Pith. sign in

REVIEW 5 major objections 5 minor 54 references

LMDM:Latent Molecular Diffusion Model For 3D Molecule Generation

T0 review · 5 major / 5 minor · reviewed 2026-08-11 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read A latent diffusion model that separates covalent from van der Waals interactions during denoising claims state-of-the-art validity, uniqueness, and novelty in 3D molecule generation on QM9 and GEOM-Drugs.

desk verdict Combines GeoLDM and MDM with a diversity noise variable, but the manuscript is too incomplete and internally inconsistent to verify the claimed SOTA numbers. read the letter →

arxiv 2412.04242 v1 pith:FNUDYXU4 submitted 2024-12-05 cs.LG

Xiang Chen This is my paper · ORCID
classification cs.LG
keywords latentdiffusionmodel3Dmoleculargenerationequivariancescore-basedgenerativemodelingSE(3)symmetrydrug-likemoleculesvariationalnoisedualequivariantnetwork
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

LMDM is a latent diffusion model for generating 3D molecules: instead of diffusing on raw atomic coordinates, it first compresses molecular point clouds into a low-dimensional equivariant latent space and then runs the denoising process there. The author claims that this design, together with a score network split into a local branch for pairs within 2 Å (covalent bonds) and a global branch for all other pairs (van der Waals forces), improves the validity, uniqueness, and novelty of generated molecules relative to the EDM and GeoLDM baselines. The paper also claims that injecting a variational noise variable at every reverse step increases the diversity of the sampled molecules. If the claims are right, the approach is a step toward generating large drug-like molecules with high stability and toward controllable generation of molecules with specified quantum properties.

What carries the argument

The central object is a molecular variational autoencoder whose encoder and decoder are equivariant graph neural networks, mapping a molecular point cloud $G = \langle x, h\rangle$ into latent variables $z = \langle z_x, z_h\rangle$ that rotate and translate with the molecule. Diffusion happens on these latent variables, and a dual equivariant score network predicts the noise: one branch processes local edges (pairs within a fixed radius $\tau = 2$ Å, meant to mimic covalent bonds) and one processes global edges (meant to capture van der Waals forces). A variational noise encoder based on SchNet produces a stochastic control variable $\eta_v$ that is added at every reverse step, and the zero center-of-mass trick keeps the prior distribution rotation invariant. This combination — equivariant latent compression, distance-split score modeling, and stochastic control noise — is what the paper argues carries the performance gains.

What would settle it

Encode a set of molecules with known covalent bond lengths and regress the latent pairwise distances $\|z_{x,i} - z_{x,j}\|$ against true interatomic distances; if latent distances are not proportional to angstroms across the dataset, then the $\tau = 2$ Å cutoff in latent space cannot be selecting covalent bonds, and the local/global split is not doing the physical work the paper claims.

Watch

Extended reading notes

Core claim

On the paper's own terms, the discovery is that a diffusion process can run on equivariant latent variables instead of raw atomic coordinates without losing the SE(3) symmetries of molecular geometry, and that splitting the denoising score into a local part for edges within 2 Å and a global part for all other pairs lets the model capture covalent bonds and van der Waals interactions separately. The reported consequence is state-of-the-art performance on QM9 and GEOM-Drugs: for example, 98.8% validity and 92.1% novelty on QM9, and 99.5% validity and 63.4% stability on GEOM-Drugs, compared with prior diffusion models. The author also reports that conditioning on properties such as polarizability and HOMO-LUMO gap produces molecules whose predicted properties track the target values more closely than the GeoLDM baseline on most QM9 properties.

Load-bearing premise

The load-bearing premise is that distances in the learned latent space are measured in the same angstrom units as real molecular geometry, so the 2 Å radius used to split local and global edges in latent coordinates actually corresponds to covalent-bond distances in the molecule.

