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Pallatom-Ligand: an All-Atom Diffusion Model for Designing Ligand-Binding Proteins

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 6.0Download Report PDF
DiffusionProtein DesignLigand Binding
Abstract:

Small-molecule ligands extend protein functionality beyond natural amino acids, enabling sophisticated processes like catalysis, signal transduction, and light harvesting. However, designing proteins with high affinity and selectivity for arbitrary ligands remains a major challenge. We present Pallatom-Ligand, a diffusion model that performs end-to-end generation of ligand-binding proteins at atomic resolution. By directly learning the joint distribution of all atoms in the protein–ligand complexes, Pallatom-Ligand delivers state-of-the-art performance, achieving the highest in silico success rates in a comprehensive benchmark. In addition, Pallatom-Ligand's novel conditioning framework enables programmable control over global protein fold and atomic-level ligand solvent accessibility. With these capabilities, Pallatom-Ligand opens new opportunities for exploring the protein function space, advancing both generative modeling and computational protein engineering.

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This report is AI-GENERATED using Large Language Models and WisPaper (A scholar search engine). It analyzes academic papers' tasks and contributions against retrieved prior work. While this system identifies POTENTIAL overlaps and novel directions, ITS COVERAGE IS NOT EXHAUSTIVE AND JUDGMENTS ARE APPROXIMATE. These results are intended to assist human reviewers and SHOULD NOT be relied upon as a definitive verdict on novelty.
NOTE that some papers exist in multiple, slightly different versions (e.g., with different titles or URLs). The system may retrieve several versions of the same underlying work. The current automated pipeline does not reliably align or distinguish these cases, so human reviewers will need to disambiguate them manually.
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Overview

Overall Novelty Assessment

Pallatom-Ligand introduces an end-to-end diffusion model for generating ligand-binding proteins at atomic resolution, directly learning the joint distribution of all protein and ligand atoms. This work resides in the Deep Learning-Based Protein Design leaf, which contains five papers including the original submission. This leaf represents a moderately active research direction within the broader Computational Design Methods branch, focusing specifically on neural network and diffusion-based approaches rather than classical physics-based or template-driven methods.

The taxonomy reveals neighboring design paradigms that provide important context. Physics-Based and Fragment-Based Design (four papers) employs molecular mechanics and quantum chemistry, while Template-Based and Homology-Guided Design (three papers) leverages existing scaffolds. De Novo Design from Target Structure (four papers) shares the goal of creating binders without templates but uses different computational strategies. Multi-State and Conformational Ensemble Design (three papers) addresses protein flexibility, a challenge that diffusion models may handle implicitly through learned distributions. The scope notes clarify that this leaf excludes classical methods, positioning Pallatom-Ligand firmly in the data-driven generative modeling space.

Among 26 candidates examined across three contributions, the unifying all-atom representation shows one refutable candidate from 10 examined, suggesting some overlap with prior atomic-level modeling approaches. The multi-level conditional generation framework found no refutations among six candidates, indicating potential novelty in programmable control over fold and solvent accessibility. The AlphaFold3-based evaluation metrics similarly showed no refutations across 10 candidates, though this may reflect the specialized nature of component-specific assessment rather than fundamental novelty. The limited search scope means these statistics capture top semantic matches rather than exhaustive prior work coverage.

Based on examination of 26 semantically related candidates, the work appears to advance deep learning-based ligand-binding protein design through its joint atomic distribution modeling and conditional generation framework. The single refutation among all contributions suggests moderate overlap with existing atomic-resolution approaches, while the conditioning capabilities may represent a more distinctive contribution. This assessment reflects the top-K semantic search scope and does not claim comprehensive coverage of all relevant literature in protein design or diffusion modeling.

