Venue: ICLR 2023 TinyPapers

Reviews:

final_score: rating: Clear, Correct, and Reproducible (CCR): a submission which meets the reviewing criteria
review_content: summary_of_contributions: The paper focuses on predicting drug properties, specifically solubility and drug subcategories, using sequence-based embedding methods such as SMILES strings. The study investigates five types of embeddings: Morgan fingerprint, MACCS fingerprint, k-mers, weighted k-mers, and minimizer-based spectrum.
strengths_and_weaknesses: ## Strengths

- The study investigates five different types of embeddings and evaluates their performance using multiple evaluation metrics, providing a comprehensive comparison of the different methods.

-  The study evaluates both classification and regression tasks, which are both important in drug discovery.

## Weaknesses

- The authors conducted their study in just on dataset of 6951 SMILES strings. An analysis to more and larger datasets would be beneficial.
The findings of the study could be limited to the specific characteristics of the dataset and may not be generalizable to other datasets or real-world drug molecules. Therefore, analyzing more and larger datasets could help to validate the findings and improve the generalizability of the results.
suggested_changes: See Weaknesses above
review_title: null

final_score: rating: Great Start (GS): a submission which meets some of the reviewing criteria but has room for improvement
review_content: summary_of_contributions: This paper investigates the effectiveness of different embedding methods on predicting (i) solubility ALOGPS and (ii) drug subcategories. The investigated embedding methods are Morgan fingerprint, MACCS fingerprint, k-mers, minimizer-based spectrum and an additional weighted version of k-mers that employs inverse document frequency.
strengths_and_weaknesses: **Strength**

Adopting an alternate embedding method from the bioinformatics domain to generate representations for SMILES strings is interesting and could be promising.

**Weaknesses**
 
The main contribution of the paper is not very obvious as the reader could be confused between the study of different embedding techniques and which technique is actually contributed by the authors

The experiments are not extensive enough. The authors should consider some benchmarking tasks that are popular for molecule property prediction classification and regression. Examples can be found in the paper: https://chemrxiv.org/engage/chemrxiv/article-details/628e5b4d5d948517f5ce6d72

There is no link to the code mentioned in the reproducibility section. The authors should consider using [anonymous GitHub](https://anonymous.4open.science/) for this purpose.
suggested_changes: Adding popular benchmark results would significantly improve the impact of the research which could be helpful in many other works relating to molecule property prediction. It will be great to see the performance of the embeddings on more recent models such as graph convolutional networks.
review_title: null

Decision: Invite to archive