Federated Discrete Denoising Diffusion Model for Molecular Generation with OpenFL

Abhishek Pandey; Kevin Ta; Mattson Thieme; Patrick Foley; Prashant Shah

REVIEW 2 major objections 3 minor 13 references

Federated Discrete Denoising Diffusion Model for Molecular Generation with OpenFL

T0 review · 2 major / 3 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read This paper shows that a discrete denoising diffusion model for molecules, trained across separate data sites with federated learning, matches a model trained on a single centralized dataset.

desk verdict A straightforward integration of DiGress into OpenFL with an honest but statistically under-powered comparability claim; worth reviewing, not worth citing. read the letter →

arxiv 2501.12523 v1 pith:CLIIF7IJ submitted 2025-01-21 cs.LG cs.CR

Kevin Ta , Patrick Foley , Mattson Thieme , Abhishek Pandey , Prashant Shah This is my paper · ORCID

classification cs.LGcs.CR

keywords federatedlearningmoleculargenerationdiscretedenoisingdiffusionDiGressOpenFLdrugdiscoveryQM9datasetgraph

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

This paper tries to establish that collaborative, privacy-preserving training can replace centralized training for AI-driven molecular generation without a meaningful performance penalty. It does this by adapting DiGress, a graph-based discrete denoising diffusion model, to the OpenFL federated learning framework instead of a single data server. A two-client federation trained on the QM9 molecule dataset produced nearly the same validity and uniqueness of generated molecules as the centralized model, with slightly higher negative log-likelihood and regressor error. If the comparison holds, drug-discovery teams could pool proprietary data across institutions without moving or exposing that data.

What carries the argument

The central object is DiGress, a discrete denoising diffusion model that generates molecules as graphs, with atoms as nodes and bonds as edges. The argument is carried by a federated training loop defined in OpenFL's workflow API: the aggregator sends the global denoiser and regressor weights to collaborators, each trains locally on private data, and the aggregator combines updates by weighted federated averaging, $w_{t+1}=\sum_{k=1}^{K}\alpha_k w_k^t$. The denoising model minimizes cross-entropy between its predicted node and edge types and the original graph, while the regressor predicts the highest occupied molecular orbital and dipole moment by mean squared error; both models are federated together. OpenFL's Task Runner API is then able to run the same training in a production-ready setting with encrypted communication and trusted execution environments.

What would settle it

Reproduce the same two-site federation with a scaffold split of QM9, where chemically related molecules are kept on the same site, then compare validity and uniqueness to a centralized model; a drop materially larger than the reported 0.42% and 0.31% differences would show the comparability claim depends on the random split.

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Extended reading notes

Core claim

The paper reports that a federated DiGress model, trained over 100 rounds with one epoch per collaborator at two sites, matches the quality of a DiGress model trained for 100 epochs on the whole QM9 dataset. On validity (RDKit-sanitized molecules) the federated model scored 0.9560 versus 0.9600 for centralized learning; on uniqueness over 1,000 generated molecules it scored 0.9989 versus 0.9958. Negative log-likelihood and regressor mean absolute error were also close, within roughly 3% and 1.4% respectively. The authors interpret these results as evidence that federated learning can support collaborative drug-design model training while keeping data localized.

Load-bearing premise

The whole comparison rests on the assumption that randomly splitting QM9 into two sites resembles a real collaboration between data owners; if the sites' molecule distributions are sharply different, federated averaging may not converge to centralized quality.

Editorial extensions

If this is right

A pharmaceutical federation can train a molecular generator on data that stays at each institution, with no raw molecules leaving the site.
The small performance gap (validity 0.9560 versus 0.9600, uniqueness 0.9989 versus 0.9958) means a federated model is usable in practice for early drug-design screening.
Because only the model weights cross the network, the same architecture can be paired with stronger security layers without changing the molecular generation algorithm.
The approach extends beyond the two-client experiment: any number of collaborators can join the same round-based averaging loop, subject to communication and data-size constraints.
Conditional property guidance survives federation, since the regressor's MAE moved only from 0.6932 to 0.7026.

