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REVIEW 4 major objections 4 minor 78 references

Boltz-ABFE: Free Energy Perturbation without Crystal Structures

T0 review · 4 major / 4 minor · reviewed 2026-08-05 · deepseek-v4-flash

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Pith's one-line read This paper shows that absolute binding free-energy calculations can run from predicted protein-ligand structures, with mean signed errors under 1 kcal/mol across four kinase targets, removing the need for experimental crystal structures in

desk verdict Useful pipeline demonstration, but the 'without crystal structures' headline overreaches: for CDK2 the pipeline needs the cyclin partner, which is structural knowledge the paper doesn't automate. read the letter →

arxiv 2508.19385 v1 pith:HLF65HDG submitted 2025-08-26 physics.comp-ph

Stephan Thaler , Zhiyi Wu , William G. Glass , Richard T. Bradshaw , Prudencio Tossou , Geoffrey P. F. Wood This is my paper · ORCID
classification physics.comp-ph
keywords absolutebindingfreeenergyBoltz-2co-foldingstructurepredictionperturbationprotein-liganddockingre-dockingdrugdiscoverymoleculardynamics
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

The paper tries to establish that rigorous absolute binding free energy (ABFE) calculations—the gold-standard physics-based way to estimate how tightly a small molecule binds a protein—can start from computer-predicted protein-ligand structures instead of experimental crystal structures. It builds a pipeline that uses the Boltz-2 co-folding model to predict the complex from sequence and SMILES, repairs common prediction defects such as steric clashes, wrong bond orders, aromaticity, and stereochemistry via multi-model sampling and re-docking, then runs alchemical free energy simulations. On four kinase targets from a public benchmark, ABFE values initiated from predicted structures achieved mean unsigned errors below 1 kcal/mol, essentially matching simulations started from crystal structures. If this holds, free energy perturbation can be applied earlier in drug discovery, before crystals exist, expanding its domain of applicability.

What carries the argument

The load-bearing mechanism is the coupling of a co-folding structure predictor (Boltz-2) with an alchemical absolute binding free energy (ABFE) protocol, mediated by structure repair. ABFE is a simulation in which the ligand is annihilated in the bound and unbound states and the free energy difference yields the binding affinity; the repair step—re-docking the ligand into the predicted pocket with POSIT, a shape- and pharmacophore-guided docking method—corrects ligand chemistry errors (bond orders, aromaticity, stereochemistry) that would otherwise poison the simulation. Supporting machinery includes sampling multiple Boltz models per complex to escape steric clashes, truncating low-confiden

What would settle it

Run the pipeline on a new protein target with no experimental complex and no known binding partner, using the untailored full UniProt sequence; if the mean unsigned error against measured affinities exceeds 1 kcal/mol, or if ABFE values diverge sharply when a binding partner is later added, the claim that crystal-free predictions are generally sufficient would be refuted.

Watch

Extended reading notes

Core claim

On the paper's own terms, the central claim, stated in Section 2.2, is that 'the ABFE results initiated from any of the Boltz predicted structures achieved satisfactory results with MUE < 1 kcal/mol on average.' The authors interpret this as demonstrating the feasibility of absolute FEP simulations without experimental crystal structures. The demonstration rests on a preparation step: Boltz predictions contain systematic chemical errors, and re-docking with the template-guided POSIT method fixes them; truncating low-confidence sequence regions and including binding partners (e.g., cyclin for CDK2) are needed to obtain pockets that match biology. The paper also shows that a top-down trained a

Load-bearing premise

The load-bearing premise is that the biological context needed for an accurate co-folding prediction—which binding partners or sequence regions to include—can be determined without consulting experimental structures; the CDK2 case shows this is currently a manual choice.

