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An evaluation of unconditional 3D molecular generation methods

T0 review · 4 major / 4 minor · reviewed 2026-08-16 · deepseek-v4-flash

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Pith's one-line read This paper argues that the near-saturation of standard 3D molecule-generation benchmarks is misleading: once chemical and physical validity are required, the best raw generator reaches 87.0% valid-unique-novel molecules, and its own…

desk verdict Useful, mostly reproducible validity-aware benchmark of five 3D generators, undercut by an abstract that contradicts its own post-processing table and a raw ranking confounded by hydrogen representation. read the letter →

arxiv 2505.00518 v1 pith:NIPDYBT4 submitted 2025-05-01 physics.chem-ph q-bio.QM

Martin Buttenschoen , Yael Ziv , Garrett M. Morris , Charlotte M. Deane This is my paper · ORCID
classification physics.chem-phq-bio.QM
keywords 3Dmoleculargenerationunconditionalvalidityphysicalchecksdrugdiscoverygenerativemodelevaluationpost-processingconformerquality
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

Unconditional 3D molecular generation is usually scored by whether the generated molecular graph is chemically valid, unique, and novel; recent papers report near-saturation on those benchmarks. This paper argues that such scores are misleading because they ignore the geometry of the 3D conformation. It evaluates five recent models on 100,000 samples each, requiring both a chemically valid graph and a physically plausible conformation, including bond lengths, bond angles, ring planarity, steric clashes, and internal strain. Under this combined test, the best raw model, SemlaFlow, produces 87.0% valid-unique-novel molecules, and after simple post-processing the best model is GCDM at 95.2%. These numbers sit below the 94.2% scored by the benchmark's own training data, so the paper concludes that unconditional 3D generation still has room to improve.

What carries the argument

The carrying object is a two-stage validity filter applied to every generated molecule. The first stage is graph-level: the molecule must parse, pass chemical sanitisation, have all explicit hydrogens, and be connected. The second stage is conformation-level: the 3D structure must pass six geometry and energy checks, including bond lengths and angles within 25% of experimental bounds, planar aromatic rings, planar double bonds, no internal steric clash, and a force-field strain-energy ratio below 100. The paper applies this filter, together with uniqueness and novelty checks based on canonical string identifiers, to 100,000 unconditional samples per model, with and without post-processing. Because some thresholds, notably the 30% van der Waals overlap allowance and the strain ratio of 100, are deliberately generous, passing them is a meaningful but not strict test of physical plausibility.

What would settle it

Re-evaluate the same five models' 100,000 samples under stricter physicality cutoffs, for example 10% bond-geometry deviation, 10% van der Waals overlap allowance, and a strain-energy ratio of 20. If the best methods still score above 90% valid-unique-novel, the paper's conclusion that unconditional 3D generation is not saturated would be called into question; if their scores drop sharply, the conclusion stands but is threshold-dependent.

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Extended reading notes

Core claim

Put in the authors' terms, the discovery is that standard benchmarks are saturated but the task is not. More than 99% of the valid molecules generated by the five methods are unique and novel, so diversity is not the bottleneck. The bottleneck is validity once it is defined to include the physical conformation: raw success rates are 59.7% for EQGAT-diff, 59.8% for FlowMol, 0.2% for GCDM, 2.9% for GeoLDM, and 87.5% for SemlaFlow. The two low models fail mainly because they do not generate explicit hydrogens; the other failures are split between chemically invalid graphs and physically invalid conformations. Post-processing, meaning keeping the largest fragment, adding hydrogens, and relaxing the structure with a universal force field, changes the ordering, putting GCDM at 95.2%, SemlaFlow at 92.4%, EQGAT-diff and FlowMol at 84.1%, and GeoLDM at 69.6%. Even the best results remain below the 94.2% validity of the training data and the 98.8% of an approved-drug reference set.

Load-bearing premise

The load-bearing premise is that the chosen thresholds, a 25% tolerance on bond geometry, a 30% allowance on van der Waals overlap, and a strain-energy ratio of 100, define what counts as physically valid; if a stricter definition is used, the reported success rates and the ranking of methods could change.