Editorial extensions

If this is right

  • On GEOM-Drugs, the model reports 99.5% validity and 63.4% molecular stability, higher than EDM's 68.6% validity and 13.7% stability, suggesting the local/global edge split helps on molecules averaging 46 atoms.
  • On QM9, it reports 98.8% validity, 95.2% uniqueness, and 92.1% novelty, reflecting the 4.8% effectiveness and 30.2% diversity improvements cited in the abstract.
  • Conditional generation with property labels attached to the atomic features produces lower mean absolute errors than GeoLDM on four of six QM9 targets, including a reported HOMO-LUMO gap error of 0.068 eV.
  • Replacing the KL regularization on the latent space with an early-stopping regularizer avoids numerical instability during training and yields better generation quality.
  • Because the diffusion runs in a lower-dimensional latent space, the model reduces the amount of computation in back-propagation and the memory footprint of the score network on large molecules.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • If the latent space distances are not calibrated to angstroms, the 2 Å cutoff in latent coordinates would not correspond to covalent bond lengths, so the local/global split would not be doing the physical modeling the paper describes; measuring that calibration would settle the question.
  • The paper attributes the 30.2% diversity improvement to the variational noise variable but does not ablate that component alone; isolating it in a controlled experiment would place that attribution on firmer ground.
  • The framework is not molecule-specific and could be carried over to other SE(3)-equivariant point-cloud generation tasks, such as protein backbones, with the radius reassigned to the appropriate scale.
  • The comparisons rely on published pre-trained baselines; an evaluation in which all models are retrained with matched compute and data splits would show how much of the margin is architectural rather than due to training protocol.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

5 major / 5 minor

Summary. The paper proposes LMDM, a latent diffusion model for 3D molecule generation. A variational autoencoder with equivariant encoders/decoders maps molecular point clouds into latent variables, and a dual equivariant score network models local edges (within a 2 Å radius) and global edges separately, with the intent of capturing covalent and van der Waals interactions. A stochastic control variable is injected at every reverse step to promote diversity. The authors report unconditional generation results on QM9 and GEOM-Drug showing improvements over EDM and GeoLDM, and conditional generation results on QM9 for six properties.

Significance. If the reported gains hold, LMDM would be a useful contribution to 3D molecular generation: the latent-space approach with explicit local/global edge modeling is timely, and the two-stage training and the control variable are reasonable design choices. However, the manuscript as presented does not support the central claim. The sampling algorithm is internally inconsistent with the training objective, the baseline comparison table is incomplete, and the experimental evaluation lacks statistical grounding. No code or checkpoints are released, which is standard practice in this area and would help resolve ambiguities in the algorithm description.

