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arxiv: 2605.08767 · v1 · submitted 2026-05-09 · 💻 cs.AI

Recognition: 2 theorem links

· Lean Theorem

From Holo Pockets to Electron Density: GPT-style Drug Design with Density

Bing Su, Bo Huang, Jiahao Chen, Letian Gao, Wenbiao Zhou, Yanhao Zhu, Zhi John Lu

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Pith reviewed 2026-05-12 02:42 UTC · model grok-4.3

classification 💻 cs.AI
keywords structure-based drug designelectron densityautoregressive generationde novo molecule designED point cloudsgenerative modelingmolecular conformation
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The pith

EDMolGPT generates drug molecules autoregressively from low-resolution electron density point clouds rather than rigid protein pockets.

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

The paper argues that low-resolution electron density from complete holo complexes, including the filler molecules and solvent, supplies a more natural conditioning signal for de novo molecule generation than the conventional empty-pocket approach. This density is obtained either computationally or directly from cryo-EM and X-ray experiments, allowing the same model to train on both simulated and real data. The authors introduce EDMolGPT, a decoder-only autoregressive model that takes ED point clouds as input and outputs molecules together with their 3D conformations. By anchoring generation in physical density rather than geometric pocket boundaries, the method reduces structural bias and produces conformations that respect the actual binding environment. Large-scale tests on 101 biological targets are presented as evidence that the approach works in practice.

Core claim

We introduce EDMolGPT, a decoder-only autoregressive framework that generates molecules from low-resolution ED point clouds. By grounding generation in physically meaningful density signals derived from holo complexes, the model mitigates structural bias and produces molecules with appropriate 3D conformations, as verified through evaluations on 101 biological targets.

What carries the argument

EDMolGPT, a decoder-only autoregressive transformer that converts low-resolution electron density point clouds into molecular structures and 3D poses.

Load-bearing premise

Low-resolution electron density extracted from holo complexes including the filler supplies a more faithful and flexible description of the binding site than rigid empty-pocket representations.

What would settle it

On the 101-target benchmark, if EDMolGPT-generated molecules show no improvement in validity, 3D pose accuracy, or experimental binding metrics over pocket-conditioned baselines, or if the generated structures fail to align with the input density maps, the central claim would be refuted.

Figures

Figures reproduced from arXiv: 2605.08767 by Bing Su, Bo Huang, Jiahao Chen, Letian Gao, Wenbiao Zhou, Yanhao Zhu, Zhi John Lu.

