Kati8/ICLR_2023_2025 huggingface dataset:
authors: Ngoc Trinh Hung NGUYEN
authors: Milo Roucairol
authors: Tristan Cazenave
reviews: 
Comments: In this work, the author addresses template imbalance in inverse synthesis by introducing a hierarchical classification approach. Besides, they use k-means clustering to split large template classes into subclasses and Implement proxy anchor loss for classification. Moreover, subclasses were mapped back to the original template categories.
Strengths: The research effectively addresses a practical challenge in the field - the imbalance of template distributions in datasets. The approach demonstrates measurable improvements on the USPTO full dataset, offering a practical solution to reduce intra-class variance. The methodology shows progression in handling imbalanced data, with a clear focus on improving classification accuracy through hierarchical structuring.
Weaknesses: Despite its contributions, the paper suffers from several limitations. The methodology presentation lacks clarity and precision, requiring interpretation to understand the core concepts. While improvements are shown, the achieved 50% accuracy falls behind the current state-of-the-art performance. The paper also omits comparisons with recent superior methods, raising questions about its relative contribution to the field. Additionally, the overall writing style lacks precision and clarity, making it challenging to fully grasp the technical details without considerable effort.
Recommendation: 5
Reviewer confidence: 5
Soundness/correctness: 2
Clarity: 2
reviews: 
Comments: Retrosynthesis is a central challenge in chemistry, crucial for effectively leveraging newly discovered and complex molecules. In this article, the authors present a framework that combines proxy anchor loss with a depth metric Transformer to improve retrosynthesis accuracy in template-based methods, framing the inverse synthesis problem as a classification task. Although the results demonstrate some improvement, the method lacks novelty and does not achieve optimal accuracy.
Strengths: None
Weaknesses: 1. Retrosynthesis accuracy is low: Compared with state-of-the-art models such as LocalRetro, the retrosynthesis accuracy is low, performing poorly across multiple datasets.
2. Lacks comparative experiments: There is a lack of comparative experiments to demonstrate the improvement achieved by the applied method, making it difficult to see the advantages of this approach.
3. Limited diversity and practicality: The model is restricted to template-based methods and cannot be extended to a broader chemical space.
4. Inconsistent writing format: The manuscript's format presents issues,  such as the abstract, which lacks clear segmentation into distinct sections. This impacts the clarity and structured presentation of key information, making it challenging for readers to discern the main contributions and findings.
Recommendation: 1
Reviewer confidence: 5
Soundness/correctness: 1
Clarity: 1
reviews: 
Comments: The article explores using Deep Metric Learning and Transformer architecture for predicting chemical compound retrosynthesis in SMILES format. It shows that combining Attention mechanisms with Proxy Anchor Loss improves classification by capturing local and global contexts, achieving promising accuracy on the USPTO-FULL dataset without requiring prior chemical knowledge.
Strengths: The use of Attention mechanisms and Proxy Anchor Loss enhances the performance of classification tasks by capturing both local and global contexts.
Weaknesses: The retrosynthesis accuracy is relatively low compared to state-of-the-art models.
The model is restricted to template-based approaches, which hinders its applicability and scalability across a broader spectrum of chemical environments.
Recommendation: 1
Reviewer confidence: 4
Soundness/correctness: 1
Clarity: 1