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REVIEW 4 major objections 4 minor 2 cited by

GEOM-Drugs Revisited: Toward More Chemically Accurate Benchmarks for 3D Molecule Generation

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

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Pith's one-line read This paper claims that standard stability scores for 3D drug-like molecule generation are inflated by a valency-counting bug and an inconsistent force-field benchmark, and that correcting them lowers reported scores while preserving model…

desk verdict A credible, useful bug report on valency evaluation in GEOM-Drugs, wrapped around a comparative flow-vs-diffusion claim that the reported experiments do not support. read the letter →

arxiv 2505.00169 v2 pith:3HQEMKON submitted 2025-04-30 cs.LG cs.AI

Filipp Nikitin , Ian Dunn , David Ryan Koes , Olexandr Isayev This is my paper · ORCID
classification cs.LGcs.AI
keywords 3DmoleculegenerationGEOM-DrugsmolecularstabilityvalencyGFN2-xTBenergybenchmarkevaluationmetricsflowmatching
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 argues that widely used evaluation protocols for 3D generation of drug-like molecules on the GEOM-Drugs benchmark overstate how well models perform. It traces the inflation to three fixable flaws: aromatic bonds counted with a valency of 1 instead of 1.5 in a widely reused implementation, a valency lookup table that accepts chemically impossible configurations such as neutral trivalent carbon, and energy evaluations using the MMFF force field even though the reference dataset was optimized with GFN2-xTB. The paper supplies corrected valency tables, removes data fragments left by failed geometry optimizations, and proposes a geometry and relaxation-energy benchmark computed with GFN2-xTB. Re-evaluated models lose 1–3 percentage points of molecular stability under the corrected valency scheme, and retraining on a kekulized dataset raises validity by roughly 5% for most tested models. The rankings of the models survive the correction, but a previously obscured gap between flow-matching and diffusion-based models becomes visible.

What carries the argument

The central object is the aromatic-dependent valency tuple: instead of a single valency per element and charge, each atom is described by $($element, formal charge, $n_{\mathrm{arom}}$, $v_{\mathrm{other}})$, where $n_{\mathrm{arom}}$ is the number of aromatic bonds and $v_{\mathrm{other}}$ is the total bond order from non-aromatic bonds, with the allowed tuples learned from the cleaned GEOM-Drugs set. This tuple is what distinguishes chemically valid aromatic environments, such as benzene carbon in the configuration $(2,1)$ or $(3,0)$, from invalid ones that the old table accepted. The second mechanism is the GFN2-xTB relaxation benchmark: each generated structure is re-optimized with GFN2-xTB and scored by the relaxation energy $\Delta E_{\mathrm{relax}}$ and by average changes in bond lengths, bond angles, and torsion angles between the raw and relaxed structures, giving an interpretable geometric and energetic measure of how far a model's output sits from the reference energy landscape.

What would settle it

Relax the same 5,000 generated molecules per model with a higher-level method such as DFT or coupled-cluster and compare model rankings by average relaxation energy; if the ordering changes, or if a molecule accepted by the aromatic-dependent valency table is found experimentally or computationally unstable, the corrected benchmark's conclusions would be overturned. A simpler check is whether the valency table reproduces all stable valency patterns in an independent large drug-like dataset.

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Extended reading notes

Core claim

The paper's central claim is that the 'molecular stability' scores reported for GEOM-Drugs generative models are artificially high and chemically unreliable. The evidence is diagnostic: fixing the aromatic-bond rounding bug alone drops stability from roughly 0.94–0.99 to roughly 0.45–0.63, while replacing the lookup table with one that records each atom by element, formal charge, number of aromatic bonds, and non-aromatic bond order produces scores only 1–3 points below the published ones. The same framework then computes GFN2-xTB relaxation energies and internal-coordinate displacements, which show that diffusion-based models already outperform MMFF-optimized structures in alignment with the reference energy landscape. On that benchmark a consistent performance gap between flow-matching and diffusion-based models appears, even when the architecture family is the same. The paper frames the result as a call to abandon inherited buggy metrics and to adopt chemically grounded preprocessing and evaluation.

Load-bearing premise

The benchmark assumes GFN2-xTB is an accurate enough energy model for drug-like molecules that relaxing to a GFN2-xTB minimum is a meaningful measure of generated-molecule quality; if GFN2-xTB misjudges the relevant chemistry, the corrected scores and rankings could change.

