From Molecules to Mixtures: Learning Representations of Olfactory Mixture Similarity using Inductive Biases

Authors: Gary Tom, Cher Tian Ser, Ella Miray Rajaonson, Stanley Lo, Hyun Suk Park, Brian Lee, Benjamin Manuel Sanchez
Primary area: applications to physical sciences (physics, chemistry, biology, etc.)
Keywords: representation learning, graph attention, graph neural networks, inductive bias, olfaction perception, molecular mixtures

Abstract:
Olfaction---how molecules are perceived as odors to humans---remains poorly understood. Recently, the primary odor map (POM) was introduced to digitize the olfactory properties of single compounds. However, smells in real life are not pure single molecules, but are complex mixtures of molecules, whose representations remain relatively underexplored. In this work, we introduce POMMix, extending the POM to represent mixtures. Our representation builds upon the symmetries of the problem space in a hierarchical manner: (1) graph neural networks for building molecular embeddings, (2) attention mechanisms for aggregating molecular representations into mixture representations, and (3) cosine prediction heads to encode olfactory perceptual distance in the mixture embedding space. POMMix achieves state-of-the-art predictive performance across multiple datasets. We also evaluate the generalizability of the representation on multiple splits when applied to unseen molecules and mixture sizes. Our work advances the effort to digitize olfaction, and highlights the synergy of domain expertise and deep learning in crafting expressive representations in low-data regimes.

Decision: reject
Reviewer scores: 6, 6, 5

Reviews and discussion:
<thread>
creation date:
1730304589453

thread_type:
official_review

<thread_object>
creator: reviewer_bx4l

<content>
summary:
This work presents POMMIX, a novel model for representing molecular mixtures, leveraging hierarchical design to capture the underlying symmetries in the mixture space. The approach comprises three key components: (1) graph neural networks for generating robust molecular embeddings, (2) attention mechanisms for effectively aggregating these molecular embeddings into comprehensive mixture representations, and (3) cosine similarity-based prediction heads to encode perceptual distances in the mixture embedding space, aligning with olfactory perception.

strengths:
POMMIX’s hierarchical architecture captures the structural complexity of molecular mixtures by integrating graph neural networks, attention mechanisms, and cosine-based prediction heads, enabling acquisition of mixture representations. By encoding perceptual distances in the embedding space through cosine prediction heads, POMMIX aligns mixture embeddings with olfactory perceptual similarities, a novel enhancement for sensory science applications. The model consistently demonstrates high predictive accuracy across multiple datasets, highlighting its robustness and generalizability for a range of mixture-related tasks, from olfactory perception to chemical property prediction. Additionally, POMMIX’s modular framework allows it to be readily adapted to various molecular mixture tasks, making it a flexible tool for predictive modeling and exploratory analysis in molecular science.

weaknesses:
This work has some limitations that highlight areas for future improvement in POMMIX. First, its performance is highly dependent on the quality and diversity of the training data; limited or biased data can hinder generalization, especially in underrepresented molecular categories. Additionally, POMMIX’s hierarchical architecture—combining graph neural networks and attention mechanisms—is computationally intensive, posing scalability challenges for very large datasets or deployment in resource-limited settings. The model’s high capacity for capturing complex representations also increases the risk of overfitting, particularly with smaller or highly correlated datasets, which could impact generalization to new data.

While innovative in its structure, POMMIX’s individual components could benefit from further exploration. For instance, alternative embedding techniques, such as chemical language models, could be evaluated alongside graph-based approaches to potentially enhance performance. An ablation study comparing the contributions of each pipeline component would provide valuable insights into optimizing and refining POMMIX’s architecture.

questions:
How does POMMIX handle biases or gaps in training data, particularly for underrepresented molecular categories? Would augmenting the data or introducing data-driven regularization improve generalization?

What steps could be taken to reduce overfitting, particularly for smaller or highly correlated datasets? Would techniques such as dropout, regularization, or data augmentation improve model robustness?

Have other embedding strategies, such as chemical language models, been considered as alternatives to graph-based methods? Would a hybrid approach provide any advantages, and how might the performance of different embeddings compare within the POMMIX framework?

Has an ablation study been conducted to evaluate the individual contributions of POMMIX’s components (graph neural networks, attention mechanisms, cosine prediction heads)? How would understanding the effectiveness of each component inform future improvements?
</content>
</thread_object>

</thread>
<thread>
creation date:
1730527745065

thread_type:
official_review

<thread_object>
creator: reviewer_usgz

<content>
summary:
In the paper, the authors introduce POMMIX, which is an extending framework of the POM to represent mixtures. The POMMIX framework includes the POM model pretrained with mono-molecular data, CHEMIX attention model and  a cosine distance prediction head. Experiments on the mixture dataset show the empirical performance of the method.

strengths:
1. The presentation of the method is very clear and easy to understand.
2. Many details of the experiments are provided, including the dataset and the schematic of the POMMIX model.

weaknesses:
1. I think ablation study about different POM network architecture is necessary.  Since the GraphNets architecture is not the current SOTA architecture, I think using other architecture may improve the performance, e.g. [1]. 