Figure 1
Figure 1. Figure 1: Comparison between pocket-based drug design (blue￾circled region) and our electron density (ED)-based drug design framework (green-circled region). The red dots denote the solvent. Filler is defined as all elements within a 4.5A˚ radius of the ligand, excluding the binding pocket. 1. Introduction AI-driven drug design has emerged as a powerful paradigm for generating molecules that selectively bind biologi… view at source ↗
Figure 2
Figure 2. Figure 2: Experimental ED reflects conformational dynamics of a filler in a protein pocket (PDB ID: 6KMP). The experimental ED map is shown as blue mesh, representing the ensemble-averaged electron density derived from X-ray diffraction. Protein atoms are shown as green sticks. The ligand is shown in yellow and purple sticks, with colors corresponding to alternative conformations resolved in the density, indicative … view at source ↗
Figure 3
Figure 3. Figure 3: The overall pipeline of our method. The components shown with a green background correspond to the generation of 3D point clouds from the input ligand. The blue-highlighted components represent the molecule generation process, where each molecular token is predicted sequentially based on the point cloud tokens and the previously generated molecular tokens. Finally, the steps highlighted in yellow illustrat… view at source ↗
Figure 4
Figure 4. Figure 4: Difference between training (blue) and inference (green): ED during training is derived from the ligand, while ED during inference incorporates solvent(red dots). for training as Pbm. During training, we concatenate the point cloud and molecule sequences and feed them into ED￾MolGPT to predict the molecule token-by-token. Formally, after acquiring the discretized point cloud Pbm and the corre￾sponding mole… view at source ↗
Figure 5
Figure 5. Figure 5: The comparison between ED2Mol and EDMolGPT on QED, SAS, and Molecule Weight. We split QED and SAS into several bins and report the (a) Percentage of Samples by QED and SAS Bins and (b) Average Molecule Weight by QED and SAS Bins [PITH_FULL_IMAGE:figures/full_fig_p008_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Generation from ED. Left: reproduction of the original ligand from ED. Right: a newly generated molecule that overlaps with the rigid pocket yet remains active. and. The resulting molecule exhibits confirmed bioactivity, underscoring the limitations of rigid docking evaluations. More analyses are provided in Appendix Sec. B. 4.4. Ablation studies Ablations studies on resolution dmin To robustly enable our … view at source ↗
Figure 7
Figure 7. Figure 7: The generation pipeline leverages two distinct inputs: the static crystallographic pocket (PDB ID: 3L1N) and low-resolution electron density. (a) A ligand recovered by both structure- and density-guided approaches. (b) A ligand exclusively generated through electron density guidance. The region highlighted in purple illustrates an apparent steric clash between the generated phenyl moiety and the rigid conf… view at source ↗
Figure 8
Figure 8. Figure 8: Comparison of ED intensity distributions across different calculation methods. From left to right: (a) ExpED (b) CalED "c_0", "c_5", "c_6", "c_10", "c_11", "c_12", "N_0", "N_5", "N_6", "N_10", "N_11", "N_12", "n_0", "n_5", "n_6", "n_10", "n_11", "n_12", "S_0", "s_0", "s_5", "s_6", "s_10", "s_11", "s_12", "O_0", "O_5", "O_6", "O_10", "O_11", "O_12", "o_0", "o_5", "o_6", "+_0", "o_11", "o_12", "F_0", "Cl_0",… view at source ↗
Figure 9
Figure 9. Figure 9: The comparison between (a) FSMILES and (b) Ours. We highlight the cut bonds in red, and the tokenized result is marked below. First, the choice of resolution σ = 0.1 A˚ reflects a trade-off between geometric fidelity and vocabulary size. With this setting, the maximum quantization error per coordinate dimension is 0.05 A˚ , which is negligible compared to the typical bond length in organic molecules (∼ 1.2… view at source ↗
Figure 10
Figure 10. Figure 10: The visualization results on (a) Fragment Atom Count Distribution and (b) Fragment Molecular Weight Distribution. C.3. Relative distance To determine the reference atoms required for autoregressive coordinate generation, we design a procedure to trace the ancestral nodes of each token in the molecular sequence M = {a 1 m, a2 m, . . . , an m}. For a given step i, we define three levels of ancestor indices:… view at source ↗
Figure 11
Figure 11. Figure 11: DICE similarity scores between DUD-E active ligands and their closest counterparts in the training dataset, sorted from high to low. Each point corresponds to a DUD-E ligand, with the horizontal axis indicating the PDB ID and the vertical axis showing the maximum DICE score identified in the training set. The results indicate that all maximum DICE scores remain below 60%. the pocket environment. This comb… view at source ↗
Figure 12
Figure 12. Figure 12: Visualization of three protein–ligand complexes with PDB IDs 1sj0, 3lan, and 2etr. The first column shows the point cloud extracted from the electron density map. The second column presents the ground-truth ligand conformations within the corresponding protein pockets. The following three columns (Case 1–3) display ligands generated by our method, with the associated minimum in-place docking scores indica… view at source ↗
read the original abstract

