Venue: NeurIPS 2022 Conference

Reviews:

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: Representation learning for chemistry and molecules in specific is an emerging area in deep learning. In this work, the authors propose a framework (and then a model) to generate low energy 3d conformations of molecules. The work proposes to segment a given molecule into  fragments (with limited internal flexibility), and then proposes to learn a joint probability distribution over the fragments using a Markov Random Field. As parameter estimation for the MRF is intractable, the authors provide an approximate learning strategy leveraging GNNs and Gibbs sampling to achieve their objective.
strengths_and_weaknesses: Strengths:
1. The idea to segment a molecule into fragments, and then use the framework to generate low energy. states is interesting.
2. The observation that even randomly sampled $\mathcal{X}_f, \mathcal{X}_d$ in section 4.3 can outperform some previous works is insightful

Weaknesses:
1. In my opinion, the writing is not precise and hard to understand. For e.g. a) in section 3.2, it is hard to decipher what is a sufficient number of 3D fragments? b) in sec 3.3 - how is the oracle configuration obtained?  c) in sec 3.5 the sampling procedure, it is not clear what the samples are? the fragments? 3d positions? and how detailed balance is satisfied d) Why are the side chains removed in 3.2, etc. Infact, i felt it was incredibly hard to read the entire of section 3. 
2. Evaluation performed on a single dataset. Also the scores from other works appear to be incorrect (e.g. GeoMol scores are better than numbers presented here, etc) in addition to RMCF not outperforming other benchmarks.
3. The idea to fragment and compute graph representations have already been performed (e.g. especially energy based models [1]), which have not been cited. The idea to use GNNs to parametrize them has also been done in [1]

References:
1. Cotta, Leonardo, et al. "Unsupervised joint k-node graph representations with compositional energy-based models." Advances in Neural Information Processing Systems 33 (2020): 17536-17547.



Post Rebuttal Updates:

Thank you very much for the rebuttal and answering my questions on the dataset (QM9 has smaller molecules, so impacts performance), backprop and other questions I had raised. I have also read through the other reviews and the authors response. In light of all this, I am updating my score.
questions: 1. Why not perform evaluation on Geom-QM9? Another dataset considered in the baseline models.
2. For me, it is not clear how you back propagate through the RMCF procedure. Please add detail
3. Similarly, please elaborate on the graph dynamic programming and also Eq (3). Specifically What does the GNN output? I was under the opinion it is the edge representations but then Eq(3) has a set on the left.
4. Also please address the concerns in the weaknesses section.
limitations: The authors have discussed the limitations of their work. For example:
1. Have discussed intractability of parameter estimation in Sec 3.4, and ways to counteract it.
2. In Sec 4.3, have discussed a few limitations of the segmentation framework

I do not see any potential negative societal impact of 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 authors propose a new method for 3D conformation generation. The method contains three steps:

(1) the authors use dynamic programming to segment a molecule into several fragments, where the objective is in Eqn.(2);

(2) Convert the generation problem into a classification problem: Given the fragments, predict the conformation and dihedral angles;

(3) Assemble the intermediate results to the conformation of the molecule.

The method achieves promising results several baselines.
strengths_and_weaknesses: ## Originality:

The general idea is novel, and make sense. The idea is also similar to how to search the docking pose in molecular docking. The authors should comment on this.

## Significance:

1. The paper miss a reference [A1]. [A1] achieves better results on COV-R, and the mean of MAT-R.

[A1] https://arxiv.org/pdf/2202.01356.pdf


## Clarify: 

The paper is hard to follow. This is my biggest concern of this paper.

1. A question to be confirmed: Given a fragment $f$, it should has its own V(f). The way to obtain V(f) is that: first, find the conformations of fragment $f$ in all molecules, and clustering them using some algorithm to obtain |V(f)| clusters. The centroid of each cluster is an element in V(f). Am I right? Do you take roto-translation invariant of the fragment conformations into consideration?


2. Line 134: "Then, we infer a configuration set $\mathcal{X}$ including the fragment state and the dihedral state": What do you mean by state? This is very important and should be explained clearly.

3. It is better to use some examples and figures to show what is $\mathcal{X}_f$, $\mathcal{X}_d$ and $\mathcal{V}_f$. Currently, it is hard for me to get it.

4. Eqn.(2): I did not get a clear solution about how to solve it.
questions: 1. What is the best choice of $|\mathcal{V}(f)|$? Any ablation study?