Editorial extensions

If this is right

  • The reported valid-unique-novel shares are upper bounds on useful output, since several of the physicality thresholds are acknowledged to be generous.
  • Uniqueness and novelty are effectively solved for these models, with over 99% of valid molecules being novel and unique, so future work should target chemical and physical validity rather than diversity.
  • Hydrogen handling is a first-order design decision: models that omit explicit hydrogens jump from near-zero raw validity to high validity once hydrogens are added in post-processing.
  • Because the training set itself scores 94.2% on the same checks, even the best method leaves room for improvement, contradicting the saturated-benchmark narrative.
  • Distribution metrics such as drug-likeness and synthetic accessibility can match the training distribution while chemical-space coverage remains incomplete, so those metrics alone cannot certify coverage.

Reading between the lines

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

  • Using the geometry and strain checks as a rejection filter or scoring term at sampling time could push several methods above 90% valid-unique-novel, an option the paper does not test.
  • The hydrogen failure pattern implies that changing the generative target from heavy-atom-only structures to full explicit-hydrogen structures could remove the largest chemical-invalidity source at the root.
  • Since the paper frames unconditional generation as a stepping stone to conditional tasks, the same physical-validity failures would likely appear in conditional generation; auditing conditional outputs with the same conformer checks would test that transfer.
  • Rankings by a single aggregate rate hide different failure modes: models fail at different stages, so future evaluations should report graph and conformation validity separately, as the paper's own tables do.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 4 minor

Summary. The manuscript evaluates five recent unconditional 3D molecular generation methods (EQGAT-diff, FlowMol, GCDM, GeoLDM, SemlaFlow) on the GEOM Drugs benchmark. The authors generate 100,000 molecules per method and report standard validity, uniqueness, and novelty metrics together with chemical and physical validity checks based on RDKit and the PoseBusters suite, both with and without post-processing (largest fragment, hydrogen addition, UFF minimization). They report that SemlaFlow achieves 87.0% valid-unique-novel molecules without post-processing and GCDM achieves 95.2% with post-processing, and they conclude that the widely reported saturated benchmarks are misleading because physical and chemical validity are far from perfect.

Significance. If the findings are correct, the paper makes a useful contribution by showing that standard benchmarks may saturate while geometric and energetic validity remains incomplete, and it provides detailed per-test failure analyses (chemical components in Table 4, physical components in Table 5) and a transparent computational protocol. The use of publicly available model weights and the fixed published thresholds of PoseBusters are strengths. However, internal inconsistencies in the headline claims and a methodological confound between hydrogen representation and chemical validity weaken the contribution as written and require correction before the conclusions can be fully credited.

