Paper: ($\texttt{PEEP}$) $\textbf{P}$redicting $\textbf{E}$nzym$\textbf{e}$ $\textbf{P}$romiscuity with its Molecule Mate – an Attentive Metric Learning Solution (ICLR-2024)

Reviewer comment (Novelty):
The technique contribution of this work is not very high since it mainly use MoCo in the protein domain. It also mixes up some biological efforts to improve performance.

Author response:
**Weakness 1: Technique Contributions?**
We respectfully disagree. While we do utilize MoCo in the protein domain, our key innovations lie in integrating biological insights into various facets of our approach. These aspects are not explicitly considered in previous machine learning algorithms for protein functional annotation. In our framework, we have leveraged the attention mechanism to capture residue importance, introduced a hierarchical loss term customized for functional annotations, and integrated ligand embeddings to enrich the representations. These elements collectively contribute to our framework's efficacy. Our detailed experimental results (summarized below) underscore that these incorporations, rather than using MoCo alone, are instrumental in achieving the performance enhancements we have observed.

| Method | Rec. | Prec. | F-1 |
| :-: | :-: | :-: | :-: |
| CLEAN | 0.187 | 0.261 | 0.204 |
| CLEAN + MoCo | 0.186 | 0.271 | 0.202 | 
| Ours | 0.217 | 0.339 | 0.241 |

Reviewer comment (Writing):
The paper might lack key explanations, such as references to EC in the second paragraph.

Author response:
Thank you for your valuable feedback. In the manuscript, we have included a paragraph explaining the EC number in Section 3.1. In response to your suggestion, we have also incorporated an introductory mention of the EC number in the second paragraph of the revised draft to provide additional context.

Reviewer comment (Novelty):
The differences between the CLEAN method and the proposed method need further discussion. I did not see a concrete motivation for CLEAN.

Author response:
The primary disparity between our approach and CLEAN lies in our incorporation of several techniques that account for biological priors across various dimensions: (1) at the input level, we fuse the representation of ligands to provide substrate-scope information; (2) at the model level, we adopt attention mechanism to learn functional residues; (3) at the objective level, we propose a metric learning objective to capture hierarchical nature of EC numbers. These enhancements collectively set our method apart from CLEAN and underline the motivations behind our approach.