Editorial extensions

If this is right

  • ABFE simulations can reach gold-standard accuracy (MUE < 1 kcal/mol) without an experimental complex, at least for well-behaved kinase targets.
  • The re-docking correction is what makes Boltz-1 predictions usable; Boltz-2's inference-time steering removes most clashes and chemistry errors but still leaves stereochemistry errors, so re-docking remains valuable.
  • For targets with flexible or partner-dependent binding sites, including binding partners in the co-folding input is essential; omitting them can create artificial pockets and extended ligand poses that degrade affinity predictions.
  • Classical scoring of co-folded poses can filter wrong targets when targets are structurally distant (AUC up to 0.91), but cannot resolve selectivity among similar off-targets (AUC about 0.62), so affinity simulations or better methods are needed for target deconvolution.
  • The sensitivity of ABFE to the starting structure offers an affinity-based benchmark for co-folding models that does not require crystal structures.

Reading between the lines

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

  • If these results generalize, the next bottleneck is not the simulation but the automation of biological context: deciding which binding partners, co-factors, or modified termini to feed into the structure predictor. The paper's CDK2 case suggests this is currently a manual choice; an automated rule would be needed for a truly crystal-free workflow.
  • The same pipeline could be pointed at hit identification rather than lead optimization, where absolute values across different proteins matter; the paper notes the offset problem but does not solve it.
  • A broader implication is a new evaluation loop: predicted complexes can be scored by how well they support ABFE convergence, turning affinity measurements into structural-validation data.
  • The competitive performance of the top-down affinity module on kinases hints that purely learned affinity models may dominate on well-represented protein families, while physics-based ABFE should be favored on novel targets—an easily testable trade-off across target families.
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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 paper presents Boltz-ABFE, a pipeline that combines Boltz-1/Boltz-2 protein-ligand co-folding predictions with a previously developed absolute binding free energy (ABFE) protocol, aiming to perform rigorous affinity calculations without experimental crystal structures. The pipeline uses ligand SMILES and protein sequence(s) as inputs, generates multiple Boltz models, corrects ligand chemistry by re-docking with OpenEye POSIT, prepares the complex with Spruce and BioSimSpace, and runs alchemical ABFE simulations with GROMACS and alchemlyb. The method is tested on four kinase targets from the FEP+ benchmark (TYK2, CDK2, JNK1, P38) with three replicates, reporting average MUE below 1 kcal/mol for Boltz-seeded simulations, comparable to crystal-structure-seeded results. The paper also explores Boltz-1 vs Boltz-2 failure modes, target deconvolution via classical scoring, truncation of low-confidence regions, and the importance of including binding partners (e.g., cyclin for CDK2) during co-folding.

Significance. If the central claim is sustained, the work would be a useful step toward extending FEP to early drug discovery stages where crystal structures are unavailable. The paper's strengths include a clearly specified pipeline built with open tools, a benchmark against experimental affinities, triplicate ABFE replicates, and explicit discussion of structure-quality failure modes. The paper also acknowledges important limitations, including a speculative side-chain-flip explanation and the need for future work on cofactors and apo-state offsets. However, the evidence base is narrow: four well-studied kinases, with one target (TYK2/Boltz-2) exceeding the 1 kcal/mol MUE threshold, and no demonstrated automation of biological-assembly choices. As a result, the headline claim that ABFE can be performed 'without crystal structures' is plausible but not yet convincingly demonstrated in the strong sense advertised.

