, is that it does not appropriately motivate the rationale behind it so I cannot judge if this is actually significant in practice or not.

In the presence of the chemical reactions, the forward problem of generating a molecule from the given synthons is actually trivially accomplished with the current methods. The inverse problem of finding the synthons from the molecule is somewhat interesting, though it does not appear a priori difficult to solve with existing methods (though there might be more trial and error involved than with a differentiable code). Perhaps the main point of this work then is that there is some simplification in the search for similar molecules, e.g. during the phase of SAR by catalogue, though it is hard to imagine that such the exhaustive search would be difficult if one limited the library to only those with common disynthons to the full molecule and it is not clear how accurate the search in this latent space actually is. Although it is good to see comparisons with prior generic generators, obviously the main contribution of such molecular generators is that they can in principle access a space much richer than the limited space of enumerable combinatorial libraries (even if the latter soon will exceed 10^12 molecules in size).  

questions: 
Can the authors please make a compelling case for this model by demonstrating a usecase that would be onerous with a different toolchain and now becomes accessible?  In the absence of such a demonstration I don't know how to judge the significance of this work and thus feel compelled to reject it from the current conference.  

How does the reverse search for synthons work when there are degenerate ways to synthesize the same molecule?  Could the authors provide a subset of statistical results for only such molecules?

[Edit: I've updated my score in part due to the additional effort by the authors.  The degeneracies can be significantly more common in large libraries than what the authors suggest.  Also, there are additional ways to find analogues rapidly, and even faster than the current speed the authors demonstrate.  However, the overall ideas are useful and might be worthy of publication.]
limitations: The authors address some of the possible technical limitations of this work.  There are no negative societal implications of this work.
review_title: null

final_score: rating: 7: Accept: Technically solid paper, with high impact on at least one sub-area, or moderate-to-high impact on more than one areas, with good-to-excellent evaluation, resources, reproducibility, and no unaddressed ethical considerations.
review_content: summary: Generative modelling of chemicals relies on generated molecules being valid and synthesizable. Often however, due to the size of combinatorial synthesis libraries, it is enough to look for hits in a library (also to forgo synthesizing a molecule oneself). Such libraries can often be (relatively) compactly described as the products of a set of starting reagents and reactions. The authors of this paper build a generative model of molecules in a large combinatorial synthesis library that embeds all molecules in a latent space.

The authors demonstrate that examples of graph generative models cannot fit the distribution of synthesizable sequences, with much of their support landing on molecules outside of the library. They show their method has a large support, generating many unique molecules, and supports a large part of the library, having a large likelihood on much of the molecules, all of this compared to examples of graph generative models.

The authors conclude by demonstrating that the latent space is continuous with respect to Tanimoto similarity, presumably making it good for semi-supervised learning.
strengths_and_weaknesses: Strengths:
1) The authors introduce a generative model that is guaranteed to output molecules in a given library.
2) The authors demonstrate that embeddings of molecules in the library are continuous and so the generative model can be used for optimization or semi-supervised molecular design.
3) The authors demonstrate that state of the art methods cannot be used to reliably generate within a library, motivating their model.
The method points to a novel approach to molecular design that is likely to be very useful to practitioners.

Weaknesses:
1) It is not checked that deep language models, which now are able to reach >90% synthesizability, can fit combinatorial libraries.
2) The primary weakness of the paper is the limited check that the latent space may be used for learning chemical features: comparing the performance of classifiers or optimization methods in the latent space of this models and the example graph-generative, and some language models would greatly strengthen the paper.
questions: See weaknesses above.
limitations: The main limitation of the method is that it relies on a compact description of the library in terms of reactions and reagents. Expanding this method to the set of all commercially available reagents may be a challenge.
As well, should a group of reagents appear multiple steps in a reaction, the architecture of the model makes it likely that the same reagent will be selected for both steps. This hinders the ability of the model to fit some sets of molecules, such as polymers.
The authors acknowledge the above limitations in their work.
review_title: null

final_score: rating: 6: Weak Accept: Technically solid, moderate-to-high impact paper, with no major concerns with respect to evaluation, resources, reproducibility, ethical considerations.
review_content: summary: The present paper is concerned about learning a neural representation of a combinatorial synthesis library, whose size is now exploding and which is difficult to use for virtual screening due to its massive size.

The idea is to construct a generative model of a molecule that receives a continuous vector and outputs a molecule that is constructed by using the combinatorial synthesis library.
In particular, the proposed model named CSLVAE consists of a library encoder, a molecular encoder, and a molecular decoder.
The library encoder is used to encode the combinatorial synthesis library into keys of reactions and synthons, which are used to retrieve them during generation. The molecular encoder receives a molecular graph and outputs a continuous feature representation of it. The molecular decoder receives a continuous feature vector and constructs a molecule by first retrieving a reaction using the query constructed from the feature vector and the keys defined by the library encoder, and then, retrieving synthons in a similar way.

The authors empirically compare CSLVAE with baseline methods to show that CSLVAE can generate molecules in the library, whereas the baseline methods cannot. The authors also conduct qualitative analyses to discuss the benefit of CSLVAE.
strengths_and_weaknesses: # Strengths
- Originality: The problem setting to construct a neural database is interesting. While most of the researchers in the community would consider their generative model of molecules as a neural database for virtual screening, the idea of approximating the existing database is not very common.
- Quality: The model proposed is sound.

# Weaknesses
- Clarity: One weakness is the presentation issue. I had trouble reading the preliminaries section (Section 3.1), because i) many of the objects ($T$, $R$, $S$, $X$, $\mathcal{P}(\cdot)$, etc.) are not explicitly defined, and ii) technical concepts such as R-group, group, _multi-component_ reaction, and synthon chain are not defined in the paper (and I could not find "R-group" in reference [42]). Since the main audience of the NeurIPS conference is not fully aware of the terminology in drug discovery, it would be very helpful (at least to me!) to explain these concepts with some examples.
- Quality: The empirical studies are limited. Since CSLVAE will be used as a substitute for the original CSL, it is necessary to demonstrate its capability by applying CSLVAE to some realistic tasks to see the computational efficiency and potential performance degradation as compared to the baseline method using the original CSL. Without such an application, the neural database concept suggested in the paper cannot be appreciated.
questions: 1. I would appreciate it if the authors could provide more detailed explanations in the preliminaries section.
limitations: The authors discussed several limitations of the proposed method in terms of computational efficiency, which are reasonable.
review_title: null

Meta-review: metareview: 
There are three reviews, all of which place this paper above the threshold.  All reviewers agreed on several strong points, such as originality, convincing motivation behind the general approach and clarity of presentation. At the same time, the reviewers also raised some critical comments about the practical utility of the model (and a lack of experiments proving this utility). This last point of criticism, however, was addressed reasonably well in the rebuttal. Therefore, I think that the positive aspects dominate, and I recommend to accept this paper. 
confidence: Certain
Decision: Accept