Editorial extensions

If this is right

  • Published stability numbers for models using the MiDi-style valency code should be read as upper bounds; the corrected aromatic-dependent lookup table lowers them by 1–3 percentage points.
  • Retraining on a fully kekulized GEOM-Drugs version raises connected validity by about 5% for four of the six evaluated models, so the representation of aromatic bonds affects what the stability metric rewards.
  • MMFF-based relaxation benchmarks are no longer suitable for ranking GEOM-Drugs models, since current models align closer to GFN2-xTB than MMFF does; MMFF remains useful only as a coarse structural-outlier filter.
  • GFN2-xTB energy and geometry scoring reveals a flow-matching versus diffusion gap that did not surface under older metrics, so future benchmark comparisons should report relaxation energy and internal-coordinate displacements.

Reading between the lines

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

  • Inference: the same aromatic-dependent valency treatment is likely needed on any aromatic-rich benchmark; a valency table learned only from GEOM-Drugs may not cover rare but viable chemistries in broader drug space.
  • Inference: if GFN2-xTB relaxation energy is accepted as a quality signal, it could plausibly be used as a training-time objective or selection criterion for generated molecules, which the paper does not itself test.
  • Inference: a testable next step is to run the same benchmark with a higher-level reference such as DFT on a subset of generated molecules; stable rankings across levels of theory would validate GFN2-xTB as a cheap benchmark standard.
  • Inference: because the corrected stability metric drops scores by only 1–3 points for most models, small differences in future reported stability values should be treated cautiously unless the full uncertainty is reported.
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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. This paper revisits the GEOM-Drugs benchmark for 3D molecule generation and argues that current evaluation protocols overstate model quality. The authors identify a bond-order counting bug in the widely used MiDi stability implementation (aromatic bonds rounded to 1 instead of 1.5), show that the associated valency lookup tables contain chemically implausible entries, and document that these errors propagated to several subsequent models. They propose an aromatic-dependent valency table, a kekulized and filtered version of GEOM-Drugs, and a GFN2-xTB-based geometry and energy benchmark, and they retrain or re-evaluate six models. The main quantitative claims are that corrected stability scores are 1-3% lower than originally reported, that retraining on kekulized data improves validity for most models, and that GFN2-xTB evaluations reveal a consistent flow-matching versus diffusion performance gap.

Significance. The paper's bug analysis and valency-table corrections are concrete and likely to be useful to the field; if adopted, the proposed pipeline would remove a known numerical artifact from a widely used metric. The authors also release a filtered dataset, processing scripts, and an implementation of the corrected stability metric, which strengthens reproducibility. The GFN2-xTB benchmark is a sensible response to the mismatch between MMFF-based evaluation and GFN2-xTB-optimized reference data, and the MMFF-to-GFN2-xTB baseline row in Table 2 neatly illustrates why that mismatch matters. However, the paper's headline comparative finding about flow-matching versus diffusion is not established by the reported experiments, because the relevant models differ in capacity and sampling budget.

