reviews: 
Comments: In this paper, the authors propose GNNAS-Dock, a GNN-based algorithm selection model for molecular docking that combines the best-performing docking algorithms rather than relying on a single one. GNNAS-Dock consists of two submodels: the Accuracy model, which selects the docking method with the highest performance, and the Efficiency model, which prioritizes models with faster runtimes while maintaining suitable accuracy. GNNAS-Dock’s performance is compared with single-model approaches based on the percentage of ligands achieving RMSD < 2 Å and runtime efficiency.
Strengths: - The use of multiple models with GNN-based Algorithm Selection to choose the most accurate or efficient model makes GNNAS-Dock unique.
- The efficiency model demonstrates runtime improvements.
- The accuracy model shows improvement in mean RMSD.

**Originality:** GNNAS-Dock presents an innovative approach by creating a GNN-based AS model that incorporates multiple models, aiming to select them based on accuracy and efficiency.
Weaknesses: - The authors highlight in the introduction and related work sections that they rely solely on the RMSD metric, which is becoming outdated in molecular docking. RMSD does not guarantee physicochemical plausibility, so I recommend they consider the PoseBuster[1] study for alternative approaches.
- Although the primary aim of the study is to select the best algorithm, the results suggest that DiffDock alone remains accurate and efficient, calling into question the effectiveness of the algorithm selection model.
- The RMSD metric used in molecular docking and Structure-Based Drug Design (SBDD) does not always yield bioactively, physically, or chemically plausible structures, as shown by studies like Posebuster[1], PoseCheck[2], PoseBench[3], and CompassDock[4]. Unfortunately, these metrics were not included in the benchmark.
- Some models may have been trained on different time splits, and if the training dataset overlaps with the validation or test sets among the chosen models, it could lead to information leakage [5]. This potential issue is insufficiently discussed.
- Details on data preparation, time-splitting, and training are inadequately explained.

**Significance:** The study does not demonstrate enough effectiveness in terms of accuracy and efficiency, which limits its applicability for scalability.

**Quality:** The paper lacks sufficient evidence and relies primarily on empirical results, without any physicochemical validation as noted above. Additionally, it is not up-to-date with the current literature, especially in the benchmarking and validation sections.

**Clarity:** The paper contains numerous grammatical errors that need revision, and the equations and annotations do not align with scientific paper standards. I recommend the authors revise the text to clarify their methods, moving away from narrative elements.

**Reproducibility:** As the code is not shared, it is currently impossible to verify whether the reported results are reproducible.


### **References**

[1] Martin Buttenschoen, Garrett M Morris, and Charlotte M Deane. Posebusters: Ai-based docking methods fail to generate physically valid poses or generalise to novel sequences. Chemical Science, 15(9):3130–3139, 2024.

[2] Charles Harris, Kieran Didi, Arian R Jamasb, Chaitanya K Joshi, Simon V Mathis, Pietro Lio, and Tom Blundell. Benchmarking generated poses: How rational is structure-based drug design with generative models? arXiv preprint arXiv:2308.07413, 2023.

[3] Alex Morehead, Nabin Giri, Jian Liu, Jianlin Cheng. Deep Learning for Protein-Ligand Docking: Are We There Yet? arXiv preprint arXiv:2405.14108, 2024.

[4] Ahmet Sarigun, Vedran Franke, Bora Uyar, Altuna Akalin. CompassDock: Comprehensive Accurate Assessment Approach for Deep Learning-Based Molecular Docking in Inference and Fine-Tuning. arXiv:2406.06841, 2024.

[5] Janani Durairaj et al. PLINDER: The protein-ligand interactions dataset and evaluation resource. bioRxiv, 2024
Recommendation: 3
Reviewer confidence: 5
Soundness/correctness: 1
Clarity: 1