Venue: ICLR 2022 Conference

Reviews:

final_score: rating: 8: accept, good paper
review_content: summary_of_the_paper: This paper introduces a method for jointly sampling structure and sequence of antibody loops in an iterative fashion that constrains structure generation less. 
main_review: This paper provides an interesting study of antibody loop generation with both novel methodology and extensive empirical evaluation. This combination of strengths makes it an excellent paper that should be of wide interest.

Strengths:
- The design of each component follows naturally from previous work (such as Ingraham et al 2019). This helps isolate out key methodological contributions, for example no teacher forcing in structure, in an easy-to-interpret way.
- The coarsening procedure is of wider applicability in protein modeling beyond sampling CDR loops and an ablation study demonstrates its value empirically for this work.
- Baselines chosen highlight key interactions in RefineGNN components and show that the model appears to effectively leverage the added complexity.
- Evaluations are thorough and span a number of datasets, one of the biggest strengths of this paper.

Weaknesses:
- The use of a predictor to evaluate neutralization is justified based on very recent work, and it is unclear that this practice is in line with broader norms in the antibody engineering community.

summary_of_the_review: This paper provides an interesting study of antibody loop generation with both novel methodology and extensive empirical evaluation. This combination of strengths makes it an excellent paper that should be of wide interest.
review_title: null

final_score: rating: 8: accept, good paper
review_content: summary_of_the_paper: This paper proposes a joint generative model (co-designing the sequence and the structure at the same time) for the CDRs of antibodies, with the goal to enhance binding specificity or neutralization capabilities. 

The proposed method and two existing baselines are evaluated on 1) perplexity on hold-out set; 2) perplexity and sequence recovery on known antigen-antibody complexes; and 3) redesign CDR-H3 of existing antibodies for coronavirus neutralization as measured by a given neutralization predictor. RefineGNN, the proposed method, shows improved performance in all three tasks.
main_review: This paper studies the important problem of computationally designing antibody CDRs. The joint modeling approach of structures and sequences is novel and interesting. The generation method is flexible and the authors also adapt it for conditional generation (conditioned on the rest of the antibody and on given properties of interest with the predictive model already available).

The empirical results are convincing. Both perplexity and sequence recovery are standard metrics in protein design, and the proposed method showed improvements in both perplexity and sequence recovery on two separate data sets. The two baselines used for comparison are well described and meaningful.

Request for clarification: In section 4.2, it is not immediately clear whether the antigen is also included in the conditioning or only the antibody itself.

It would be appreciated if the authors can add more discussion around when this proposed method applies in practice. For example, the method requires already knowing the frame of the antibody. When is a fixed frame variable CDR-H3 design a reasonable assumption? Or, when can we realistically expect to have accurate predictors for properties of interest?

While the model is trained on unbound antibody structures, the second evaluation task is conditioned on the antibody structures in bound state within a complex (if I’m not mistaken), will there be a mismatch between the bound and unbound structures? Clarification and discussion around this point would also be appreciated.

summary_of_the_review: Interesting approach to an important problem. Convincing empirical results and baselines. The approach might be limited to specific use cases in practice depending on the availability of a predictive model for the properties of interest and the knowledge of the antibody frame.
review_title: null

final_score: rating: 8: accept, good paper
review_content: summary_of_the_paper: The paper proposed a deep generative model named iterative refinement graph neural network to generate antibody CDR for Y-shaped antibodies. Specifically, it sequentially generates the CDR residue sequence and refines the global structure iteratively. Empirical results show superior performance compared with baselines. 
main_review: Strengths:
- The paper is well-written and easy to follow. 
- The proposed refinement method has high novelty and outperform state-of-the-art baseline methods. 

Weakness:
- The experimental evaluation may be problematic, it is not convincing to use machine learning methods to predict the neutralization ability based on CDR H3. 
summary_of_the_review: The proposed method is novel and the paper is well written, validated by thorough empirical studies.
review_title: null

Meta-review: This paper proposes use of a novel generative modelling approach, over both sequences and structure of proteins, to co-design the CDR region of antibodies so achieve good binding/neutralization. The reviewers are in agreement that the problem is one of importance, and that the technical and empirical contributions are strong. There are concerns over the relevance of evaluating the method by using a predictive model as ground truth. Still, the overall contributions remain.
Decision: Accept (Spotlight)