2. I'd like to know more about the fragments you obtained. How many atoms of each atom (average pm std)? If the conformation of a fragment does not lie in a plain, how to determine the dihedral angles?

limitations: Yes
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: This paper proposes a novel fragment-based model for molecular conformation generation. The authors develop a data-driven molecular segmentation algorithm to break each molecule into several fragments. The model learns the joint probability distribution of fragment configurations and dihedral angles between fragments. The proposed method outperforms existing baselines with a clear margin.
strengths_and_weaknesses: Strengths:
- The proposed fragment-based model for molecular conformation generation is novel.
- The paper is well-written and easy to follow.
- The proposed method shows great empirical performance.

Weaknesses:
- I have some concerns about the segmentation algorithms. See details in Questions.
questions: Questions:

- If I understand it correctly, the BRICS segmentation will give a unique segmentation for each molecule. It seems only clustering is needed after that, but where the "argmin P"  in Equation (2) comes from? Also, how do you determine the number of clustering centers?
- The segmentation algorithm returns a vocabulary of 9k 2D fragments and 30k 3D fragments. How is the frequency of each fragment? Since the number of modes is still very large, I'm curious about whether the model will have some difficulties in learning the long tail modes.
- Is it hard to train the model without discretizing the continuous angle values into bins?

Missing reference:
Besides EGNN and GeoDiff, [1] is also an equivariant network which directly learns 3D coordinates of atoms.

[1] Guan, Jiaqi, Wesley Wei Qian, Wei-Ying Ma, Jianzhu Ma, and Jian Peng. "Energy-Inspired Molecular Conformation Optimization." In International Conference on Learning Representations. 2021.
limitations: The authors didn't discuss limitations. There is not negative social impact.
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: This work provides a way to generate small molecule conformations from their molecular graph. The key idea is to find an appropriate fragmentation of a set of small molecules and to learn a Markov Random Field for the joint probability distribution of small molecular fragments. The paper demonstrates the model on the GEOM-Drugs dataset and compares to previously published baselines.

strengths_and_weaknesses: 
The key idea behind this work is sound and original. The presentation occasionally leaves some open questions (see below).  Although there is some code that allows for the reproducibility of this work and would add to the clarity of this method, I haven't verified if the code actually works as advertised. The problem that this work addresses is significant enough so it warrants some additional consideration in my opinion, despite the possible problems with this first draft, some of which I highlight below.

questions: 
The presentation of the segmentation algorithm is unclear so I'll focus most of my questions on that topic.  In particular: 1) what is the clustering algorithm used and what is the way to decide the initial number of 3D clusters?  2) What aspect of equation (2) is optimized, i.e. how is the set of molecules re-fragmented (or is it not?) to obtain an optimal solution.  I did not spend much time, but from a quick search of the codebase, I could not find what part of the provided code performs this segmentation optimization.  3) The language in the paper makes me concerned that the authors might have accidentally used the full molecule set (including the validation split) during the construction of the 3D fragments, therefore possibly having some minor leakage.  If that is not the case, please fully clarify the description of this process and point to the relevant code.  4) How does the code deal with out-of-sample fragments?  If the fragmentation of a validation molecule leads to a piece that is not existing in the training set, what happens then?! 5) When there are multiple options, how are the atoms selected that perform the connecting pieces between fragments (in order to reduce the problem to only dihedrals) and does the selection influence the results at all? 6) would it be possible to avoid binning the angles and what if any would be the disadvantages of such an approach?  7) Finally, how do the authors address possible steric clashes from the generated conformations, or does the markov field somehow avoid from ever proposing such a clash even in reasonably complicated small molecules, e.g. a large macrocycle?


Although the following would not be necessary for acceptance of this work, it would be nice if the authors showed the behavior of their model for the dataset in Table 4 as a function of the cutoff parameter delta, if they clarified how they selected the examples for Figure 5, and if they could include a brief comparison with the torsional-diffusion model, whose preprint is here:

https://arxiv.org/abs/2206.01729


limitations: 
The authors did not adequately address any limitations of this work. Perhaps the authors could address how this method would scale to macromolecules, to molecules with fragments outside the training set, or to macrocycles.  Would it be possible to predict the structure of macrocycles if those were outside of a training set?
review_title: null

Meta-review: metareview: This work introduces fragment-based data-driven model for molecular conformation generation. The method has a segmentation that breaks each molecule into several fragments and then learns a joint probability distribution over the fragment configurations and dihedral angles between fragments with a Markov Random Field, which enables the method to sample from different low-energy regions of a conformation space.

The idea is novel with convincing results. There were some initial questions about clarity and further discussions that were necessary but these have been mostly addressed during rebuttal, and reviewers have increased their scores once those were cleared out. Overall this is a solid paper and we recommend acceptance.
confidence: Certain
Decision: Accept