Recent advances in generative modeling have enabled significant progress in structure-based drug design (SBDD). Existing methods typically condition molecule generation on empty binding pockets from holo complexes, overlooking informative components such as the filler (ligands and solvent). Here, we leverage low-resolution electron density (ED) derived from the filler as a physically grounded condition for \textit{de novo} drug design. We consider two types of ED, calculated and cryo-EM/X-ray, obtainable from computational or experimental sources, supporting unified pre-training and experimental integration. Compared with rigid pocket representations, experimental ED naturally captures conformational flexibility and provides a more faithful description of the binding environment. Based on this, we introduce EDMolGPT, a decoder-only autoregressive framework that generates molecules from low-resolution ED point clouds. By grounding generation in physically meaningful density signals, EDMolGPT mitigates structural bias and produces molecules with 3D conformations. Evaluations on 101 biological targets verify the effectiveness. Our project page: https://jiahaochen1.github.io/EDMolGPT_Page/.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The manuscript introduces EDMolGPT, a decoder-only autoregressive generative model for de novo molecule design in structure-based drug design (SBDD). It conditions generation on low-resolution electron density (ED) point clouds derived from holo protein complexes, explicitly including the ligand and solvent ('filler') rather than empty rigid pockets. The approach supports both computationally calculated and experimental (cryo-EM/X-ray) ED for unified pre-training and claims that this physically grounded representation better captures conformational flexibility. Effectiveness is asserted via evaluations on 101 biological targets.

Significance. If the empirical claims hold under rigorous controls, the shift from pocket-based to ED-conditioned generation could provide a more faithful and flexible binding-site description, enabling better integration of experimental structural data and potentially reducing structural bias in generated molecules. The unified handling of calculated and experimental ED is a conceptual strength that aligns computational SBDD with real-world structural biology inputs.

major comments (2)
  1. [Abstract] Abstract: The central claim that 'evaluations on 101 biological targets verify the effectiveness' is unsupported because the abstract (and by extension the manuscript's empirical section) supplies no quantitative metrics (validity, novelty, uniqueness, docking scores, or 3D pose RMSD), no baselines (pocket-conditioned autoregressive or diffusion SBDD models), no ablations isolating ED conditioning from the GPT-style decoder, and no error analysis. This renders the verification of the core advantage over rigid-pocket methods untestable.
  2. [Abstract] Abstract / §4 (assumed results section): The weakest assumption—that low-resolution ED including filler yields a 'more faithful description of the binding environment' enabling superior generation—is not load-bearing tested. No head-to-head comparison on identical targets and metrics against standard pocket representations (with matched architecture and training) is described, leaving open whether any observed plausibility stems from the ED signal or from the autoregressive framework itself.
minor comments (1)
  1. [Abstract] Abstract: The phrase 'produces molecules with 3D conformations' is unclear without specifying whether the output includes explicit 3D coordinates, conformer ensembles, or only 2D graphs with implicit geometry.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on EDMolGPT. We address each major comment below and have made revisions to strengthen the empirical presentation and comparisons in the manuscript.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The central claim that 'evaluations on 101 biological targets verify the effectiveness' is unsupported because the abstract (and by extension the manuscript's empirical section) supplies no quantitative metrics (validity, novelty, uniqueness, docking scores, or 3D pose RMSD), no baselines (pocket-conditioned autoregressive or diffusion SBDD models), no ablations isolating ED conditioning from the GPT-style decoder, and no error analysis. This renders the verification of the core advantage over rigid-pocket methods untestable.

    Authors: We agree that the abstract would be strengthened by including quantitative support for the claim. In the revised manuscript, we have updated the abstract to summarize key metrics from our evaluations on the 101 targets, including validity, novelty, uniqueness, docking scores, and 3D pose RMSD. The full results in Section 4 already detail these metrics along with comparisons to baselines such as pocket-conditioned autoregressive and diffusion models, ablations isolating the ED conditioning, and error analysis in the supplementary material. These changes make the verification of effectiveness more self-contained and testable directly from the abstract. revision: yes

  2. Referee: [Abstract] Abstract / §4 (assumed results section): The weakest assumption—that low-resolution ED including filler yields a 'more faithful description of the binding environment' enabling superior generation—is not load-bearing tested. No head-to-head comparison on identical targets and metrics against standard pocket representations (with matched architecture and training) is described, leaving open whether any observed plausibility stems from the ED signal or from the autoregressive framework itself.

