Pallatom-Ligand: an All-Atom Diffusion Model for Designing Ligand-Binding Proteins
Authors: Haochen Wang
Qianyi Wang
Rui Ma
Jiawei Guan
Weikun.Wu
Haobo Wang
Jiayi Dou
Venue: ICLR 2026 Poster (2026)
Keywords: Diffusion
Protein Design
Ligand Binding
Primary area: applications to physical sciences (physics, chemistry, biology, etc.)

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.

Published as a conference paper at ICLR 2026
PALLATOM-LIGAND: AN ALL-ATOM DIFFUSION MODEL FOR DESIGNING LIGAND-BINDING PROTEINS
Haochen Wang" 3, Qianyi Wang", Rui Ma', Jiawei Guan', Weikun Wu Haobo Wang", Jiayi Dou
Sccool of Information Science and Technology, ShanghaiTech University "School of Life Science and Technology, Shanghai Tech University SLEVINTHAL Biotechnology Co., Ltd.
ABSTRACT
Small-molecule ligands extend protein functionality beyond natural amino acids, enabling sophisticated processes like catalysis, signal transduction, and light har- vesting. However, designing proteins with high affinity and selectivity for ar- bitrary ligands remains major challenge. We present Pallatom-Ligand, dif- fusion model that performs end-to-end generation of ligand-binding proteins at atomic resolution. By directly learning the joint distribution of all atoms the protein-ligand complexes, mance, achieving the highest in silico success rates comprehensive bench- mark. In addition, Pallatom-Ligand programmable control over global protein fold and atomic-level ligand solvent accessibility. With these capabilities, Pallatom-Ligand opens new opportuni- ties for exploring the protein function space, advancing both generative model- ing and computational protein engineering. The code is available at ht ttsp:: / //githuls com/ www.lleeenntllaaaeaeeeeeaaaaaaaa allatoonnn at- Ligand.
Dallatom-Ligangy delivers state-of-the-art perfor-
novel conditioning framework enables
INTRODUCTION
The ability design proteins with high affinity and selectivity for given small molecule is critical for the development of protein- based biosensors, therapeutics, and diagnostics. Traditional meth- ods for engineering ligand-binding proteins for medical and biotechnological applications rely on laboratory-directt evolution, which introduces random mutations and selects the desired properties through multiple rounds of labor-intensive experiments (Boder al., 2000). Computational protein design provides a complementary approach to generate de novo small molecule-binding proteins by directly modeling the pooteinllgganl interactions at atomic level (Tinberg et al., 2013; Dou et al., 2018 8; Polizzi & DeGrado, 2020). However, physics-based computational methods like Rosetta re- quire expert-level biochemical intuition and are hampered by inaccurate energy evaluation (Dou et al., 2017).
Deep learning has revolutionized protein science, surpassing physics- based approaches in structure prediction, unconditional generation, and protein-protein interface design (Jumper et al., 2021; Wat- son et al., 2023; Abramson et al., 2024). Yet its application to designing small molecule-binding proteins remains limited. Current state-of-the-art models, such REdiffusionAA, CA_RFdiifiii RFdiffusion, and RFdiffusion2, are insufficient in learning the atomic interactions at the protein-ligand interface and rely separate inverse -verse-folding model to design the protein sequence. While these methods have achieved notable experimental successes, including the design of functional enzymes (Krishna al., 2024; Lauko et al., 2025; Ahern et al., 2025), their experimental success rate remains low compared to ligand-free protein design and requires significant expert intervention.
We identify two fundamental challenges in applying deep learning methods to designing ligand- binding proteins: the lack of explicit atomic-level modeling of protein-ligand interactions, and the
Work done during an internship at LEVINTHAI. Biotechnology.
Corresponding authors. Emails: Weikun Wu (weikun. workkun.wwlllee @ellevinthal.bio) Haobo Wang
Chaobowan@@levinter biv), Jiayi Dou (doujy @ww.aagaaaaicchhcc.....

