Title: Towards Unified and Lossless Latent Space for 3D Molecular Latent Diffusion Modeling
Venue: NeurIPS 2025
Accepted: yes
Keywords: 3D molecule generation

3D variational auto-encoder

latent diffusion model
Abstract: 3D molecule generation is crucial for drug discovery and material science, requiring models to process complex multi-modalities, including atom types, chemical bonds, and 3D coordinates. A key challenge is integrating these modalities of different shapes while maintaining SE(3) equivariance for 3D coordinates. To achieve this, existing approaches typically maintain separate latent spaces for invariant and equivariant modalities, reducing efficiency in both training and sampling. 
In this work, we propose **U**nified Variational **A**uto-**E**ncoder for **3D** Molecular Latent Diffusion Modeling (**UAE-3D**), a multi-modal VAE that compresses 3D molecules into latent sequences from a unified latent space, while maintaining near-zero reconstruction error. This unified latent space eliminates the complexities of handling multi-modality and equivariance when performing latent diffusion modeling. We demonstrate this by employing the Diffusion Transformer--a general-purpose diffusion model without any molecular inductive bias--for latent generation. Extensive experiments on GEOM-Drugs and QM9 datasets demonstrate that our method significantly establishes new benchmarks in both *de novo* and conditional 3D molecule generation, achieving leading efficiency and quality. On GEOM-Drugs, it reduces FCD by 72.6% over the previous best result, while achieving over 70% relative average improvements in geometric fidelity. Our code is released at [https://github.com/lyc0930/UAE-3D/](https://github.com/lyc0930/UAE-3D/).


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nMolecules possess multiple modalities, such as atom types, inter-atomic bonds, and 3D spatial positions. Latent-based generative molecular models typically use distinct latent spaces to capture different properties like equivariance and invariance. In this manuscript, the authors propose a generative model that utilizes a single latent space—embedding all modalities within the same representation used for a diffusion process. The proposed method is faster than existing approaches while maintaining great accuracy.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths**\n\nThe authors provide extensive comparisons against many baseline methods. The proposed model appears faster while achieving comparable or superior results to existing approaches.\n\n**Weaknesses**\n\nOn GEOM-Drugs results:\n\n- JODO should be bold for the Frag metric.\n\nOn QM9 results:\n\n- JODO should be bold in Table 2 for the SNN metric.\n- In Table 2, EQGAT-diff should be bold for the AtomStable metric (2D).\n\nOverall, most results in Table 2 are very close, especially for the 2D metrics.\n\nIt is difficult to assess whether the model truly performs better for de novo design, as many scores are quite similar to those of JODO. If the authors are aware of another dataset that better differentiates between methods, additional experiments would be appreciated. On the other hand, since most studies use these datasets, the benchmark might be saturated. Still, the results are interesting, as they suggest that a different modeling paradigm can achieve comparable or better outcomes. I would suggest revising phrasing such as \"Breakthrough in Geometric Accuracy,\" as this claim is not clearly supported by the results in Tables 1 and 2. It is perfectly acceptable that the method does not outperform across all metrics—what is compelling is that a different architecture and training paradigm can achieve competitive results while being more efficient. This, in itself, can be a valuable contribution.\n\nQUESTIONS:\n- In the section SE(3)-Equivariant Augmentations, the authors could cite other works that use similar data augmentation techniques—for instance, AlphaFold 3.\n\n- The manuscript refers to both Lossless and near-Lossless reconstruction. I slightly prefer the term near-Lossless, as any trained method may result in minor reconstruction losses.\n\n- For the sampling time comparisons: did you re-run each model (e.g., GeoLDM), or are the reported sampling times taken from their original papers?\n\n- I would be interested in further exploration of the learned latent space. For example, does it preserve molecular clusters?\n\n- What variant of Maximum Mean Discrepancy (MMD) do you use? The result depends on the kernel choice.\n\n*Minor Comments:*\n\n> Equivariance through our tailor-made SE(3) augmentations\n\nUnless the authors introduce novel augmentations, this phrasing may be overstated, as these data augmentation strategies are fairly common in small molecule and protein design.\n\n> ** indicates results reproduced\n\nThis sentence should not begin with a symbol.\n\nLIMITATIONS:\nThe limitations of the work are not highlighted—either through experiments or in an explicit discussion. The authors should clarify and discuss limitations more clearly.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 2, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-06-21"}

comments: SUMMARY:
The paper describes a two step approach to molecular generation where first a VAE (built using transformers) is trained to learn a unified per-atom latent space of 3D molecules and then a diffusion model is trained to generate such spaces. The model demonstrates success in both unconditional and conditional evaluations and is significantly more efficient than the compared approaches.

STRENGTHS AND WEAKNESSES:
Strengths.  The described approach is highly efficient as diffusion can be done in the unified latent space using standard architectures.  The paper is clearly written and the model makes interesting use of transformers.

Weaknesses. 
This is a fast moving field and the SoA has advanced beyond the presented baselines (e.g., SemlaFlow outperforms the presented work both in terms of accuracy and speed).

The performance of the model is repeatedly attributed to the unified latent space, but no evidence is provided supporting this assertion.

QUESTIONS:
How do you achieve near zero reconstruction with a VAE? The latent space is variational so one would expect sampling from the generated embedding to produce different outputs. Do you only evaluate the mean? What are sampled outputs like?

To what extent is this model permutation invariant?

Can you design an ablation study or other experiment that supports your claim the accuracy of your model is due to the unified latent space instead of any of the other architectural and training procedure choices you made?

What advantages does your approach have over recently described flow matching approaches?