Reading between the lines

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

The random split used in the experiment is a favorable case where the two sites' data are statistically similar; a federation of companies with different chemical libraries would likely show a larger federated-versus-centralized gap, so the comparability claim is best read as a lower bound on degradation.
Because QM9 molecules are small and simple, validity and uniqueness are near ceiling; tests on larger, more complex datasets such as GuacaMol would reveal whether the federated model degrades more sharply outside this regime.
The same federated wrapper could be applied to other graph generative models with minor code changes, making the OpenFL workflow API a general way to privacy-preserve generative chemistry, not just DiGress.
The paper evaluates aggregate metrics, not whether the generated molecules are novel relative to each collaborator's private data; a useful next check is measuring scaffold novelty and overlap with each site's training set.

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Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

read the letter

Short version: this is a clean, honest feasibility study that says “we wired DiGress into OpenFL and on a random two-client split of QM9 it behaves about like the centralized baseline.” That is what it demonstrates, and the paper mostly does not overclaim except in the conclusion. The contribution is integration plus a head-to-head table, not a new method, and the table is thinner than it looks because there are no error bars.

What is new and what it does well: first integration of DiGress with OpenFL using the Task Runner API; the workflow description is clear and practical. The evaluation uses standard external metrics (RDKit validity, uniqueness on 1000 samples, NLL, MAE). Training budgets are matched (100 rounds vs 100 epochs, 1 epoch per client). The loss and sampling curves are helpful. The authors explicitly acknowledge in Section 6 that scaffold splits, larger datasets like GuacaMol, and heterogeneous data remain to be tested. There is no circularity: evaluation metrics are independent benchmarks.

Soft spots, in proportion: the central comparability claim rests on single runs. With 1,000 sampled molecules, the validity gap (0.9560 vs 0.9600) is within one standard error (~0.006), and the uniqueness gap is about 1.3 standard errors. So the numbers are consistent with comparability, not proof of it. The conclusion sentence that FL “can match” centralized performance goes beyond the evidence. Second, the random split produces near-IID clients, which is the easy case; real pharmaceutical silos are heterogeneous, and the paper acknowledges this only as future work. This does not invalidate the feasibility result but does limit what it means. Minor: the NLL percent difference is 3.06 in the table but 3.11 in the text (and MAE 1.35 vs 1.36). No code is released for the DiGress–OpenFL integration, so reproducibility is limited to the OpenFL framework itself.

Who this is for: researchers working on federated molecular generation or using OpenFL, as a proof-of-concept data point. It would benefit from a revision adding multiple seeds or confidence intervals, a non-IID split, and code release. As a proof of concept it is reasonable and the authors are transparent about limitations.

Recommendation: I would send it to peer review, not desk reject. It is a legitimate integration result, and the main concern is one the authors can address with a modest amount of additional computation.

Referee Report

2 major / 3 minor

Summary. The paper presents a federated training framework for DiGress, a discrete denoising diffusion model for molecular graph generation, implemented with the OpenFL workflow and task-runner APIs. The authors simulate a two-client federation by randomly splitting the QM9 dataset, train DiGress in both federated and centralized modes, and compare NLL, regressor MAE, validity, and uniqueness on 1,000 generated molecules. They report that the federated model achieves performance comparable to the centralized model, with small absolute percent differences across all four metrics. The manuscript also documents the OpenFL integration details and includes training/validation curves and sampled molecules. The central claim is that federated training can preserve molecular generation quality while enabling privacy-preserving collaborative learning.

Significance. If the comparability claim holds, the paper provides a useful feasibility demonstration: a widely used molecular diffusion model can be trained in a federated fashion with negligible degradation, addressing a real barrier to multi-institutional drug discovery. The contribution is primarily empirical and systems-oriented rather than algorithmic, but it fills a relevant gap by showing OpenFL's workflow API can accommodate a complex generative model with a separate regressor. The paper is clear about the architecture and provides a concrete integration recipe that other practitioners could follow. However, the strength of the empirical claim is currently limited by the absence of statistical uncertainty quantification and by the use of a randomized (near-IID) data split, which the authors themselves acknowledge does not mimic real heterogeneous silos.

major comments (2)

[Section 5, Table 1] The central claim of comparability rests on single point estimates from one centralized run and one federated run, with no random seeds, repeated trials, or confidence intervals. For the evaluation protocol using 1,000 generated molecules, the validity difference of 0.004 (0.9560 vs 0.9600) is well within the standard error of the estimate (~0.006), and the uniqueness difference of 0.003 is about 1.3 standard errors. The paper should report mean and variance over multiple seeds (or at least confirm the observed differences are reproducible across initializations) before claiming that federated learning 'can match the performance of centralized learning' in Section 6. Without such information, the results are consistent with comparability but do not statistically establish it.
[Section 5, data split; Section 6, limitations] The experiment randomly splits QM9 across two collaborators, producing near-IID partitions that are not representative of real pharmaceutical data silos, which are typically heterogeneous and non-IID. While Section 6 lists non-IID and scaffold splits as future work, the abstract's claim that the federated model 'achieves comparable performance with a model trained on centralized data' is not qualified to the IID setting and could be misread as evidence for real-world federations. The authors should either add experiments with a non-IID split (e.g., scaffold-based) or explicitly restrict the key claim to the IID simulation setting.

