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: This paper proposes a Trigonometry-Aware Neural Network (TankBind) for drug-protein binding site prediction. In the proposed method,  the protein is first segmented into several functional blocks by an out-of-shelf model. Then, a trigonometry module inspired by Evoformer of AlphaFold2 is applied to update the pairwise embedding between protein and ligand. The contrastive loss with local region negative sampling is applied to jointly optimize the binding position and affinity. The model could achieve state-of-the-art performance in both binding position prediction and binding affinity prediction tasks.
strengths_and_weaknesses: Strengths:
- The proposed method is novel, especially combining different designs together: breaking proteins into functional blocks, applying trigonometry module to update distance maps, performing affinity and binding poses joint training with contrastive losses.
- The paper is well-written and easy to follow.
- The proposed method shows superior empirical performance over existing baselines.

Generally, I think it's a very good paper and didn't see any major weakness. Only some minor questions left,  see details in Questions.
questions: - Is the ligand conformer fixed after the 3D initialization?
- How is the margin value epsilon determined in Equation 4? 
- In the binding affinity prediction experiment, is the same loss function (Eq. 4) applied? Is the binding site given as known information? If so, there is no need to apply the contrastive loss.
- In the docking experiments, are methods listed in Table 2 all deterministic? If not, have you run these methods multiple times to make results more reliable?
limitations: The authors discuss several future work directions in the conclusion section. There is not negative social impact.
review_title: null

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: Based on Geometric Vector Perceptron (GVP) for protein modelling and  Graph Isomorphism Network (GIN) for drug processing, the paper proposes a method for drug-protein binding structure prediction.  The main idea is to build a trigonometry constraint and explicitly attend to all possible binding sites for each protein by segmenting the whole protein into functional blocks. 
strengths_and_weaknesses: 1. The paper is well written. Readers can learn a lot about drug-protein interaction from the paper.

2. The method is mostly technically sound.  

3. Comprehensive experiments show the effectiveness of the proposed method. 
questions: 1. Although I recognize that the paper is mostly technically sound, such as using the sum of local protein and protein information to reflect the interaction, I feel some details are not well motivated. Specifically, according to Eq. (1) and Eq. (2), the method employs many gates, whose motivation is not very clear. Moreover, it could be better to visualize the gates to show some insights. 


2. The framework includes a  self-attention module to modulate the interaction between a protein node and all compound nodes by taking the whole interaction between this protein node and all compound nodes into consideration.  As a  self-attention module, it could be better to visualize the self-attention map via a few examples to verify the motivation. 


3.  Trigonometry is the main contribution or novelty and is similar to or inspired by the Evoformer module used in AlphaFold2. The difference should be highlighted.  
limitations: NA.
review_title: null

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: In this manuscript, the authors propose Trigonometry-Aware Neural networKs for binding structure prediction, TANKBind, that builds trigonometry constraint as a vigorous inductive bias into  the model and explicitly attends to all possible binding sites for each protein by  segmenting the whole protein into functional blocks.  The algorithm is trained by minimizing a novel contrastive loss with local region negative sampling. The model is able to predict both the binding poses and binding affinity between protein targets and small molecules.
strengths_and_weaknesses: Strengths: So far as I know, this is the first algorithm which adopts the geometric consistency to the binding of proteins and small molecules. Another contribution is the loss function which treats the non-binding sites on the same protein as the negative training data. 

Weakness: First, I want to emphasize that geometric consistency (trigonometry) is first proposed by AlphaFold2. The embedding z_ij for a pair of amino acids i,j is updated by another two embeddings z_ik and z_kj to satisfy the triangle constraints. Tankbind changes the transformer of AlphaFold2 to another gating function. In theory, it is easy to modify the StructureModule of AlphaFold2 to also fit this task. For me, this decreases the novelty of this work. Second, my major concern is that it is not clear what is the impact of P2rank which predicts the functional site of proteins. VINA-based methods and EquiBind both identify the binding sites by themselves. If AUTODOCK predicts a wrong binding site, it will yield a super low score. However, what if the authors first run the P2rank to predict the binding sites and then feed the predicted binding sites to AUTODOCK and EquiBind? The ablation study also shows that P2rank is the most important component and removing the P2rank will decrease the performance. 

questions: Questions:
1. What is the overlap between the training data of P2rank and Tankbind? It is necessary to check whether there is information leak for training and test datasets.
2. I did not find the definition of ‘blind’ 
3. It might take biologists decades to verify a new drug target. A more practical experiment is to show the generalizability to the new compounds instead of new protein targets.
4. It seems that the predicted protien-ligand complex is not feasible even using the trigonometry update function. That is why the authors need to use Eq 5 and 6. Please add discussion that why this component is still necessary when trigonometry update function is applied.
limitations: No obvious limitations and potential negative societal impact
review_title: null

Meta-review: metareview: This is a bordeline paper.

All three reviewers liked the paper and appreciated the feedback from the authors. The numerical score are 7, 5 and 5. The two 5s appear a bit on the low side given the comments.

The paper tackles the difficult problem of protein-ligand docking with a geometrical GNN approach inspired in part by AlphaFold. This is an important problem and the paper presents a novel approach in a clear and convincing way. Acceptance is therefore recommended. 
confidence: Less certain
Decision: Accept