Ligand-Conditioned Discrete Diffusion for Protein Sequence-Structure Co-Design

Chen Wei; Fanding Xu; Lin Wang; Minghao Sun; Tianrui Jia; Yang Zhang; Yihang Zhou; Zhiyuan Liu

arxiv: 2605.27413 · v1 · pith:3ZN46OBQ · submitted 2026-05-15 · q-bio.BM · cs.AI

Ligand-Conditioned Discrete Diffusion for Protein Sequence-Structure Co-Design

Chen Wei , Fanding Xu , Minghao Sun , Zhiyuan Liu , Lin Wang , Tianrui Jia , Yihang Zhou , Yang Zhang This is my paper

Reviewed by Pith T0 review T1 audit T2 compute T3 formal T4 kernel 2026-06-30 19:45 UTCgrok-4.3pith:3ZN46OBQ record.json open to challenge →

classification q-bio.BM cs.AI

keywords protein designdiscrete diffusionligand conditioningsequence-structure co-designprotein-ligand complexespocket designdiffusion models

0 comments

The pith

Ligand-conditioned discrete diffusion jointly designs protein sequences and structures that bind given small molecules more accurately than prior token models.

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

The paper establishes that conditioning a discrete diffusion process on ligand geometry and chemistry allows simultaneous generation of amino-acid sequences and structure tokens that form compatible, functional binding sites. A reader would care because this token-space approach avoids the need to separately optimize sequence and coordinates while still respecting explicit ligand constraints. The method extends masked diffusion with cross-attention that injects ligand features and adds an inference-time ReMask step that iteratively corrects low-confidence tokens. Results show measurable gains on both global fold metrics for full proteins and local active-site accuracy plus docking success for pockets.

Core claim

ProtLiD² jointly generates amino-acid sequence and discrete structure tokens while incorporating ligand chemical and geometric information through geometry-aware cross-attention. Trained on over one million ligand-protein complexes, it extends masked discrete diffusion to ligand-aware functional protein design and uses maximum confidence-margin guided ReMask decoding at inference. This yields higher TM-score and pLDDT than Complexa on whole-protein design and lower active-site backbone RMSD plus higher ligand-aware pass rates than FAIR and PocketGen on pocket co-design.

What carries the argument

Geometry-aware cross-attention inside a masked discrete diffusion process over sequence and structure tokens, plus maximum confidence-margin guided ReMask decoding.

If this is right

Whole-protein designs achieve higher global fold confidence (TM-score rising from 0.672 to 0.802).
Pocket designs produce active-site backbones with lower RMSD (1.97 Å versus 3.4 Å).
Ligand-aware docking success rates increase substantially under both standard and stricter thresholds.
The same token-space framework supports both full-length and local pocket co-design without separate coordinate optimization.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

The approach could be tested on designing proteins that bind entirely new ligand scaffolds never seen in the training distribution.
ReMask decoding might transfer to other discrete diffusion tasks such as antibody or enzyme active-site redesign.
If the model truly captures ligand geometry in token space, it could reduce reliance on post-hoc docking validation during design campaigns.

Load-bearing premise

The training set of over one million ligand-protein complexes is assumed to be sufficiently diverse and representative that the learned conditional distribution generalizes to novel ligands and protein targets without significant distribution shift or memorization.

What would settle it

Evaluation on a held-out test set of ligands and protein targets whose binding pockets share no structural or sequence similarity with any training example, showing no improvement over unconditioned baselines on TM-score or docking pass rate.

Figures

Figures reproduced from arXiv: 2605.27413 by Chen Wei, Fanding Xu, Lin Wang, Minghao Sun, Tianrui Jia, Yang Zhang, Yihang Zhou, Zhiyuan Liu.