Taxonomy

Core-task Taxonomy Papers
50
3
Claimed Contributions
26
Contribution Candidate Papers Compared
1
Refutable Paper

Research Landscape Overview

Core task: Designing ligand-binding proteins with atomic resolution. The field encompasses a diverse set of approaches organized into several major branches. Computational Design Methods for Ligand-Binding Proteins includes both classical Rosetta-based techniques and modern deep learning-based strategies, exemplified by works like LigandMPNN[2] and PocketGen[40], which leverage neural architectures to generate binding sites. Binding Affinity Prediction and Scoring focuses on evaluating protein-ligand interactions through machine learning models such as Deep Learning Affinity[3] and physics-based scoring functions like Semiempirical Free Energy[13]. Binding Site Analysis and Identification employs geometric and graph-based methods, including PointSite[20] and Atomic Environment Vectors[5], to locate and characterize potential binding pockets. Structural and Biophysical Characterization addresses experimental validation through techniques like In-Cell NMR[27] and Diffracted X-ray Tracking[37]. Specialized Applications and Domains targets specific systems such as metalloprotein design, GPCR virtual screening, and therapeutic protein engineering. Reviews and Methodological Perspectives, including Design Retrospective[19] and Design Essentials[35], synthesize progress and identify open challenges. Finally, Protein Stability and Structural Engineering considers the interplay between binding function and overall protein fold integrity, as seen in Atomic Precision Stability[10]. Within the deep learning-based design branch, a particularly active area involves generative models that directly produce binding-competent protein structures. Pallatom-Ligand[0] sits squarely in this cluster, emphasizing atomic-resolution generation of ligand-binding sites through advanced neural architectures. It shares methodological kinship with LigandMPNN[2], which focuses on sequence design conditioned on ligand geometry, and Atomic Flow Matching[16], which applies flow-based generative modeling to protein structure. Compared to PocketGen[40], which generates pockets in a more modular fashion, Pallatom-Ligand[0] appears to integrate ligand context more tightly during the generation process. A central tension across these works involves balancing designability—ensuring that generated structures are physically realistic and stable—with functional specificity, namely high-affinity and selective ligand recognition. While earlier efforts like High Affinity Selectivity[8] relied heavily on physics-based energy functions, recent deep learning methods trade explicit physical modeling for data-driven pattern recognition, raising questions about generalization to novel ligands and the interpretability of learned representations.

Claimed Contributions

Unifying all-atom representation for protein-ligand complexes

The authors introduce a unified atomic representation scheme where small-molecule ligands are encoded at the atomic level and protein residues are modeled as generic 14-atom entities. This representation enables joint learning of the distribution of all atoms in protein-ligand complexes through a novel ligand-aware all-atom diffusion transformer.

10 retrieved papers
Can Refute
Multi-level conditional generation framework

The authors develop a hierarchical conditioning framework that enables control at two levels: global control over protein fold via alpha ratio to encourage structural diversity, and atomic-level control over ligand solvent accessibility to guide binding pocket design for specific applications.

6 retrieved papers
AlphaFold3-based component-specific evaluation metrics

The authors introduce a set of component-specific metrics derived from AlphaFold3 predictions that separately assess protein scaffold quality, ligand pose accuracy, and binding interface complementarity, enabling more discriminating evaluation than aggregate confidence scores.

10 retrieved papers

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

Unifying all-atom representation for protein-ligand complexes

The authors introduce a unified atomic representation scheme where small-molecule ligands are encoded at the atomic level and protein residues are modeled as generic 14-atom entities. This representation enables joint learning of the distribution of all atoms in protein-ligand complexes through a novel ligand-aware all-atom diffusion transformer.

Contribution

Multi-level conditional generation framework

The authors develop a hierarchical conditioning framework that enables control at two levels: global control over protein fold via alpha ratio to encourage structural diversity, and atomic-level control over ligand solvent accessibility to guide binding pocket design for specific applications.

Contribution

AlphaFold3-based component-specific evaluation metrics

The authors introduce a set of component-specific metrics derived from AlphaFold3 predictions that separately assess protein scaffold quality, ligand pose accuracy, and binding interface complementarity, enabling more discriminating evaluation than aggregate confidence scores.

Table of Contents

Pallatom-Ligand: an All-Atom Diffusion Model for Designing Ligand-Binding Proteins | Novelty Validation