major comments (5)
  1. [Algorithm 2, line 9; Eq. (11); Appendix A, Eq. (19)] The reverse-sampling update in Algorithm 2 is not consistent with the training objective. Eq. (11) trains s_theta to match the score \nabla_{z_t} \log q(z_t|z_0), but Algorithm 2 line 9 computes \mu_\theta = (1/\sqrt{1-\beta_t})\left[z_t + (\beta_t/\sqrt{1-\bar\alpha_t}) s_\theta\right]. For the DDPM posterior, the correct score-based update is \mu_\theta = (1/\sqrt{\alpha_t})\left[z_t + \beta_t s_\theta\right], since s_\theta \approx -\epsilon/\sqrt{1-\bar\alpha_t}. The displayed coefficient is off by a factor of 1/\sqrt{1-\bar\alpha_t}, which grows large near t=1 and would cause the sampler to overshoot. If, instead, s_\theta is intended to be the noise predictor, the sign should be minus, as in Appendix A Eq. (19). The paper never reconciles the score-based notation of the main text with the noise-based derivation of the appendix. Because no code or checkpoints are provided, the reader cannot determine which update actually produced the reported results. This is a load-bearing issue for the central claim.
  2. [Table 1] The baseline comparison is incomplete in exactly the cells needed to support the claim that LMDM outperforms GeoLDM and EDM on all four metrics. GeoLDM's novelty is missing for QM9, and GeoLDM's uniqueness, novelty, and stability are missing for GEOM. In addition, EDM's GEOM validity, uniqueness, and novelty are all reported as 68.6, which is either a striking coincidence or an error. The text states that published pre-trained models were evaluated, so the missing entries are not explained. These gaps undermine the conclusion that LMDM 'outperforms the state-of-the-art models' and the numerical claims in the abstract.
  3. [Section 5.1] All metrics are computed from a single run of 10,000 generated molecules, with no error bars, multiple seeds, or statistical significance tests. Some reported differences are small relative to sampling noise; for example, LMDM's QM9 stability is 90.8% versus EDM's 91.1%, which is lower, not higher. Without repeated runs, the claimed improvements of 4.8% and 30.2% cannot be assessed. The paper should report mean and standard deviation over at least three independent sampling runs or otherwise demonstrate that the differences are not due to stochasticity.
  4. [Section 4.2, 'Edge Construction'] The split of edges into local and global is based on a fixed radius tau = 2 Å applied to the latent coordinates z_t, but the paper never verifies that distances in the learned latent space correspond to physical distances. If the autoencoder rescales or distorts coordinates, the local/global separation no longer matches the intended covalent versus van der Waals interactions, and the modeling of interatomic forces becomes unjustified. This assumption is load-bearing for the method's main design motivation and should be checked empirically (e.g., by comparing latent distances to real-space distances on the training set).
  5. [Section 4.2, 'Enhance diversity through variational noise'] The reported diversity improvement is partly mechanical. The method injects a fresh random variable eta_v at every reverse step (Algorithm 2 line 7) and then reports higher uniqueness/novelty than baselines that do not have this additional stochasticity. To support the claim that the architecture itself improves diversity, the comparison should include a baseline with an equivalent source of randomness (e.g., by adding the same per-step noise to EDM or GeoLDM) or measure diversity while controlling for the injected noise budget.
minor comments (5)
  1. [Section 4.2, first two paragraphs] The first two paragraphs of Section 4.2 are identical verbatim; one should be removed.
  2. [Figure 1] The label 'Local Radius =?' in Figure 1 is unfinished and should give the actual value (2 Å).
  3. [Eq. (5) and Algorithm 2] The notation is inconsistent: Eq. (5) uses alpha_t where the context requires \bar\alpha_t, and the relationship between s_theta and \epsilon_theta is never defined. This makes it difficult to check the sampling equations.
  4. [Section 5.2, Table 2] The text says 'almost all attributes can exceed' the baselines, but LMDM is worse than GeoLDM on dipole moment mu and heat capacity Cv. The claim should be qualified accordingly.
  5. [Abstract] The phrase 'reduces the amount of calculation in the back-propagation process' is not quantified or substantiated anywhere in the paper.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the central claims are external benchmark comparisons, and the diversity mechanism is a design choice rather than a fitted prediction.

full rationale

The paper's derivation chain is not circular in the sense defined by the review. The main claims are benchmark comparisons against EDM, GeoLDM, and G-Schnet on QM9 and GEOM-Drug, which are external baselines with published pre-trained models. The latent diffusion construction follows standard DDPM score-matching, and the reported validity, uniqueness, novelty, and stability scores are computed from generated samples rather than being equal to any model input by construction. The added variational noise for diversity is an explicit generative mechanism, not a fitted parameter that is later renamed as a prediction; the diversity metric is evaluated empirically. There are no load-bearing self-citations: the cited prior works (e.g., Xu et al. 2023, Huang et al. 2022, Ho et al. 2020) are external and do not constitute the present paper's own prior results. The manuscript does contain serious internal consistency issues, most notably the conflicting plus sign in the reverse sampling update in Algorithm 2 and Eq. (5) versus the minus sign in Appendix A Eq. (19), and incomplete benchmark table entries, but these are correctness and reproducibility defects, not circularity. The in-scope passages that assert limitations, such as the inability to reproduce G-Schnet on GEOM-Drug, weaken the evidence but do not make any claimed result equivalent to its inputs. Accordingly, the appropriate circularity score is 0.