major comments (4)
  1. [Sec. 2.1.3 / Fig. 7] The headline claim 'without experimental crystal structures' (Abstract, Sec. 2.1.1) is not established for systems that require biological-assembly knowledge. For CDK2, Boltz-2 alone predicts an artificially expanded pocket and an extended ligand pose; including CyclinE1 is necessary to restore the native pocket and improve ABFE (Figs. 7C and 7F). The decision to include the cyclin partner is not derived from the ligand SMILES and CDK2 sequence by any automated step in the pipeline; it is external biological knowledge typically obtained from crystallography, homology, or prior experiment. The four-target benchmark (Sec. 2.2, Fig. 8) does not expose this dependency because the correct assemblies for TYK2/CDK2/JNK1/P38 are already known. To support the central claim, the authors should either provide an automated, sequence-derived rule for selecting partner chains and validate it on less-c
  2. [Sec. 2.2 / Fig. 8] The 'MUE < 1 kcal/mol on average' claim is an average over four targets; the TYK2/Boltz-2 condition exceeds 1 kcal/mol, as acknowledged in the text. With only four targets, a single outlier represents 25% of the benchmark, and the three-replicate error bars do not propagate experimental affinity uncertainties. Without such propagation, the MUE/RMSE comparisons lack a statistically grounded uncertainty estimate. The proposed side-chain-flip explanation for the TYK2 Boltz-2 discrepancy is also speculative: no structural overlay, per-residue analysis, or alternative-model ABFE calculation is provided to causally link the flipped side chain to the computed error. The authors should report per-target errors with experimental error propagated and either test the side-chain hypothesis or clearly label it as a hypothesis requiring further study.
  3. [Sec. 2.1.1 / Sec. 2.2] The choice of POSIT over Hybrid re-docking is made based on ABFE results on TYK2 (SI Table S1), and TYK2 is then included in the benchmark set reported in Sec. 2.2. This is protocol selection on the test set, which can inflate the apparent performance for TYK2 and potentially for the average. To make the benchmark clean, the authors should either select the docking protocol using a separate validation set or an oracle not involving the reported targets, or explicitly state that TYK2 serves as a training target for the pipeline choice. The current presentation conflates tuning and evaluation, weakening the generalizability claim.
  4. [Sec. 4.1 / Intro] The paper claims that the targets include structures the models have not seen before (Introduction), but no training-containment analysis is presented. The four FEP+ benchmark targets are public and well studied, and Boltz-2 is developed by the same group (Ref. 51), so the reader cannot rule out that these complexes or close homologs are in the training data. This matters because the central claim is about generalization to uncharacterized targets. The authors should provide evidence of training-set exclusion (e.g., sequence/structure similarity analysis to Boltz-2 training data, or validation on recently deposited structures such as 9OB2–9OB6). Without this, the benchmark may be optimistic.
minor comments (4)
  1. [Sec. 2.2] Typo: 'RSME' in the text should be 'RMSE'.
  2. [Fig. 5 caption] The caption contains '(Figure 5G, G)' where the second reference appears to be a typo (likely 'Figure 5F, G').
  3. [Sec. 2.2] The phrase 'MUE ′s > 1 kcal/mol' contains an awkward apostrophe; suggest 'MUE values > 1 kcal/mol'.
  4. [Sec. 2.2] The sentence describing Boltz-1+P performance relative to crystal-structure results is difficult to parse ('yielding a modest increase of 0.1 kcal/mol in mean unsigned error compared to the crystal structure results on average'). Please clarify whether this is an increase or decrease in error, and relative to which condition.

Circularity Check

0 steps flagged · score 2.0 of 10

No significant circularity: ABFE results are benchmarked against external experimental affinities; self-citations are not load-bearing.

full rationale

The paper's derivation chain is: Boltz-1/2 co-folded complex -> redocking/relaxation -> alchemical ABFE -> comparison with experimental ΔG. The final comparison is against external affinity measurements (FEP+ benchmark, Figure 8), not against any quantity used to build the model or set the protocol. No ABFE value is obtained by fitting or inverting the experimental data; the free energy is computed from an MD alchemical transformation (Section 4.4). The main self-citations (Boltz-2, ref 51; ABFE protocol, refs 55/73) are to previously published tools/protocols from the same group, but they are used as executable methods and their output is evaluated here against experiment, so the citations are not load-bearing unverified premises. The selection of POSIT over Hybrid was informed by a TYK2 pilot ABFE comparison, and TYK2 is then included in the headline benchmark; this is a mild selection/validation-leak concern about the reported average, but it is not an equation-level reduction of the reported free energies to the selection criterion. Similarly, the manual inclusion of binding partners (e.g., cyclin for CDK2) is a scope limitation for the 'without crystal structures' claim, not a circularity: the subsequent ABFE calculation still has independent physical content. Overall, no circular step is identifiable from the paper's own equations or construction.

