Kati8/ICLR_2023_2025 huggingface dataset:
authors: Hannes Stark
authors: Bowen Jing
authors: Tomas Geffner
authors: Jason Yim
authors: Tommi Jaakkola
authors: Arash Vahdat
authors: Karsten Kreis
reviews: 
Comments: Introduces ProtComposer, a generative model for proteins. ProtComposer seeks better control over the shape of generated proteins, as well as allow for greater novelty in the generation of proteins. Notably, the user has to choose between control or novelty, ProtComposer does not achieve both simultaneously. These tasks are accomplished by the introduction of 3D ellipsoid frames to guide the generation of proteins by the pre-existing Multiflow algorithm. 

Without a pre-existing metric the authors feel sufficiently quantifies compositionality, they introduce their own metric. It is shown that ProtComposer outperforms existing methods in this metric.
Strengths: It addresses an important and relevant area. The results are overall quite impressive as well. Additionally, showing the ability to work with handcrafted ellipsoid frames or the more easily scalable ML generated frames shows the practicality of ProtComposer.

I really like the point about using simple ML models for ellipsoid sampling as compared to NNs. I would like to see some stronger analytical or experimental justification of the claim though.
Weaknesses: Since a custom metric is introduced in this paper and then used as justification for the performance of the model, a section (either in the main paper or the appendix) justifying this metric would be nice. Showing comparisons to other pre-existing metrics, performance of many other algorithms under this metric, or stronger domain justification would strengthen the meaning of the results. I did not reject over this since the metric intuitively looks good, but further empirical or analytical support would be nice.

The formatting of the extra results in the appendices results in very odd page layouts, where some pages are blank, others have odd whitespace gaps, etc. Adjustment to make these pages more presentable would be nice.
Recommendation: 8
Reviewer confidence: 4
Soundness/correctness: 3
Clarity: 3
reviews: 
Comments: This paper presents ProtComposer, which is a fine-tuned model from MultiFlow to support conditional generation based on 3D ellipsoids, showing success at controllable design and achieving SOTA on the Pareto frontier of designability and diversity.
Strengths: The paper is well written with clean visualizations demonstrating methods and results. The concept of utilizing ellipsoids as conditions for protein generation is interesting and novel, providing a bridge between protein-level conditioning and atom-level conditioning. In addition, the authors propose an effective Invariant Cross Attention module for integrating ellipsoid conditioning and demonstrate success at achieving SOTA performance on the Pareto frontier of designability and diversity.
Weaknesses: I have no major concerns about this paper. However, it would be helpful if the authors could elaborate on the applicability of the ellipsoid-based conditioning approach on practical protein design tasks. How would it help with or facilitate the protein design process?
Recommendation: 8
Reviewer confidence: 4
Soundness/correctness: 4
Clarity: 4
reviews: 
Comments: This paper proposes a framework to generate protein structures by conditioning on layouts specified through 3D ellipsoids. The conditions include location, size, orientation and secondary structure. These conditions are injected to flow-base protein generative models via proposed cross-attention and update modules. It shows greatly improved controllability and designability over baselines.
Strengths: This paper is well motivated, formulated, written, and evaluated. 

1. The injection of ellipsoid information is achieved through cross attention. This allows the ellipsoids to be unordered set. i.e. user doesn’t have to specify the order or ellipsoids; model also decides the order of ellipsoids. 
2. The formulation of ellipsoid token is effective and is easy to be extended to different conditions other than secondary structure. (such as hydrophobicity)
3. Thorough analysis of ellipsoid consistency, including both geometric and probabilistic metrics.
4. Fig4: The comparison on designability/diversity with baseline methods are done as comparing Pareto frontiers with varying sampling temperatures/guidances. This clearly shows the tradeoff between the methods and the performance improvement from baselines.  I found this analysis insightful and believe other papers arguing increased performance could benefit from a similar evaluation scheme.
5. The practical use-case of the method is shown in Section4.3 flexible conditioning.
6. The controllability is greatly improved from Chroma (Table1). Accuracy and coverage is very impressive.
Weaknesses: How is sequence design performance? What I understand is that the designability is solely based on the generated structure (i.e. generated sequence is discarded). Can you also present co-design designability value as in MultiFlow paper?

Other than that, I did not find any major weaknesses from the paper. However, the ablation study can be improved. Most of the model ablations are based on guidance strength, but I am also curious about ablation study on (i) ellipsoid segementation cutoff (5A currently), (ii) allow residue token to update ellipsoid token or not. For (ii), explaining the reasoning behind the design choice may suffice.
Recommendation: 8
Reviewer confidence: 3
Soundness/correctness: 4
Clarity: 4