Assumptions & free parameters 4 free parameters · 5 assumptions · 1 invented entities

The model rests on standard DDPM machinery, EGNN equivariance, and the latent-diffusion setup from GeoLDM. The only new free design choices are the local radius, the latent dimension, and the prior distribution of the control variable; none are fitted to independent data. The paper introduces one new latent variable (ηv) with no external falsifiable handle.

free parameters (4)
  • Local edge radius τ = 2 Å
    Threshold for local vs global edges in the score network; chosen by hand, and applied in the latent coordinate space without checking the metric scale.
  • Latent dimension k = 1 for QM9, 2 for GEOM-Drug
    Chosen by hand based on dataset size; the ablation in Appendix F shows k=1 works better on QM9.
  • Control variable prior = U(−1, +1) in the text; N(0,I) in Algorithm 2
    The paper says sampling zv from U(−1,+1) improves generation, but the sampling pseudocode draws ηv from N(0,I). The distribution is a free design choice.
  • Diffusion timesteps T and noise schedule = not stated
    The number of diffusion steps and the βt schedule are never given, though they are standard free choices in any DDPM.
assumptions (5)
  • standard math The standard DDPM forward and reverse processes (Eqs. 1-4) are assumed.
    Appendix A re-derives the standard Ho et al. (2020) diffusion results; the paper relies on these as background.
  • domain assumption The EGNN autoencoder is E(n)-equivariant.
    The encoder and decoder are parameterized with EGNN (Satorras et al., 2021); the proof in Appendix C reproduces the standard equivariance argument.
  • domain assumption The latent diffusion prior p(z_T) is an isotropic Gaussian.
    The model uses zero center-of-mass noise and ES-reg to approximate a standard Gaussian latent prior, which underpins the equivariant Markov kernel proof in Appendix D.
  • ad hoc to paper Latent coordinates preserve physical distances.
    The local/global edge split uses a 2 Å radius on latent coordinates (Section 4.2), which assumes the latent space is calibrated to Ångstrom units.
  • standard math Equivariant Markov kernels preserve invariance of the marginal distribution.
    Appendix D follows Xu et al. (2022) and Hoogeboom et al. (2022) to show that equivariant transitions preserve invariance; this is a design principle, not a new proof.
invented entities (1)
  • Stochastic control variable ηv
    purpose: Injected into every reverse diffusion step to increase generation diversity
    A latent variable with a learned mean/variance encoder and a KL loss; at sampling it is replaced by a sample from a hand-picked prior. It has no falsifiable handle beyond the reported benchmark metrics.

how reviews work

0 comments
Cite this review

Pith. "Pith review of LMDM:Latent Molecular Diffusion Model For 3D Molecule Generation." pith.science (2026). https://pith.science/paper/FNUDYXU4

@misc{pith2026241204242,
  author       = {Pith},
  title        = {Pith review of: LMDM:Latent Molecular Diffusion Model For 3D Molecule Generation},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/FNUDYXU4}},
  note         = {Machine review of arXiv:2412.04242}
}
read the original abstract

n this work, we propose a latent molecular diffusion model that can make the generated 3D molecules rich in diversity and maintain rich geometric features. The model captures the information of the forces and local constraints between atoms so that the generated molecules can maintain Euclidean transformation and high level of effectiveness and diversity. We also use the lowerrank manifold advantage of the latent variables of the latent model to fuse the information of the forces between atoms to better maintain the geometric equivariant properties of the molecules. Because there is no need to perform information fusion encoding in stages like traditional encoders and decoders, this reduces the amount of calculation in the back-propagation process. The model keeps the forces and local constraints of particle bonds in the latent variable space, reducing the impact of underfitting on the surface of the network on the large position drift of the particle geometry, so that our model can converge earlier. We introduce a distribution control variable in each backward step to strengthen exploration and improve the diversity of generation. In the experiment, the quality of the samples we generated and the convergence speed of the model have been significantly improved.

Figures

Figures reproduced from arXiv: 2412.04242 by the authors.