major comments (4)
  1. [GFN2-xTB energy-based geometry benchmark, Table 2] The claim that corrected metrics reveal 'a consistent performance gap between flow-matching and diffusion-based models—even when the underlying architecture remains the same' is not supported by the reported experiments. The table caption states that diffusion models use 500 sampling steps while flow-matching models use 100 steps, and the text states that Megalodon-flow is a smaller 'quick' network and that SemlaFlow is a retrained variant. The 8-fold difference in mean Delta_E_relax between Megalodon (5.76 kcal/mol) and Megalodon-flow (46.9 kcal/mol), and the elevated SemlaFlow value (91.0 kcal/mol), could therefore be explained by model capacity or inference-time compute rather than by the generative paradigm. The authors should either benchmark the models at matched sampling budgets and comparable capacities, or explicitly withdraw the paradigm-level interpretation.
  2. [GFN2-xTB energy-based geometry benchmark, around Table 2] The choice of GFN2-xTB as the reference energy is justified only by the fact that GEOM-Drugs geometries were optimized with this method. Since generated molecules are scored by their relaxation energy and structural displacement to GFN2-xTB minima, the benchmark implicitly assumes that GFN2-xTB is an adequate description of the energy landscape for all generated chemistries. The paper does not check whether generated molecules have the same formal-charge or protonation-state distribution as the GEOM-Drugs reference, and it does not report any validation of GFN2-xTB against a higher-level method on a sample of generated molecules. Without such checks, the energy ranking in Table 2 is conditional on a reference model that may penalize valid but out-of-distribution chemistry.
  3. [Table 1 and footnotes] The claim that 'the relative rankings of the models remained largely consistent' is weakened by the fact that the table compares several modified variants rather than the original published models: JODO was retrained with the EQGAT-Diff objective using categorical diffusion, and Megalodon-flow is a retrained 'quick' variant that differs from the larger model in the original paper. The corrected numbers for these rows are therefore not direct re-evaluations of the models that originally reported the flawed metrics. The authors should either obtain or train the original configurations for these rows, or restrict the ranking-preservation claim to the models for which a direct comparison is available.
  4. [Solution and Table 5] The aromatic-dependent valency table is derived only from the filtered GEOM-Drugs set, and any tuple (element, number of aromatic bonds, formal charge, v_other) that is absent from the table is treated as invalid. The paper does not report what fraction of the 5,000 generated molecules per model contain atoms whose bonding environment is absent from Table 5, nor does it analyze whether those atoms are chemically reasonable. This matters because the reported 1-3% drop in stability under the corrected table could conflate genuine chemical invalidity with incomplete coverage of the lookup table. Reporting per-tuple coverage would make the metric's behavior transparent.
minor comments (4)
  1. [Section 'Solution', p. 7] 'their is no explicit modeling' should be 'there is no explicit modeling'; similarly, p. 11 contains 'semi-emperical' which should be 'semiempirical'.
  2. [Section 'Solution', cross-reference] The text refers to 'SI Table 5', but the table appears in Appendix I of the main supplement rather than in a separately numbered SI file; the cross-reference should be updated.
  3. [Table 5] The formatting of Table 5 is hard to read: entries such as '2, 1' for carbon with two aromatic bonds and charge 0 are not explained, so the reader cannot tell whether commas separate alternative allowed values or components of a tuple. A sentence defining the cell format would remove this ambiguity.
  4. [Repository link] The GitHub link should be versioned (for example, with a commit hash or release tag) so that the exact filtering scripts and valency tables can be cited reproducibly.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity found: the corrected valency tables and GFN2-xTB benchmarks are empirical, externally anchored evaluation criteria, not predictions derived from their own inputs.

full rationale

The paper's derivation chain is self-contained and empirically grounded rather than circular. (1) The valency-lookup corrections are explicitly empirical: valid valencies are 'defined as valencies observed in the training data,' and the refined (element, number of aromatic bonds, formal charge, valency) table is constructed from the filtered GEOM-Drugs set. Using a training-set-derived lookup table as an evaluation criterion is standard benchmarking practice; it is not a fitted parameter that is then renamed as a prediction. (2) The GFN2-xTB energy benchmark is anchored to an external reference—the GEOM-Drugs dataset was itself optimized with GFN2-xTB—so the benchmark choice is conditional on the dataset's documented generation protocol, not on the authors' models or fitted values. The reported relaxation energies and geometric displacements are computed from actual GFN2-xTB optimizations, not from the benchmark definition itself. (3) The comparisons against previously published models are re-evaluations on generated molecules; the stability scores in Table 1 change because the bugs and table entries differ, but this is a measurement correction, not a result forced by construction. (4) Self-citations to Megalodon (ref. 13), FlowMol (refs. 11–12), and SemlaFlow (ref. 9) are used as models under test, not as load-bearing evidence for the corrected framework; the correction arguments rest on chemical reasoning, RDKit-based kekulization, and the GFN2-xTB provenance of the dataset, not on these citations. The only substantive caveat—Table 2's unequal sampling budgets (500 diffusion steps vs. 100 flow-matching steps) and the smaller 'quick' Megalodon-flow architecture—is a confound for the flow-versus-diffusion gap claim, not a circularity, because it does not make any equation or metric equivalent to its own input. Accordingly, no circular step meets the evidentiary bar of exhibiting a specific reduction, and the appropriate finding is no significant circularity.