[1].Ying, Chengxuan, et al. "Do transformers really perform badly for graph representation?." Advances in neural information processing systems 34 (2021): 28877-28888.

questions:
See the weaknesses part above.
</content>
</thread_object>

</thread>
<thread>
creation date:
1730642019356

thread_type:
official_review

<thread_object>
creator: reviewer_xfhx

<content>
summary:
The authors report POMMIX, an approach to extend principal odor maps to mixtures. For this, they derive embeddings using a GNN and then use attention to aggregate those embeddings. Since they focus on a contrastive task, they use cosine predictive heads. 
They show that their approach outperforms various baselines.

strengths:
- The paper is very well written and the methodology is clearly described 
- The authors also carefully described their hyperparameter optimization 
- It also seems as if the authors were careful in building baselines
- Dealing with mixtures is an important problem in chemistry that is often ignored - many people focus on predicting the properties of pure compounds as this is simpler
- The analysis of the "White noise hypothesis" is a nice case study

weaknesses:
- The methodology the authors proposed seems to be well-suited to address the task, but there seem to be no major innovations. Using GNN-derived embeddings and aggregating them via attention has been done before, e.g., in https://arxiv.org/pdf/2312.16473
- The attention maps are interesting, but I found it difficult to gain insights from them. Also the discussion in the paper is mainly focussed on general observations as number of interactions increasing with the number of components. 

Overall, it is an interesting applied ML paper that nicely shows how ML can be applied to an exciting chemistry problem. There is little advancement in the ML methodology.

questions:
- Perhaps it goes beyond the scope of the work but I wonder if the performance of such models might not be improved a lot with MixUp-like augmentation techniques. 
- Could one obtain more chemical insights from the attention-map analysis by analyzing, for instance, how often certain functional groups/scaffolds interact with each other?
</content>
</thread_object>

</thread>
<thread>
creation date:
1732231156400

thread_type:
official_comment

<thread_object>
creator: authors

<content>
comment:
Dear Reviewers,

Thank you for your valuable feedback. We have carefully addressed your concerns and implemented your suggestions where possible and produced additional results to strengthen our claims. 

We paraphrase the comments we have included in our updated PDF in the official comments to each reviewer. We will summarize the reviewer comments, the additional experiments, and the corresponding changes below.

- **Ablation of features, GNN, CheMix, and prediction head**. We provided ablation studies (in addition to **Table 1**) on the different parts of the POMMix model, as suggested by Reviewer `uSgz`  and `BX4L`. All experimental results are found in the **Appendix A.7** section.
  - GNN ablation, comparing our models with other SOTA graph models. (**Table A2**)
  - CheMix prediction head ablation, justifying our choice of the scaled cosine distance prediction head (**Table A3**)
  - Ablation of feature, using chemical language model embedding and compare results between baseline and CheMix. (**Table A4**)

- **Possible augmentation methods for our dataset**. As brought up by Reviewers `XFHX` and `BX4L`, augmentation methods may improve the performance and generalization of our model. 
  - We have previously done augmentation of mixture data with single-molecule mixtures attained from the larger GS-LF, augmenting our dataset by ~15k mixtures. 
  - We add **Table A5** to show the reduced performance of our CheMix attention model from this augmentation.
  - MixUp augmentation is not applicable to our modeling problem. Other augmentation methods require hyperparameter tuning on the existing dataset in order to generate new similarity; however there is no physical prior to motivate doing this, and would likely cause overfitting.

- **Further analysis on the chemistry of interpretability study**. As brought up by Reviewer `XFHX`, more insight into the interpretability study would make the work stronger, and also more distinct from other work in mixture modeling. 
  - We provide chemical structure insights based on the interpretability studies (**Appendix A.10**) by clustering the molecules based on odors, and looking at the correspondance of the clusters with the strength of interaction. 
  - Additional plots are added in **Figure A5**.

We appreciate your time and effort in providing constructive feedback for our submission.

Kind regards, the authors.
</content>
</thread_object>

</thread>


Metareview:
metareview:
The paper extends POM (primary odor map) of single compounds molecules to POMMIX handling mixture compounds mixtures olfactory properties.  The molecular embedding are obtained via a graph neural networks , the mixture representation is obtained via an attention aggregation, and  a cosine prediction head encodes perceptual distance in the mixture embedding space. State of the art results are obtained on various benchmarks. 

Some of the reviewers did not unfortunately engage during the discussion phase although reminded several times by the AC and they did not respond in the AC/reviewers discussion phase. 
Given this lack of interaction, I have checked the paper and the reviews and author rebuttal and building my decision on that. 
In the rebuttal phase: 
* Authors provided in the rebuttal comparison to Molsets 
* Authors provided abalations on GNN, chemical language embeddings, attention mechanism, and the prediction head.

One of the main concerns in the paper is that the metholdolgy is not indeed novel as raised by the reviewer XFHX, hence the ablation of the method and more details on it will further strengthen the work. 
Unfortunately, although they were provided in the discussion phase, these were not incorporated to the manuscript and it is hard to judge the paper in its current form and a major revision is needed.