Figure 1
Figure 1. Illustration of LMDM. We outline the training process of the proposed LMDM model. The encoder Eϕ coordinates x and molecular features h are encoded into equivariant latent variables R,A, and the time step encoding is used to incorporate the sequential information into the molecular information. We gradually add noise through the latent diffusion transformation q(Gt | Gt−1) until the latent variable distribution conv… view at source ↗
Figure 2
Figure 2. [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Illustration of the specific implementation of the Markov kernel (double equivariant denoising score network). In fact, de￾pending on the local and global edges of the input, we can use it as a local or global equivariant encoder to capture the molecular internal forces in the model and output the expected target score. The implementation of Schnet comes from (Schutt et al. ¨ , 2017). of the ψ function, maintain the… view at source ↗
Figures from the paper (4 more)
Figure 4
Figure 4. Figure 4: Molecules generated by LMDM trained on QM9 (left four) and DRUG (right two). This simplified objective is equivalent to learning sθ by sampling the diffusing molecules zt with a time step of t and using the logarithmic density gradient of the data distribution. Algorit…
Figure 5
Figure 5. Figure 5: Molecules generated by conditional LMDM. We conduct controllable generation with interpolation among different Polariz￾ability α values with the same reparametrization noise ϵ. The given α values are provided at the bottom [PITH_FULL_IMAGE:figures/full_fig_p008_5.png]
Figure 6
Figure 6. Figure 6: Molecules generated from LMDM trained on QM9. 17 [PITH_FULL_IMAGE:figures/full_fig_p017_6.png]
Figure 7
Figure 7. Figure 7: Molecules generated from LMDM trained on GEOM-Drug. 18 [PITH_FULL_IMAGE:figures/full_fig_p018_7.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

54 extracted references · 24 canonical work pages

  1. [1]

    write newline

    " write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 global.max substring 't := if while FUNCTION format.date year duplicate empty "emp...

  2. [2]

    S., and Kondor, R

    Anderson, B., Hy, T. S., and Kondor, R. Cormorant: Covariant molecular neural networks. Advances in neural information processing systems, 32, 2019

    work page 2019

  3. [3]

    A contrastive learning approach for training variational autoencoder priors

    Aneja, J., Schwing, A., Kautz, J., and Vahdat, A. A contrastive learning approach for training variational autoencoder priors. Advances in neural information processing systems, 34: 0 480--493, 2021

    work page 2021

  4. [4]

    and Gomez-Bombarelli, R

    Axelrod, S. and Gomez-Bombarelli, R. Geom, energy-annotated molecular conformations for property prediction and molecular generation. Scientific Data, 9 0 (1): 0 1--14, 2022

    work page 2022

  5. [5]

    E., Sun, L., Mailoa, J

    Batzner, S., Smidt, T. E., Sun, L., Mailoa, J. P., Kornbluth, M., Molinari, N., and Kozinsky, B. Se (3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials. arXiv preprint arXiv:2101.03164, 2021

    arXiv 2021

  6. [6]

    and Wipf, D

    Dai, B. and Wipf, D. Diagnosing and enhancing VAE models. In International Conference on Learning Representations, 2019. URL https://openreview.net/forum?id=B1e0X3C9tQ

    work page 2019

  7. [7]

    E., Fischer, V., and Welling, M

    Fuchs, F., Worrall, D. E., Fischer, V., and Welling, M. Se(3)-transformers: 3d roto-translation equivariant attention networks. In Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS , 2020

    work page 2020

  8. [8]

    Symmetry-adapted generation of 3d point sets for the targeted discovery of molecules

    Gebauer, N., Gastegger, M., and Sch \"u tt, K. Symmetry-adapted generation of 3d point sets for the targeted discovery of molecules. Advances in neural information processing systems, 32, 2019

    2019

Show all 54 references
  1. [9]

    Gebauer, N. W. A., Gastegger, M., and Schütt, K. T. Symmetry-adapted generation of 3d point sets for the targeted discovery of molecules, 2020. URL https://arxiv.org/abs/1906.00957

    2020 arXiv

  2. [10]

    N., Duvenaud, D., Hern \'a ndez-Lobato, J

    G \'o mez-Bombarelli, R., Wei, J. N., Duvenaud, D., Hern \'a ndez-Lobato, J. M., S \'a nchez-Lengeling, B., Sheberla, D., Aguilera-Iparraguirre, J., Hirzel, T. D., Adams, R. P., and Aspuru-Guzik, A. Automatic chemical design using a data-driven continuous representation of mol...