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

No free parameters are fitted in this paper; the protocol settings are taken from prior work or chosen as defaults. The axioms listed are the load-bearing domain assumptions from prior literature and from the paper's own pipeline choices.

assumptions (4)
  • domain assumption The ABFE protocol of Wu et al. (refs 55/73) is accurate for kinase targets when initialized from near-native structures.
    The paper uses it as the baseline and compares Boltz-initialized runs against crystal-initialized runs; the protocol itself is not re-validated here.
  • domain assumption Experimental binding data in the FEP+ benchmark (refs 4,35) are accurate ground truth.
    Used as labels for MUE/RMSE/correlation calculations; experimental error is not propagated.
  • domain assumption Boltz-1/2 co-folding can produce a relevant binding pocket without experimental structures once sequence and biological context are supplied.
    Central to the pipeline; the 4 targets and cross-docking sets provide evidence, but generalization is assumed.
  • domain assumption POSIT re-docking with the Boltz template preserves the near-native pose while correcting chemistry errors.
    The paper selects POSIT based on TYK2 only (SI Table S1) and then applies it to all targets.

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Cite this review

Pith. "Pith review of Boltz-ABFE: Free Energy Perturbation without Crystal Structures." pith.science (2026). https://pith.science/paper/HLF65HDG

@misc{pith2026250819385,
  author       = {Pith},
  title        = {Pith review of: Boltz-ABFE: Free Energy Perturbation without Crystal Structures},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/HLF65HDG}},
  note         = {Machine review of arXiv:2508.19385}
}
read the original abstract

Free energy perturbation (FEP) is considered the gold-standard simulation method for estimating small molecule binding affinity, a quantity of vital importance to drug discovery. The accuracy of FEP critically depends on an accurate model of the protein-ligand complex as an initial condition for the underlying molecular dynamics simulation. This requirement has limited the impact of FEP in earlier stages of the discovery process, where appropriate experimental crystal structures are rarely available. The latest generation of structure prediction models, such as Boltz-2, promise to overcome this limitation by predicting protein-ligand complex structures. In this work, we combine Boltz-2 with our own absolute FEP protocol to build Boltz-ABFE, a robust pipeline for estimating the absolute binding free energies (ABFE) in the absence of experimental crystal structures. We investigate the quality of the structures predicted by Boltz-2, propose automated approaches to improve structures for use in molecular dynamics simulations, and demonstrate the effectiveness of the Boltz-ABFE pipeline for four protein targets from the FEP+ benchmark set. Demonstrating the feasibility of absolute FEP simulations without experimental crystal structures, Boltz-ABFE significantly expands the domain of applicability of FEP, paving the way towards accelerated early-stage drug discovery via accurate, structure-based affinity estimation.

Figures

Figures reproduced from arXiv: 2508.19385 by the authors.

Figure 1
Figure 1. The Boltz-ABFE pipeline uses Boltz-1/Boltz-2 to predict the protein-ligand com [PITH_FULL_IMAGE:figures/full_fig_p005_1.png] view at source ↗
Figure 2
Figure 2. Common chemical inaccuracies in Boltz-1-generated ligand structures and the [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 3
Figure 3. Comparison of success rates for the Boltz-1 and Boltz-2 models in generating [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗
Figures from the paper (5 more)
Figure 4
Figure 4. Figure 4: Receiver Operator Characteristic (ROC) plots for correct target deconvolution [PITH_FULL_IMAGE:figures/full_fig_p010_4.png]
Figure 5
Figure 5. Figure 5: Truncating low-confidence regions improves model confidence scores and structural [PITH_FULL_IMAGE:figures/full_fig_p011_5.png]
Figure 6
Figure 6. Figure 6: Schematic representation of ligand binding conformations in the presence and ab [PITH_FULL_IMAGE:figures/full_fig_p012_6.png]
Figure 7
Figure 7. Figure 7: (A) RMSD of ligand heavy atoms, pocket heavy atoms, pocket C [PITH_FULL_IMAGE:figures/full_fig_p013_7.png]
Figure 8
Figure 8. Figure 8: (A) Root mean square error (RMSE) between calculated and experimental ∆G [PITH_FULL_IMAGE:figures/full_fig_p015_8.png]

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Pith tools

Reviewed August 5, 2026 · model on record in the stance chip above.