Venue: ICLR 2022 Track Blog

Reviews:

final_score: rating: 8: Top 50% of accepted papers, clear accept
review_content: The introduction of the blog post is nice, it clearly explains the problem, requiring no prior knowledge, in an intuitive fashion that you wouldn't see in a paper.

The blog post also does a good job in separating the key ingredients such as graph networks, normalising flows, etc. and explaining each independently, while building up the main model. The math is kept to the minimum and most intuitive equations and does a good job explaining them.

The future work section is interesting and raises good questions about possible alternative approaches. 
review_title: Nice blog post

final_score: rating: 8: Top 50% of accepted papers, clear accept
review_content: The blog post is a valuable extension to the paper "Learning Neural Generative Dynamics for Molecular Conformation Generation".

The introduction section clearly introduces the background of the conformation generation problem and its pratical value (such as docking), which is less covered in the cited paper. Also, by citing Levinthal's Paradox, the blog post illustrates more concretely and intuitively the challenge in generating molecular conformation. Finally, the intro section summarizes two important points which explain why deep generative models are suitable and the design principle of generative models for the problem. Overall, the introduction section provides valuable insight into the problem.

The following sections clearly present the method in a more accessible way. Especially for readers that are not familiar with deep generative models and normalizing flows.

The "Future Work" section is another highlight of the blog. It points out serveral novel and exciting future directions such as using large-scale pretrained models. They are worth studying and they also one of the topics that attract much attention recently. The author also identifies an issue on the benchmark used in the cited papers --- the test datasets are not obtained from experiments. Thus, it is also important to derive another test set from experiment databases such as PDB and CSD, in order to better evaluate the performance of the models.
review_title: Review

final_score: rating: 8: Top 50% of accepted papers, clear accept
review_content: The blog post is well written and mostly summarizes the cited paper. It does a good job of focussing on the key ingredients (which is not obvious given the complexity of the pipeline) and points to other resources (like a tutorial on normalizing flows).  The figures are also value added on top of the original paper, and bring some pedagogical insights.

There is also a nice future work section that seems to propose completely new follow-ups, and that was a pleasant surprise.

In that section, the author also asks questions and make suggestions regarding to extension of the paper to protein 3D conformations. Is there a way to take advantage of evolutionary information (like classical methods do)? Could we control the expected trade-off between the stability (low energy) of the sampled configurations and their diversity?  Could we measure coverage of the modes of the distribution (the low energy conformations)?

review_title: interesting proposals for future work

Decision: Accept