major comments (4)
  1. [Abstract and Section 3 (Table 3)] The abstract states: "Overall, the best method, SemlaFlow, has a success rate of 87% in generating valid, unique, and novel molecules without post-processing and 92.4% with post-processing." This is directly contradicted by Section 3, which says "With post-processing, the best method according to these metrics becomes GCDM," and by Table 3, where GCDM+PP achieves 95.2% versus SemlaFlow+PP at 92.4%. The abstract must be corrected to report GCDM as the best post-processed method or to clearly qualify SemlaFlow as best only without post-processing.
  2. [Section 3 and Section 2.2 (Tables 1, 3, 4)] The no-post-processing ranking is confounded by hydrogen representation. Section 2.2 includes "the molecule has all of its hydrogens added explicitly" as a chemical validity test, but GCDM and GeoLDM output heavy-atom-only structures, with explicit-hydrogen pass rates of 0.2% and 5.3% in Table 4. Consequently, the sentence "All five 3D molecular generation methods generated large sets of valid, unique, and novel molecules" is false for these two methods, which achieve only 0.2% and 2.9% valid-unique-novel in Table 1. The authors acknowledge the cause, but a fair method comparison requires either identical hydrogen addition for all methods before the validity assessment or a heavy-atom-only validity definition; without one of these, the 87.0% headline for SemlaFlow conflates model quality with output format.
  3. [Section 3] The sentence "Given that these training data scores are higher than the best model, there is still room for improvement" is contradicted by Table 3: GEOM Drugs achieves 99.8% chemical and 94.2% physical validity for an overall 94.2%, while GCDM+PP achieves 95.5% chemical and 95.2% physical validity for an overall 95.2%. The best post-processed model therefore exceeds the training-data aggregate validity. The claim should be revised to refer to the per-test failure rates (for example, connectedness or steric clashes) rather than the aggregate training-data scores.
  4. [Section 2.2 and Tables 1/3] All success rates are reported as point estimates without variance, confidence intervals, or repeated-seed statistics, and the physical-validity thresholds are acknowledged as generous (30% van der Waals overlap tolerance and UFF energy ratio of 100). Since the post-processing ranking changes by only a few percentage points (SemlaFlow+PP 92.4% versus GCDM+PP 95.2%), the authors should report variability across runs and a sensitivity analysis over the validity thresholds to establish that the ranking is robust rather than an artifact of threshold choice.
minor comments (4)
  1. [Section 3] The sentence "The failure modes are in terms of both the generated molecular graphs and the 3D conformations are shown in Table 3" is grammatically broken and should be rephrased, for example as "The failure modes, for both the generated molecular graphs and the 3D conformations, are shown in Table 3."
  2. [Appendix C] The phrase "CA approved" appears to be a typo; if the intended status is FDA-approved or simply "approved," the text should be corrected to avoid ambiguity.
  3. [Table 6 and Appendix A] The term "spacial score" is likely a misspelling of "spatial score," unless the cited source (Krzyzanowski et al., 2023) intentionally uses that spelling; please verify and align the usage with the original publication.
  4. [Figure 3 caption] The caption contains a formatting error ("Fr ´echet") and the sentence about the recommended data set size "(5'000)" is unclear; please state the source of the recommended size and use consistent thousands separators.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity found: the benchmark is self-contained and no predicted quantity is identical to an input by construction.

full rationale

This paper is an empirical evaluation rather than a derivation, and no step in its argument reduces to its own inputs. The validity definition is stated upfront: a molecule must pass four chemical tests and six physical PoseBusters tests, and the authors apply these fixed tests uniformly to all five models and to the GEOM Drugs and DrugBank reference sets. No parameter is fitted to any subset of the generated data and then renamed as a prediction; the reported percentages are direct counts of molecules passing the predefined tests. The only author-overlapping input is the PoseBusters tool (Buttenschoen et al., 2024), but it is an externally published, fixed-threshold benchmark, and the thresholds are stated openly rather than tuned to the reported ranking, so its use does not make the central claim circular. The paper also discloses the hydrogen-format confound explicitly: 'Note that GCDM and GeoLDM do not add all hydrogens without post-processing,' and later states, 'The improvements of GCDM and GeoLDM are mostly due to the addition of hydrogens since these two methods do not generate all hydrogens explicitly.' That is a benchmark-design limitation, not a circularity, because the paper does not claim these models pass a heavy-atom-only validity test and it separately reports post-processed results. No uniqueness theorem, ansatz, or known result is imported from the authors' prior work to force a conclusion, and no equation in the paper has an output that is equivalent to its input by definition. The comparison of model scores to training-data scores is a standard benchmark practice even if the no-post-processing ranking is debatable; the debate concerns metric choice and interpretation, not circular reasoning.

Assumptions & free parameters 3 free parameters · 4 assumptions · 0 invented entities

This paper introduces no new entities and performs no fitting of generative model parameters. The central claim rests on standard cheminformatics tools and on the specific PoseBusters validity thresholds, which are hand-set from prior work and acknowledged as generous. The main assumptions are listed above.