**Figure 1.** Figure 1: Overview of the proposed ProtLiD2 model. (a) A frozen GCP-VQVAE tokenizer converts protein backbone coordinates into residue-level structure tokens. (b) ProtLiD2 jointly denoises sequence and structure tokens with a ligand-conditioned masked discrete diffusion Transformer. Ligand chemical and geometric features are injected through geometry-aware cross-attention, and MCM-ReMask retains confident prediction… view at source ↗

**Figure 2.** Figure 2: Comparison of different unmasking strategies across protein lengths. [PITH_FULL_IMAGE:figures/full_fig_p007_2.png] view at source ↗

**Figure 3.** Figure 3: Qualitative pocket co-design case study on 3BKQ. ProtLiD [PITH_FULL_IMAGE:figures/full_fig_p009_3.png] view at source ↗

read the original abstract

Proteins perform their biological functions through three-dimensional structures encoded by amino acid sequences, and ligand-binding protein co-design requires models that generate sequence-structure compatible proteins under explicit ligand constraints. Although continuous diffusion and flow-based models support ligand-aware design in coordinate or latent spaces, existing discrete diffusion protein language models mainly operate over sequence or structure tokens without direct small-molecule conditioning. We introduce \textbf{ProtLiD$^2$}, a \textbf{Prot}ein \textbf{L}igand-conditioned \textbf{D}iscrete \textbf{D}iffusion model for protein sequence-structure co-design. ProtLiD$^2$ jointly generates amino-acid sequence and discrete structure tokens while incorporating ligand chemical and geometric information through geometry-aware cross-attention. Trained on over one million ligand-protein complexes, ProtLiD$^2$ extends masked discrete diffusion to ligand-aware functional protein design. We further propose maximum confidence-margin guided ReMask decoding, an inference-time self-correction strategy that retains confident predictions and remasks uncertain tokens. ProtLiD$^2$ improves global fold confidence over Complexa in whole-protein design, increasing TM-score from 0.672 to 0.802 and pLDDT from 64.55 to 73.00. In pocket co-design, ProtLiD$^2$ reduces active-site BB-RMSD from 3.46/3.40{\AA} for FAIR/PocketGen to 1.97{\AA}, and improves ligand-aware pass rates over PocketGen from 14.86% to 59.73% and from 6.08% to 23.49% under stricter docking thresholds. These results support ligand-conditioned discrete diffusion as an effective token-space framework for functional protein co-design. Code will be available at https://github.com/auroua/ProtLiD.

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.

Desk Editor's Note private letter to a colleague

ProtLiD² adds ligand geometry-aware cross-attention to masked discrete diffusion for joint seq-structure co-design and reports benchmark gains, but the abstract leaves the empirical claims under-specified.

read the letter

The main thing to know is that this paper puts explicit ligand conditioning into a masked discrete diffusion model for protein sequence and structure co-design. It uses geometry-aware cross-attention to bring in the small molecule's chemical and spatial features, adds a maximum-confidence-margin ReMask step at inference, and trains on over a million complexes. The abstract shows better numbers than Complexa, FAIR, and PocketGen on TM-score, pLDDT, active-site BB-RMSD, and ligand-aware docking pass rates.

What is actually new is the specific combination of discrete token diffusion with direct ligand conditioning and the ReMask decoding trick. Earlier discrete diffusion protein models lacked the ligand part, so this is a straightforward engineering extension that keeps everything in token space. The paper does well by demonstrating that the conditioning produces measurable lifts in both global fold quality and pocket accuracy, and the scale of the training data is substantial.

The soft spots are in the experimental reporting. The abstract gives no error bars, no statistical tests, and no information on data splits, hyperparameter search, or how evaluation thresholds were set. Without those, the size of the reported gains is hard to judge for robustness. The assumption that the million-complex training set is diverse enough to generalize to new ligands is also left as a standard caveat rather than something tested with clear out-of-distribution cases. These issues are real but not load-bearing if the full methods section fills them in.

This is for people working on conditional generative models for proteins in drug design or enzyme engineering. Readers who follow discrete diffusion or ligand-aware design will find the architecture and decoding details worth seeing. The work shows clear thinking on how to integrate the ligand signal and deserves a serious referee so the methods and numbers can be checked properly.

Referee Report

2 major / 1 minor

Summary. The manuscript introduces ProtLiD², a ligand-conditioned discrete diffusion model for joint protein sequence and structure co-design. It generates amino-acid sequences and discrete structure tokens while incorporating ligand information via geometry-aware cross-attention, trained on over one million complexes. The work proposes maximum confidence-margin guided ReMask decoding for inference. Reported results include improved whole-protein design metrics over Complexa (TM-score 0.672→0.802, pLDDT 64.55→73.00) and pocket co-design gains over FAIR/PocketGen (active-site BB-RMSD reduced to 1.97Å; ligand-aware pass rates increased to 59.73% and 23.49% under varying docking thresholds).