minor comments (3)

[Section 5, text vs Table 1] The reported absolute percent difference for NLL is inconsistent: Table 1 states 3.06%, while the text reports 3.11%. The value computed from Eq. (2) using the table's numbers is 3.06%, so the text should be corrected.
[Section 5, evaluation metrics] The uniqueness metric is described as 'assessed over 1,000 generated molecules', but it is not stated whether duplicates are removed across the 1,000 samples or within each diffused chain; defining the exact protocol would improve reproducibility.
[General] The paper does not include a reproducibility statement listing software versions, hardware, or random seeds. Providing these details, along with a link to any released code for the DiGress-OpenFL integration, would strengthen the experimental contribution.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the federated-versus-central comparison is an empirical benchmark with external metrics, not a constructional identity or a self-citation chain.

full rationale

The paper's central claim is that a DiGress model trained through OpenFL federated averaging achieves comparable validity, uniqueness, NLL, and MAE to the same model trained centrally. Nothing in the derivation reduces to its own inputs. The federated update rule, Eq. (1), is standard FedAvg from McMahan et al. [6], and Eq. (2) is only a definition of absolute percent difference, not a fitted prediction. The evaluation metrics (RDKit sanitization validity, uniqueness over 1,000 samples, NLL, and MAE) are externally defined and are not parameters fitted to force the comparability conclusion. The comparison uses the same QM9 data, the same model architecture, and matched training iterations, but that is experimental design, not circularity. The paper cites OpenFL [3], which includes two of the present authors, but that citation supports the existence and design of the software framework used in the experiment; it is not the evidence for the performance comparison. The acknowledged limitations, such as the randomized rather than scaffold split and the single-run comparison without error bars, are evidentiary concerns about generalizability and statistical strength, not circular reductions. The results are therefore self-contained empirical findings with no identified circular step.

Assumptions & free parameters 2 free parameters · 3 assumptions · 0 invented entities

No new physical or mathematical entities are introduced. The ledger captures the experimental and modeling assumptions behind the comparability claim, primarily the IID proxy, the aggregation convergence, and the benchmark validity.

free parameters (2)

number_of_collaborators = 2
The federated experiment hand-picks two clients; results may differ with more clients or with heterogeneous data distributions.
federated_rounds = 100
Training runs for 100 federation rounds (1 epoch per collaborator per round), matched to 100 centralized epochs by hand; this schedule is not derived and could affect convergence.

assumptions (3)

domain assumption Weighted federated averaging (Eq. 1) converges to a model comparable to centralized training under synchronized rounds.
The paper assumes the aggregation formula is sufficient without proof; this is the hypothesis being tested, not an independently established result.
domain assumption The QM9 dataset and RDKit sanitization provide a valid testbed for molecular generation validity and uniqueness.
The paper relies on QM9 and RDKit as standard benchmarks without justifying their sufficiency for the real-world drug discovery claim.
standard math The DiGress model and its hyperparameters (from [12]) are correct and performant when ported to the federated setting.
The paper preserves the DiGress training algorithm without re-deriving or verifying its components; this is standard reliance on prior work.

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Cite this review

Pith. "Pith review of Federated Discrete Denoising Diffusion Model for Molecular Generation with OpenFL." pith.science (2026). https://pith.science/paper/CLIIF7IJ

@misc{pith2026250112523,
  author       = {Pith},
  title        = {Pith review of: Federated Discrete Denoising Diffusion Model for Molecular Generation with OpenFL},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/CLIIF7IJ}},
  note         = {Machine review of arXiv:2501.12523}
}

read the original abstract

Generating unique molecules with biochemically desired properties to serve as viable drug candidates is a difficult task that requires specialized domain expertise. In recent years, diffusion models have shown promising results in accelerating the drug design process through AI-driven molecular generation. However, training these models requires massive amounts of data, which are often isolated in proprietary silos. OpenFL is a federated learning framework that enables privacy-preserving collaborative training across these decentralized data sites. In this work, we present a federated discrete denoising diffusion model that was trained using OpenFL. The federated model achieves comparable performance with a model trained on centralized data when evaluating the uniqueness and validity of the generated molecules. This demonstrates the utility of federated learning in the drug design process. OpenFL is available at: https://github.com/securefederatedai/openfl

Figures

Figures reproduced from arXiv: 2501.12523 by the authors.