    Authors: We acknowledge the value of a controlled isolation of the ED signal. While the original manuscript includes comparisons to standard pocket-based SBDD methods, we have added a new ablation study in the revised Section 4. This uses the identical decoder-only autoregressive architecture but replaces the low-resolution ED point cloud conditioning (including filler) with standard rigid pocket representations on the same 101 targets and metrics. The results show improved generation quality with ED conditioning, indicating that the gains arise from the more faithful binding environment description rather than the framework alone. revision: yes

Circularity Check

0 steps flagged

No circularity in derivation chain

full rationale

The paper presents EDMolGPT as a new decoder-only autoregressive model that generates molecules conditioned on low-resolution electron density point clouds derived from holo complexes, contrasting this with rigid pocket representations. No equations, parameter fittings, or derivations are described that would reduce the claimed generation effectiveness or superiority to a self-referential definition, fitted input renamed as prediction, or chain of self-citations. The central claim rests on the introduction of the framework and empirical evaluations across 101 targets, which are presented as independent verification rather than tautological outputs from the inputs. The approach is self-contained as a methodological proposal grounded in physical signals, with no load-bearing steps that collapse by construction to the model's own assumptions or prior author results.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review provides no explicit free parameters, axioms, or invented entities; the central claim rests on the unstated assumption that ED point clouds are a superior conditioning signal.

pith-pipeline@v0.9.0 · 5496 in / 1011 out tokens · 51002 ms · 2026-05-12T02:42:48.018812+00:00 · methodology

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Reference graph

Works this paper leans on

45 extracted references · 45 canonical work pages · 2 internal anchors

  1. [1]

    Scaling Learning Algorithms Towards

    Bengio, Yoshua and LeCun, Yann , booktitle =. Scaling Learning Algorithms Towards

    work page

  2. [2]

    and Osindero, Simon and Teh, Yee Whye , journal =

    Hinton, Geoffrey E. and Osindero, Simon and Teh, Yee Whye , journal =. A Fast Learning Algorithm for Deep Belief Nets , volume =

    work page

  3. [3]

    2016 , publisher=

    Deep learning , author=. 2016 , publisher=

    work page 2016

  4. [4]

    International conference on machine learning , pages=

    Pocket2mol: Efficient molecular sampling based on 3d protein pockets , author=. International conference on machine learning , pages=. 2022 , organization=

    work page 2022

  5. [5]

    Nature Machine Intelligence , volume=

    Generation of 3D molecules in pockets via a language model , author=. Nature Machine Intelligence , volume=. 2024 , publisher=

    work page 2024

  6. [6]

    arXiv preprint arXiv:2303.03543 , year=

    3d equivariant diffusion for target-aware molecule generation and affinity prediction , author=. arXiv preprint arXiv:2303.03543 , year=

    work page arXiv

  7. [7]

    arXiv preprint arXiv:2404.12141 , year=

    MolCRAFT: structure-based drug design in continuous parameter space , author=. arXiv preprint arXiv:2404.12141 , year=

    work page arXiv

  8. [8]

    Journal of medicinal chemistry , volume=

    Directory of useful decoys, enhanced (DUD-E): better ligands and decoys for better benchmarking , author=. Journal of medicinal chemistry , volume=. 2012 , publisher=

    work page 2012

  9. [9]

    Nature Machine Intelligence , volume=

    Electron-density-informed effective and reliable de novo molecular design and optimization with ED2Mol , author=. Nature Machine Intelligence , volume=. 2025 , publisher=

    work page 2025

  10. [10]

    bioRxiv , year=

    ECloudGen: access to broader chemical space for structure-based molecule generation , author=. bioRxiv , year=

    work page

  11. [11]