Assumptions & free parameters 1 free parameters · 4 assumptions · 0 invented entities

The central evaluation depends on the dataset-derived valency table and on GFN2-xTB as the reference energy landscape. These are not fitted to the target results, but they are chosen by the authors and inherited from the data generation protocol, so they function as domain assumptions rather than derived facts. The free parameter listed is the valency lookup table, which is fitted to the filtered training distribution and used to decide stability.

free parameters (1)
  • Aromatic-dependent valency table (Table 5) = Observed (n_arom, v_other) tuples per element and formal charge in the filtered GEOM-Drugs set
    Used as the ground truth for molecular stability. Because the table is built from the same dataset used for training and evaluation, the stability metric measures agreement with the training distribution rather than with an independent chemical standard. This is standard practice but means the metric cannot flag systematic errors present in the training data.
assumptions (4)
  • domain assumption GFN2-xTB is an appropriate reference for evaluating drug-like molecule geometries and energies, and the GEOM-Drugs conformers are correctly optimized minima of this functional.
    Central to the proposed energy benchmark; the paper justifies it by noting the dataset was optimized with GFN2-xTB, but this does not guarantee the functional is chemically accurate for all generated structures.
  • domain assumption Formal charges are known or consistently assigned for generated molecules when running GFN2-xTB relaxation.
    The paper does not describe how formal charges are assigned to model outputs; GFN2-xTB requires a charge input, and errors here would propagate into relaxation energies.
  • domain assumption All remaining GEOM-Drugs molecules (after removing 0.18% fragmented ones) are chemically valid and their valencies are the correct ground truth.
    The valency lookup table is derived from this filtered set; if the dataset contains additional systematic errors (e.g., incorrect protonation or bond orders), the stability metric would inherit them.
  • domain assumption Kekulization of all molecules in the prepared dataset is chemically unambiguous and does not alter molecular identity.
    The kekulized dataset is used for retraining; if kekulization changes tautomers or resonance forms, models may be evaluated on different chemistry than the original data.

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Cite this review

Pith. "Pith review of GEOM-Drugs Revisited: Toward More Chemically Accurate Benchmarks for 3D Molecule Generation." pith.science (2026). https://pith.science/paper/3HQEMKON

@misc{pith2026250500169,
  author       = {Pith},
  title        = {Pith review of: GEOM-Drugs Revisited: Toward More Chemically Accurate Benchmarks for 3D Molecule Generation},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/3HQEMKON}},
  note         = {Machine review of arXiv:2505.00169}
}
read the original abstract

Deep generative models have shown significant promise in generating valid 3D molecular structures, with the GEOM-Drugs dataset serving as a key benchmark. However, current evaluation protocols suffer from critical flaws, including incorrect valency definitions, bugs in bond order calculations, and reliance on force fields inconsistent with the reference data. In this work, we revisit GEOM-Drugs and propose a corrected evaluation framework: we identify and fix issues in data preprocessing, construct chemically accurate valency tables, and introduce a GFN2-xTB-based geometry and energy benchmark. We retrain and re-evaluate several leading models under this framework, providing updated performance metrics and practical recommendations for future benchmarking. Our results underscore the need for chemically rigorous evaluation practices in 3D molecular generation. Our recommended evaluation methods and GEOM-Drugs processing scripts are available at https://github.com/isayevlab/geom-drugs-3dgen-evaluation.

Figures

Figures reproduced from arXiv: 2505.00169 by the authors.

Figure 1
Figure 1. An example of a molecule where the assumption that aromatic bonds contribute [PITH_FULL_IMAGE:figures/full_fig_p005_1.png] view at source ↗
Figure 2
Figure 2. Examples of molecules that pass the molecular stability test under commonly [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 3
Figure 3. Examples from GEOM-Drugs where GFN2-xTB failed and resulted in fractured [PITH_FULL_IMAGE:figures/full_fig_p021_3.png] view at source ↗

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Forward citations

Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. VEDA: 3D Molecular Generation via Variance-Exploding Diffusion with Annealing

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    VEDA generates 3D molecules with VE diffusion plus LMMSE preconditioning and an arcsin scheduler, reaching near-relaxed geometries with 100 sampling steps.

  2. FlowMol3: Flow Matching for 3D De Novo Small-Molecule Generation

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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...

Reference graph

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