LIMITATIONS:
yes
TITLE: Official Review
CLARITY: 3
IMPACT: 2
ORIGINALITY: 3
DATE: 2025-07-02

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nIn this work, the authors present a VAE-based framework that compresses the multi-modal features (positions, atom types, and bonds) of 3D molecules into a latent space. Using a diffusion model, they show that sampling on this latent space is effective and demonstrate this by conditional generation on QM9 and unconditional generation on GEOM DRUGs and the QM9 dataset. This approach, due to its disjoint training pipeline, is faster than a few existing frameworks for generation.\n\nSTRENGTHS AND WEAKNESSES:\n### Strengths\n1. This paper presents joint modeling of continuous and discrete features for both molecular generation and property prediction. Due to its decoupled nature of 1. training a VAE to have a latent space 2. performing diffusion on latents it is faster than a few previous methods.\n\n\n\n### Weakness\n1. The results presented in all the table do not have error bars. \n2. This work only compares with a few discrete graph generation models, and leaves out a few joint modeling papers.  \n3. The paper misses important citations in the text/table\n- PONITA, Bekkers et al: https://openreview.net/forum?id=dPHLbUqGbr \n- FlowMol, Dunn et al, https://arxiv.org/abs/2404.19739\n- MolFM, https://arxiv.org/pdf/2307.09484\n\nQUESTIONS:\nQ1. How does the model truly ensure that the equivariance to SE(3) is truly present in the generated samples? In both the augmented and non-augmented cases.\n\nQ2. Given that this method relies on a lossless latent space, how can to ensure that this is not overfitting on training data? Could you also report (only) uniqueness scores in Tables 2 and 5?\n\nQ3. Could you provide additional details on how augmentation is done and added to the model? \n\nQ4. How to update this model if one of the modalities says bonds have missing data. Would this approach overfit on the bonds present and not generate the bonds not seen before? Is this a good model for molecule discovery, and if so, could you elaborate on this?\n\nQ5. The tsne plot is unclear to me. If this is in a Center of mass frame, how do I interpret translations? What do these plots contribute? \n\n\nOther suggestions:\n- Add other joint modeling papers and other missing citations in the Table for comparison.\n\nLIMITATIONS:\nLimitations section is not present in the main paper.", "TITLE": "Official Review", "RECOMMENDATION": 3, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-07-03"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper tackles the challenge of multi-modal 3D molecular generation by proposing UAE-3D, a unified variational autoencoder capable of compressing atom types, bond types, and 3D coordinates into a single latent space while maintaining near lossless reconstruction. By leveraging SE(3)-equivariant data augmentations in training, the method learns equivariant representations without relying on 3D-specific architectural biases. The authors demonstrate that their unified latent space allows for the application of latent diffusion models (specifically, Diffusion Transformer, DiT) to generate realistic molecules efficiently and accurately.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths**\n- The paper is methodologically well-grounded. The approach replaces separate modal latent spaces for molecular generation with a unified latent space, validated by strong empirical results. The SE(3)-equivariant augmentations for learning equivariant representations are elegantly simple and effective, and the use of a Relational Transformer allows seamless integration of node and edge features.\n\n- As visualized in Figure 2 , the model achieves 100% atom/bond accuracy and extremely low coordinate RMSD on reconstruction—substantially better than prior work (GeoLDM) and critical for downstream generative modeling. Demonstrates that training and inference times are significantly reduced compared to strong baselines (GeoLDM and JODO), largely due to unified latent modeling and use of a general diffusion backbone.\n\n- The model and pipeline architecture are clearly depicted in Figure 3, providing an accessible overview of feature extraction, latent encoding, and generative modeling.\n\n- This paper offers insightful ablations, showing the value of each key component (unified latent space, DiT backbone, data augmentation) and the effects on RMSD and geometry metrics. Figures 4(a-c) (t-SNE plots) demonstrate latent representation consistency under SE(3) transformations, aligning with the claim of geometric structure preservation.\n\n**Weakness**\n- The paper relies heavily on empirical demonstrations, lacking formal theoretical analysis of why the unified latent space retains all necessary information for 3D generation, or convergence guarantees for the learning of equivariance solely via data augmentation. While the gains are empirically solid, more discussion or theoretical intuition would improve the scientific contribution.\n\n- The ablation studies in Section 4.5, Table 5, and Table 6 are thoughtful but could be further strengthened. For example, the effect of latent dimension size, number of R-Trans layers, or replacing the DiT backbone with other modern diffusion architectures could be explored to validate robustness and architectural choices beyond what is already discussed. It would also be instructive to see whether the benefits of unified latent space and data augmentation persist across substantially larger or more complex molecules.\n\n- The paper claims broad applicability of the unified latent approach to diverse 3D molecular and multimodal datasets, but empirical validation is limited to QM9 and GEOM-Drugs. Explicit discussion of the limitations in generalization, scalability to much larger chemical spaces, or application to proteins/RNAs is somewhat deferred to future work.\n\nQUESTIONS:\n- Could the authors provide more formal justification (or empirical evidence across much more diverse datasets) for the claim that SE(3)-equivariant augmentation paired with the unified VAE suffices to ensure geometric and chemical preservation, as opposed to specialized architectures?\n- What is the effect of varying the latent space dimension, depth of R-Trans encoder, DiT architecture (vs other transformer-based or non-transformer LDM backbones), or hyperparameters $\\gamma$, $\\lambda$? Is the model robust to such design choices or sensitive in particular to one?\n- Are there patterns to the errors or invalid/implausible generations outside of what is summarized quantitatively in Tables 1/2? Could the authors expand with more systematic failure analysis (qualitative or quantitative)?\n\nLIMITATIONS:\nThe limitations section is in the appendix, but should be summarized more explicitly in the main paper.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-03"}