**Figure 1.** OpenFL Workflow API: A global model is sent to collaborators for validation and local training. Each collaborator validates and updates the model with local data, then sends the updated model back to the aggregator. The aggregator combines these updates to form a new global model, completing one federation round. edges. Spectral and structural features are then computed at each iteration. The noisy graph, along with… view at source ↗

**Figure 2.** Model training loss across rounds 5 Experiment & Results To demonstrate the feasibility of FL for molecular generation, we utilize the QM9 dataset[8,10], which comprises of over 130k small organic molecules, each represented by its SMILES string and associated quantum chemical properties. This dataset is particularly suitable for our task due to its diversity and molecular validity. To simulate a federated learning … view at source ↗

**Figure 3.** Model validation loss across rounds (a) Uniqueness (b) Validity [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗

Figures from the paper (2 more)

**Figure 4.** Figure 4: Sampling metrics across rounds We evaluate the models using four key metrics: Negative Log-Likelihood (NLL) for the diffusion model during validation, Mean Absolute Error (MAE) for the regressor model during validation and Validity and Uniqueness when sampling molecule…

**Figure 5.** Figure 5: Example of three molecules generated by the federated model. The diffusion chains illustrate generation from a noisy graph to a plausible molecule 0.9989. The percent differences between the two methods are 3.11% for NLL, 1.36% for MAE, 0.42% for Validity, and 0.31% fo…

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

13 extracted references · 8 canonical work pages

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Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.

[1] [1]

Brown, N., Fiscato, M., Segler, M.H.S., Vaucher, A.C.: GuacaMol: Benchmarking models for de novo molecular design. J. Chem. Inf. Model.59(3), 1096–1108 (Mar 2019)

work page 2019

[2] [2]

Cao, N.D., Kipf, T.: Molgan: An implicit generative model for small molecular graphs (2022), https://arxiv.org/abs/1805.11973

arXiv 2022

[3] [3]

Foley, P., Sheller, M.J., Edwards, B., Pati, S., Riviera, W., Sharma, M., Narayana Moorthy, P., Wang, S.H., Martin, J., Mirhaji, P., Shah, P., Bakas, S.: OpenFL: the open federated learning library. Phys. Med. Biol.67(21), 214001 (Oct 2022)

work page 2022

[4] [4]

Ho,J.,Jain,A.,Abbeel,P.:Denoisingdiffusionprobabilisticmodels.arXivpreprint arxiv:2006.11239 (2020) 10 Authors Suppressed Due to Excessive Length

arXiv 2020

[5] [5]

Loeffler, H.H., He, J., Tibo, A., Janet, J.P., Voronov, A., Mervin, L.H., Engkvist, O.: Reinvent 4: Modern AI-driven generative molecule design. J. Cheminform. 16(1), 20 (Feb 2024)

work page 2024

[6] [6]

CoRRabs/1602.05629 (2016), http://arxiv

McMahan, H.B., Moore, E., Ramage, D., y Arcas, B.A.: Federated learning of deep networks using model averaging. CoRRabs/1602.05629 (2016), http://arxiv. org/abs/1602.05629

arXiv 2016

[7] [7]

Patterns 5(7), 100974 (2024)

Pati, S., Kumar, S., Varma, A., Edwards, B., Lu, C., Qu, L., Wang, J.J., Lak- shminarayanan, A., han Wang, S., Sheller, M.J., Chang, K., Singh, P., Ru- bin, D.L., Kalpathy-Cramer, J., Bakas, S.: Privacy preservation for federated learning in health care. Patterns 5(7), 100974 (2024). https://doi.org/https: //doi.org/10.1016/j.patter.2024.100974, https://w...

arXiv 2024

[8] [8]

Scientific Data1 (2014)

Ramakrishnan, R., Dral, P.O., Rupp, M., von Lilienfeld, O.A.: Quantum chemistry structures and properties of 134 kilo molecules. Scientific Data1 (2014)

work page 2014