Significance. If the empirical gains are robust, the work demonstrates that discrete diffusion in token space can support effective ligand-aware protein co-design, providing a scalable alternative to continuous diffusion or flow-based methods. The scale of training data and the inference-time correction mechanism represent practical strengths for applications in functional protein engineering and ligand-binding design.

major comments (2)

[Abstract] Abstract: the central empirical claims of metric improvements (TM-score 0.672 to 0.802, BB-RMSD to 1.97Å, pass-rate gains from 14.86% to 59.73%) are reported without error bars, statistical significance tests, details on data splits, hyperparameter search procedures, or evaluation threshold selection criteria, preventing full verification of the performance claims.
[Results/Methods] Results and Methods sections: the manuscript provides no quantitative assessment of training-set diversity, potential memorization, or distribution shift for the >1M complexes, which is load-bearing for the generalization claims to novel ligands and targets.

minor comments (1)

[Abstract] Abstract: the model name expansion (ProtLiD²) and the description of ReMask decoding could be expanded slightly for immediate clarity to readers unfamiliar with discrete diffusion variants.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for these constructive comments on the empirical reporting and generalization analysis. We address each point below and will incorporate revisions to strengthen the manuscript.

read point-by-point responses

Referee: [Abstract] Abstract: the central empirical claims of metric improvements (TM-score 0.672 to 0.802, BB-RMSD to 1.97Å, pass-rate gains from 14.86% to 59.73%) are reported without error bars, statistical significance tests, details on data splits, hyperparameter search procedures, or evaluation threshold selection criteria, preventing full verification of the performance claims.

Authors: We agree that the abstract alone does not provide sufficient context for verification. In the revised manuscript we will (i) report error bars and statistical significance (e.g., paired t-tests across independent runs) for all headline metrics in the Results section, (ii) expand the Methods section with explicit descriptions of data splits, hyperparameter search ranges and selection criteria, and (iii) clarify how docking-score thresholds were chosen. The abstract will be updated to reference these supporting details or to present the improvements more conservatively. revision: yes
Referee: [Results/Methods] Results and Methods sections: the manuscript provides no quantitative assessment of training-set diversity, potential memorization, or distribution shift for the >1M complexes, which is load-bearing for the generalization claims to novel ligands and targets.

Authors: We acknowledge this gap. The current manuscript does not contain quantitative checks for training-set diversity, memorization, or distribution shift. In revision we will add a dedicated subsection reporting (a) sequence-identity and TM-score distributions within the training set, (b) nearest-neighbor similarity analysis between generated outputs and training examples on held-out ligands, and (c) a brief discussion of potential distribution shift between training and evaluation ligands. These additions will directly support the generalization claims. revision: yes

Circularity Check

0 steps flagged

No significant circularity

full rationale

The paper presents an empirical ML model (ProtLiD²) trained on an external dataset of >1M ligand-protein complexes and evaluated via benchmark comparisons on held-out complexes. No derivation chain, equation, or prediction reduces to a fitted parameter or self-citation by construction; results consist of standard performance metrics (TM-score, pLDDT, BB-RMSD, pass rates) against baselines. The central claims are data-driven engineering outcomes rather than self-referential identities.

Axiom & Free-Parameter Ledger

2 free parameters · 2 axioms · 0 invented entities

The central claim rests on standard assumptions of discrete diffusion models plus the representativeness of the training complexes; no new physical entities are postulated and the only free parameters are the usual neural-network hyperparameters.

free parameters (2)

diffusion schedule and masking ratio
Standard hyperparameters of masked discrete diffusion that control the training objective and are tuned on the ligand-protein dataset.
cross-attention layer dimensions and geometry encoding
Architecture choices fitted during training to maximize the reported metrics.

axioms (2)

domain assumption Discrete tokens for backbone geometry and side-chain identities are sufficient to capture ligand-binding compatibility
Invoked when the model operates exclusively in token space rather than continuous coordinates.
standard math The diffusion process can be conditioned on ligand features without violating the Markov property of the forward noising process
Core modeling assumption of conditional discrete diffusion.

pith-pipeline@v0.9.1-grok · 5891 in / 1643 out tokens · 30317 ms · 2026-06-30T19:45:35.989665+00:00 · methodology

discussion (0)

Reference graph

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