    2018

  3. [11]

    Bidirectional molecule generation with recurrent neural networks

    Grisoni, F., Moret, M., Lingwood, R., and Schneider, G. Bidirectional molecule generation with recurrent neural networks. Journal of chemical information and modeling, 60 0 (3): 0 1175--1183, 2020

    2020

  4. [12]

    Denoising diffusion probabilistic models

    Ho, J., Jain, A., and Abbeel, P. Denoising diffusion probabilistic models. arXiv preprint arXiv:2006.11239, 2020

    2006 arXiv

  5. [13]

    G., Vignac, C., and Welling, M

    Hoogeboom, E., Satorras, V. G., Vignac, C., and Welling, M. Equivariant diffusion for molecule generation in 3d. In International Conference on Machine Learning, pp.\ 8867--8887. PMLR, 2022 a

    2022

  6. [14]

    G., Vignac, C., and Welling, M

    Hoogeboom, E., Satorras, V. G., Vignac, C., and Welling, M. Equivariant diffusion for molecule generation in 3d, 2022 b . URL https://arxiv.org/abs/2203.17003

    2022 arXiv

  7. [15]

    Mdm: Molecular diffusion model for 3d molecule generation

    Huang, L., Zhang, H., Xu, T., and Wong, K.-C. Mdm: Molecular diffusion model for 3d molecule generation. arXiv preprint arXiv:2209.05710, 2022

    2022 arXiv

  8. [16]

    Jing, B., Eismann, S., Suriana, P., Townshend, R. J. L., and Dror, R. Learning from protein structure with geometric vector perceptrons, 2021. URL https://arxiv.org/abs/2009.01411

    2021 arXiv

  9. [17]

    Jo, J., Lee, S., and Hwang, S. J. Score-based generative modeling of graphs via the system of stochastic differential equations. arXiv preprint arXiv:2202.02514, 2022

    2022 arXiv

  10. [18]

    Highly accurate protein structure prediction with alphafold

    Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Z \' dek, A., Potapenko, A., et al. Highly accurate protein structure prediction with alphafold. Nature, 596 0 (7873): 0 583--589, 2021

    2021

  11. [19]

    Kingma, D. P. and Ba, J. Adam: A method for stochastic optimization. In Bengio, Y. and LeCun, Y. (eds.), 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings , 2015. URL http://arxiv.org/abs/1412.6980

    2015 arXiv

  12. [20]

    Kingma, D. P. and Welling, M. Auto-encoding variational bayes. In 2nd International Conference on Learning Representations, 2013

    2013

  13. [21]

    Directional message passing for molecular graphs

    Klicpera, J., Gro , J., and G \" u nnemann, S. Directional message passing for molecular graphs. In 8th International Conference on Learning Representations, ICLR , 2020

    2020

  14. [22]

    Equivariant flows: exact likelihood generative learning for symmetric densities

    K \"o hler, J., Klein, L., and No \'e , F. Equivariant flows: exact likelihood generative learning for symmetric densities. In International conference on machine learning, pp.\ 5361--5370. PMLR, 2020

    2020

  15. [23]

    Diffwave: A versatile diffusion model for audio synthesis

    Kong, Z., Ping, W., Huang, J., Zhao, K., and Catanzaro, B. Diffwave: A versatile diffusion model for audio synthesis. In International Conference on Learning Representations, 2021. URL https://openreview.net/forum?id=a-xFK8Ymz5J

    2021

  16. [24]

    L., Thickstun, J., Gulrajani, I., Liang, P., and Hashimoto, T

    Li, X. L., Thickstun, J., Gulrajani, I., Liang, P., and Hashimoto, T. Diffusion- LM improves controllable text generation. In Oh, A. H., Agarwal, A., Belgrave, D., and Cho, K. (eds.), Advances in Neural Information Processing Systems, 2022. URL https://openreview.net/forum?id=...

    2022

  17. [25]

    Lin, H., Huang, Y., Liu, M., Li, X., Ji, S., and Li, S. Z. Diffbp: Generative diffusion of 3d molecules for target protein binding. arXiv preprint arXiv:2211.11214, 2022

    2022 arXiv

  18. [26]

    A kernelized stein discrepancy for goodness-of-fit tests

    Liu, Q., Lee, J., and Jordan, M. A kernelized stein discrepancy for goodness-of-fit tests. In International conference on machine learning, pp.\ 276--284. PMLR, 2016

    2016

  19. [27]

    Antigen-specific antibody design and optimization with diffusion-based generative models for protein structures

    Luo, S., Su, Y., Peng, X., Wang, S., Peng, J., and Ma, J. Antigen-specific antibody design and optimization with diffusion-based generative models for protein structures. In Oh, A. H., Agarwal, A., Belgrave, D., and Cho, K. (eds.), Advances in Neural Information Processing Sys...