free parameters (3)
  • Bond length/angle validity tolerance = 25% deviation threshold
    Violations below or above 25% are flagged in Section 2.2. This cutoff comes from PoseBusters but is a hand-set tolerance, and all physical validity rates depend on it.
  • Steric clash tolerance = 30% van der Waals overlap allowed
    Section 2.2 states that internal steric clash violations below 30% are flagged. This generous threshold directly affects physical validity percentages.
  • Strain energy ratio cutoff = 100 (UFF energy ratio)
    Section 2.2 defines physical validity as an energy ratio below 100 relative to an ensemble of minimized conformations. Changing this cutoff would re-rank the methods.
assumptions (4)
  • domain assumption RDKit sanitization and canonical SMILES are valid operational definitions of chemical validity, uniqueness, and novelty.
    Section 2.2 uses RDKit MolFromMolFile, SanitizeMol, and MolToSmiles as ground truth, with no independent chemical validation.
  • domain assumption PoseBusters geometric thresholds and the UFF energy ratio are valid proxies for physical validity of conformations.
    Section 2.2 defines physical validity solely via these tests; false negatives or false positives would bias the reported rankings.
  • domain assumption The published model weights are faithful implementations of EQGAT-diff, FlowMol, GCDM, GeoLDM, and SemlaFlow.
    Appendix B downloads weights from the cited repositories or by request and assumes they reflect the methods as described.
  • domain assumption GEOM Drugs is an appropriate novelty reference and distribution target for all five methods despite differing train/validation splits.
    Section 2.2 uses the entire GEOM Drugs set for novelty; comparisons remain well-defined, but distributional comparisons may be affected by split differences.

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Cite this review

Pith. "Pith review of An evaluation of unconditional 3D molecular generation methods." pith.science (2026). https://pith.science/paper/NIPDYBT4

@misc{pith2026250500518,
  author       = {Pith},
  title        = {Pith review of: An evaluation of unconditional 3D molecular generation methods},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/NIPDYBT4}},
  note         = {Machine review of arXiv:2505.00518}
}
read the original abstract

Unconditional molecular generation is a stepping stone for conditional molecular generation, which is important in \emph{de novo} drug design. Recent unconditional 3D molecular generation methods report saturated benchmarks, suggesting it is time to re-evaluate our benchmarks and compare the latest models. We assess five recent high-performing 3D molecular generation methods (EQGAT-diff, FlowMol, GCDM, GeoLDM, and SemlaFlow), in terms of both standard benchmarks and chemical and physical validity. Overall, the best method, SemlaFlow, has a success rate of 87% in generating valid, unique, and novel molecules without post-processing and 92.4% with post-processing.

Figures

Figures reproduced from arXiv: 2505.00518 by the authors.

Figure 1
Figure 1. Distributions of the molecules in terms of drug-likeness estimated by QED and synthetic [PITH_FULL_IMAGE:figures/full_fig_p005_1.png] view at source ↗
Figure 2
Figure 2. Visualization of the chemical space covered by the generated molecules and the reference [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 3
Figure 3. Frechet ChemNet distance between the sets of generated molecules with post-processing, ´ the training data GEOM Drugs and the approved drugs in DrugBank. SemlaFlow and EQGAT-diff capture the training data the best as they have the lowest distance (5.1) to GEOM Drugs. Note that all sets are significantly larger than the recommended data set size (5’000) to calculate this metric except DrugBank which contains 2,066 mo… view at source ↗
Figures from the paper (4 more)
Figure 4
Figure 4. Figure 4: Distributions of the shortest and longest bond lengths. The values are normalized by the [PITH_FULL_IMAGE:figures/full_fig_p009_4.png]
Figure 5
Figure 5. Figure 5: Distributions of the shortest non-covalent distances and most extreme bond angles. The [PITH_FULL_IMAGE:figures/full_fig_p010_5.png]
Figure 6
Figure 6. Figure 6: Distributions of the valid molecules in terms of number of heavy atoms, number of rotat [PITH_FULL_IMAGE:figures/full_fig_p013_6.png]
Figure 7
Figure 7. Figure 7: Distributions of the valid molecules in terms of synthetic accessibility, molecule complex [PITH_FULL_IMAGE:figures/full_fig_p014_7.png]

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Forward citations

Cited by 1 Pith paper

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    Combining self-conditioning, fake atoms, and late-stage geometry distortion lets a compact flow-matching model generate nearly always valid 3D drug-like molecules and match training-data chemistry better than existing...

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