    Journal of cheminformatics , volume=

    Why is Tanimoto index an appropriate choice for fingerprint-based similarity calculations? , author=. Journal of cheminformatics , volume=. 2015 , publisher=

    work page 2015

  12. [12]

    Nature Communications , volume=

    DynamicBind: predicting ligand-specific protein-ligand complex structure with a deep equivariant generative model , author=. Nature Communications , volume=. 2024 , publisher=

    work page 2024

  13. [13]

    International Conference on Learning Representations , year=

    3D Equivariant Diffusion for Target-Aware Molecule Generation and Affinity Prediction , author=. International Conference on Learning Representations , year=

    work page

  14. [14]

    Iscience , volume=

    The clinical trials puzzle: How network effects limit drug discovery , author=. Iscience , volume=. 2023 , publisher=

    work page 2023

  15. [15]

    Decoupled Weight Decay Regularization

    Decoupled weight decay regularization , author=. arXiv preprint arXiv:1711.05101 , year=

    work page internal anchor Pith review Pith/arXiv arXiv

  16. [16]

    Journal of medicinal chemistry , volume=

    Pharmacophore modeling, quantitative structure--activity relationship analysis, and in silico screening reveal potent glycogen synthase kinase-3 inhibitory activities for cimetidine, hydroxychloroquine, and gemifloxacin , author=. Journal of medicinal chemistry , volume=. 2008 , publisher=

    work page 2008

  17. [17]

    Drug metabolism and disposition , volume=

    Pharmacophore and three-dimensional quantitative structure activity relationship methods for modeling cytochrome p450 active sites , author=. Drug metabolism and disposition , volume=. 2001 , publisher=

    work page 2001

  18. [18]

    Journal of chemical information and modeling , volume=

    MolGPT: molecular generation using a transformer-decoder model , author=. Journal of chemical information and modeling , volume=. 2021 , publisher=

    work page 2021

  19. [19]

    Nature Communications , volume=

    TamGen: drug design with target-aware molecule generation through a chemical language model , author=. Nature Communications , volume=. 2024 , publisher=

    work page 2024

  20. [20]

    Nature communications , volume=

    Accelerating discovery of bioactive ligands with pharmacophore-informed generative models , author=. Nature communications , volume=. 2025 , publisher=

    work page 2025

  21. [21]

    Journal of computational chemistry , volume=

    AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility , author=. Journal of computational chemistry , volume=. 2009 , publisher=

    work page 2009

  22. [22]

    Current Computer-Aided Drug Design , volume=

    Virtual screening of drugs: score functions, docking, and drug design , author=. Current Computer-Aided Drug Design , volume=. 2008 , publisher=

    work page 2008

  23. [23]

    Neuron , volume=

    Molecular dynamics simulation for all , author=. Neuron , volume=. 2018 , publisher=

    work page 2018

  24. [24]

    Advances in neural information processing systems , volume=

    Sample efficiency matters: a benchmark for practical molecular optimization , author=. Advances in neural information processing systems , volume=

    work page

  25. [25]

    arXiv preprint arXiv:2203.02923 , year=

    Geodiff: A geometric diffusion model for molecular conformation generation , author=. arXiv preprint arXiv:2203.02923 , year=

    work page arXiv

  26. [26]

    arXiv preprint arXiv:2308.07413 , year=

    Benchmarking Generated Poses: How Rational is Structure-based Drug Design with Generative Models? , author=. arXiv preprint arXiv:2308.07413 , year=

    work page arXiv

  27. [27]

    Glide: a new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy , author=. Journal of medicinal chemistry , volume=. 2004 , publisher=

    work page 2004

  28. [28]

    MedChemComm , volume=

    Computational ligand-based rational design: role of conformational sampling and force fields in model development , author=. MedChemComm , volume=. 2011 , publisher=

    work page 2011

  29. [29]

    SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules , author=. Journal of chemical information and computer sciences , volume=. 1988 , publisher=

    work page 1988

  30. [30]

    OpenAI blog , volume=

    Language models are unsupervised multitask learners , author=. OpenAI blog , volume=

    work page

  31. [31]

    ChemMedChem , volume=

    On the art of compiling and using'drug-like'chemical fragment spaces , author=. ChemMedChem , volume=

    work page

  32. [32]

    Journal of Chemical Information and Modeling , volume=

    Observing noncovalent interactions in experimental electron density for macromolecular systems: a novel perspective for protein--ligand interaction research , author=. Journal of Chemical Information and Modeling , volume=. 2022 , publisher=

    work page 2022

  33. [33]

    Communications Chemistry , volume=

    Using macromolecular electron densities to improve the enrichment of active compounds in virtual screening , author=. Communications Chemistry , volume=. 2023 , publisher=

    work page 2023

  34. [34]

    Scientific reports , volume=

    A pocket-based 3D molecule generative model fueled by experimental electron density , author=. Scientific reports , volume=. 2022 , publisher=

    work page 2022

  35. [35]

    Journal of chemical information and modeling , volume=

    Ligand strain energy in large library docking , author=. Journal of chemical information and modeling , volume=. 2021 , publisher=

    work page 2021

  36. [36]

    Journal of Chemical Information and Modeling , volume=

    Large-scale analysis of bioactive ligand conformational strain energy by ab initio calculation , author=. Journal of Chemical Information and Modeling , volume=. 2021 , publisher=

    work page 2021

  37. [37]

    Gaussian Error Linear Units (GELUs)

    Gaussian error linear units (gelus) , author=. arXiv preprint arXiv:1606.08415 , year=

    work page internal anchor Pith review Pith/arXiv arXiv

  38. [38]

    Journal of Chemical Information and Modeling , volume=

    How good are current pocket-based 3D generative models?: The benchmark set and evaluation of protein pocket-based 3D molecular generative models , author=. Journal of Chemical Information and Modeling , volume=. 2024 , publisher=

    work page 2024

  39. [39]

    2006 , Journal =

    Automated ligand fitting by core-fragment fitting and extension into density , Author =. 2006 , Journal =. doi:10.1107/s0907444906017161 , Number =

    work page doi:10.1107/s0907444906017161 2006

  40. [40]

    Journal of Chemical Information and Modeling , volume =

    Ding, Kang and Yin, Shiqiu and Li, Zhongwei and Jiang, Shiju and Yang, Yang and Zhou, Wenbiao and Zhang, Yingsheng and Huang, Bo , title =. Journal of Chemical Information and Modeling , volume =. 2022 , type =. doi:10.1021/acs.jcim.1c01406 , url =

    work page doi:10.1021/acs.jcim.1c01406 2022

  41. [41]

    Journal of chemical information and modeling , volume=

    Comparative assessment of scoring functions: the CASF-2016 update , author=. Journal of chemical information and modeling , volume=. 2018 , publisher=

    work page 2016

  42. [42]

    Briefings in Bioinformatics , volume=

    Beware of the generic machine learning-based scoring functions in structure-based virtual screening , author=. Briefings in Bioinformatics , volume=. 2021 , publisher=

    work page 2021

  43. [43]

    Journal of chemical information and modeling , volume=

    Extended-connectivity fingerprints , author=. Journal of chemical information and modeling , volume=. 2010 , publisher=

    work page 2010

  44. [44]

    Journal of Chemical Theory and Computation , volume=

    Identification of protein--ligand binding sites by the level-set variational implicit-solvent approach , author=. Journal of Chemical Theory and Computation , volume=. 2015 , publisher=

    work page 2015

  45. [45]

    Journal of Chemical Information and Modeling , volume=

    Cosolvent and dynamic effects in binding pocket search by docking simulations , author=. Journal of Chemical Information and Modeling , volume=. 2021 , publisher=

    work page 2021