    2022

  20. [28]

    F low S eq: Non-autoregressive conditional sequence generation with generative flow

    Ma, X., Zhou, C., Li, X., Neubig, G., and Hovy, E. F low S eq: Non-autoregressive conditional sequence generation with generative flow. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natur...

    2019 doi

  21. [29]

    Nichol, A. Q. and Dhariwal, P. Improved denoising diffusion probabilistic models. In International Conference on Machine Learning, pp.\ 8162--8171. PMLR, 2021

    2021

  22. [30]

    C., Caffarena, E

    Pereira, J. C., Caffarena, E. R., and Dos Santos, C. N. Boosting docking-based virtual screening with deep learning. Journal of chemical information and modeling, 56 0 (12): 0 2495--2506, 2016

    2016

  23. [31]

    O., Rupp, M., and Von Lilienfeld, O

    Ramakrishnan, R., Dral, P. O., Rupp, M., and Von Lilienfeld, O. A. Quantum chemistry structures and properties of 134 kilo molecules. Scientific data, 1 0 (1): 0 1--7, 2014

    2014

  24. [32]

    Generating diverse high-fidelity images with vq-vae-2

    Razavi, A., Van den Oord, A., and Vinyals, O. Generating diverse high-fidelity images with vq-vae-2. Advances in neural information processing systems, 32, 2019

    2019

  25. [33]

    High-resolution image synthesis with latent diffusion models

    Rombach, R., Blattmann, A., Lorenz, D., Esser, P., and Ommer, B. High-resolution image synthesis with latent diffusion models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp.\ 10684--10695, 2022

    2022

  26. [34]

    G., Hoogeboom, E., Fuchs, F

    Satorras, V. G., Hoogeboom, E., Fuchs, F. B., Posner, I., and Welling, M. E (n) equivariant normalizing flows for molecule generation in 3d. arXiv preprint arXiv:2105.09016, 2021

    2021 arXiv

  27. [35]

    G., Hoogeboom, E., and Welling, M

    Satorras, V. G., Hoogeboom, E., and Welling, M. E(n) equivariant graph neural networks, 2022. URL https://arxiv.org/abs/2102.09844

    2022 arXiv

  28. [36]

    u tt, K., Kindermans, P.-J., Sauceda Felix, H. E., Chmiela, S., Tkatchenko, A., and M\

    Sch\" u tt, K., Kindermans, P.-J., Sauceda Felix, H. E., Chmiela, S., Tkatchenko, A., and M\" u ller, K.-R. Schnet: A continuous-filter convolutional neural network for modeling quantum interactions. In Advances in Neural Information Processing Systems, pp.\ 991--1001. Curran ...

    2017

  29. [37]

    Serre, J.-P. et al. Linear representations of finite groups, volume 42. Springer, 1977

    1977

  30. [38]

    and Komodakis, N

    Simonovsky, M. and Komodakis, N. Graphvae: Towards generation of small graphs using variational autoencoders. In International conference on artificial neural networks, pp.\ 412--422. Springer, 2018

    2018

  31. [39]

    Deep unsupervised learning using nonequilibrium thermodynamics

    Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., and Ganguli, S. Deep unsupervised learning using nonequilibrium thermodynamics. In International Conference on Machine Learning, pp.\ 2256--2265. PMLR, 2015

    2015

  32. [40]

    Denoising diffusion implicit models

    Song, J., Meng, C., and Ermon, S. Denoising diffusion implicit models. In International Conference on Learning Representations, 2021 a . URL https://openreview.net/forum?id=St1giarCHLP

    2021

  33. [41]

    and Ermon, S

    Song, Y. and Ermon, S. Generative modeling by estimating gradients of the data distribution. In Advances in Neural Information Processing Systems, pp.\ 11918--11930, 2019

    2019

  34. [42]

    P., Kumar, A., Ermon, S., and Poole, B

    Song, Y., Sohl-Dickstein, J., Kingma, D. P., Kumar, A., Ermon, S., and Poole, B. Score-based generative modeling through stochastic differential equations. In International Conference on Learning Representations, 2021 b

    2021

  35. [43]

    M., Yang, L., Li, L., Kohlhoff, K., and Riley, P

    Thomas, N., Smidt, T., Kearnes, S. M., Yang, L., Li, L., Kohlhoff, K., and Riley, P. Tensor field networks: Rotation- and translation-equivariant neural networks for 3d point clouds. CoRR, abs/1802.08219, 2018

    2018 arXiv

  36. [44]

    Townshend, R. J. L., V \"o gele, M., Suriana, P. A., Derry, A., Powers, A., Laloudakis, Y., Balachandar, S., Jing, B., Anderson, B. M., Eismann, S., Kondor, R., Altman, R., and Dror, R. O. ATOM 3d: Tasks on molecules in three dimensions. In Thirty-fifth Conference on Neural In...

    2021

  37. [45]

    Score-based generative modeling in latent space

    Vahdat, A., Kreis, K., and Kautz, J. Score-based generative modeling in latent space. Advances in Neural Information Processing Systems, 34: 0 11287--11302, 2021

    2021

  38. [46]

    Wave N et: A generative model for raw audio

    van den Oord, A., Dieleman, S., Zen, H., Simonyan, K., Vinyals, O., Graves, A., Kalchbrenner, N., Senior, A., and Kavukcuoglu, K. Wave N et: A generative model for raw audio. arXiv preprint arXiv:1609.03499, 2016

    2016 arXiv

  39. [47]

    Smiles, a chemical language and information system

    Weininger, D. Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences, 28 0 (1): 0 31--36, 1988

    1988

  40. [48]

    Unsupervised learning of group invariant and equivariant representations

    Winter, R., Bertolini, M., Le, T., Noe, F., and Clevert, D.-A. Unsupervised learning of group invariant and equivariant representations. In Oh, A. H., Agarwal, A., Belgrave, D., and Cho, K. (eds.), Advances in Neural Information Processing Systems, 2022. URL https://openreview...

    2022

  41. [49]

    Diffusion-based molecule generation with informative prior bridges

    Wu, L., Gong, C., Liu, X., Ye, M., and qiang liu. Diffusion-based molecule generation with informative prior bridges. In Oh, A. H., Agarwal, A., Belgrave, D., and Cho, K. (eds.), Advances in Neural Information Processing Systems, 2022. URL https://openreview.net/forum?id=TJUNtiZiTKE

    2022

  42. [50]

    Geodiff: A geometric diffusion model for molecular conformation generation

    Xu, M., Yu, L., Song, Y., Shi, C., Ermon, S., and Tang, J. Geodiff: A geometric diffusion model for molecular conformation generation. arXiv preprint arXiv:2203.02923, 2022

    2022 arXiv

  43. [51]

    Geometric latent diffusion models for 3d molecule generation

    Xu, M., Powers, A., Dror, R., Ermon, S., and Leskovec, J. Geometric latent diffusion models for 3d molecule generation. In International Conference on Machine Learning. PMLR, 2023

    2023

  44. [52]

    Y., Zhang, H., Pang, R., Qin, J., Ku, A., Xu, Y., Baldridge, J., and Wu, Y

    Yu, J., Li, X., Koh, J. Y., Zhang, H., Pang, R., Qin, J., Ku, A., Xu, Y., Baldridge, J., and Wu, Y. Vector-quantized image modeling with improved VQGAN . In International Conference on Learning Representations, 2022. URL https://openreview.net/forum?id=pfNyExj7z2

    2022

  45. [53]

    and Wang, F

    Zang, C. and Wang, F. Moflow: an invertible flow model for generating molecular graphs. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp.\ 617--626, 2020

    2020

  46. [54]

    LION : Latent point diffusion models for 3d shape generation

    Zeng, X., Vahdat, A., Williams, F., Gojcic, Z., Litany, O., Fidler, S., and Kreis, K. LION : Latent point diffusion models for 3d shape generation. In Oh, A. H., Agarwal, A., Belgrave, D., and Cho, K. (eds.), Advances in Neural Information Processing Systems, 2022. URL https:/...

    2022

Pith tools

Reviewed August 11, 2026 · model on record in the stance chip above.