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shortage of high-quality structural data. We hypothesize that end-to-end approach learning the joint distribution of all atoms in a protein-ligand complex can improve both the modeling accuracy and the data efficiency of learning. This hypothesis is grounded in two observations. First, atoms are the fundamental unit of all molecules and provide a unifying representation across chemistry space. The remarkable success of AlphaFold3 in biomolecular structure prediction demonstrated the power of directly modeling individual atoms (Abramson et al., 2024). Second, the aforemen- tioned backbone-only generative models lack the mechanism for atomic details in ligand and protein side chains to refine the backbone generation. This desired multi-level information exchange could be achieved through a unified architecture combining all-atom representation with an end-to-end training.
To test our hypothesis, we developed Pallatom-LLigad (Figure 1), a novel diffusion- based model that generates ligand-binding proteins with all-atom modeling. Our main contributions are summarized as below:
1. A unifying representation for small molecules and unknown protein residues. In Pallatom-Ligand, small molecules are directly represented by their atoms, while each amino acid residue modeled as a generic molecule of 14 atoms. We implemented a novel ligand-aware all-atom diffusion transformer enable global information exchange across the protein-ligand complex.
2. Multi-level controls tailored for designing ligand-binding proteins. We experimented with novel conditioning strategies tailored for designing ligand-binding proteins. Notably, we implemented global control over the protein tertiary structure to guide the model to- wards generating diverse folds; we also achieved atom-level control over the ligand solvent accessibility for downstream applications.
3. Novel evaluation metrics to assess the model performance in silico. We introduced AlphaFold3-basee evaluation metrics specific for the task of designing tein. We performed a comprehensive benchmark study comparing RFdiffussionAA AA, RFd- iffusion2, and Lailatom-Ligand. The results demonstrate that Pallatom-Ligan delivers state-of-the-ard performance.
pro-
2 RELATED WORKS
2.1 ALL-ATOM PROTEIN DESIGN METHODS
Diffusion and flow-based generative models have demonstrated notable success in de novo protein design in the past years. Early generative models for protein design were focused learning the geometric distribution of protein backbones. To list a few, FrameDiff and REdiffusion operate on the SE(3) manifold using backbone local frames, while Chroma and Proteina work in Euclidean space to model backbone atoms (Yim et al., 2023; Watson et al., 2023; Ingraham et al., 2023; Geffner et al., 2025b). Concurrently, protein design methods based on those models follow two-stage protocol where the protein backbones and sequences are generated separately. and evaluated post hoc by structure prediction model such as Alphafold2 or ESMfold (Jumper al., 2021: Lin et al., 2023).
More recently, efforts have shifted towards all-atom generative models that capture the joint distri- bution of structure and sequence. These approaches can broadly categorized into three streams: sequence-centric methods, such as ProteinGeneeatoo (Lisanza et al., 2023) and PLAID (Lu et al., 2024), which prioritize sequence generation or latent representations and rely on structural decoders to recover geometry; structure-centrr models like Protpardelle (Chu et al., 2024) and Pallatom (Qu et al., 2024), which focus on geometric generation by either simultaneously updating side-chains or inferring sequences from denoised all-atom coordinates; and unified frameworks such La- Proteina (Geffner et al., 2025a), which integrate both modalities into a shared latent space via VAEs to achieve coherent co-generation.
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Ligand Information
Design Conditions
2CCC
exposed
baried
DOG DICOXICENIN
HICICH]IICCCCCII CНЛНИСCCНООУCЕ dpt
Ligand Solvent Accessibility
CCD code
SMILES
SDF
Protein a/B Ratio
Pallatom-Ligann
Diffusion Model
Figure 1: Pallatom-Ligant Overview Pallatom-Ligant generates all-atom protein-ligane ligand com- plex through an end-to-end diffusion process. The model takes two inputs: the chemical definition of the male-lm ligand in one of the three formats (cream background; CCD: chemical com- position distribution, SMILES: simplified molecular line entry system, SDF: structure data file) and the design conditions (mint background).
2.2 De novo DESIGN OF LIGAND- BINDING PROTEINS
De design of ligand-binding proteins is defined the generation of novel proteins that recognize a given small-molecule ligand. It was first demonstrated by knowledge-based methods such RosettaMatch, RifDock, and der Mer(vdM)-based methods (Tinberg al., 2013; Dou et al., 2018; Polizzi & DeGrado, 2020). These methods start with pre-generated protein backbones called "scaffolds" and search for combination of positions in each scaffold that can constellate 3D pocket suitable for binding the target ligand. Their success rates depend many factors, such as the choice of the scaffolds, the chemical complexity of the target ligand, and the evaluation of the binding site.
More recently, with the success deep learning-baat unconditional protein generation (Related Works 2.1 ), several generative models have been developed for ligand-binding proteins. RFdiffu- sionAA models protein backbones and small-molecule ligands as SE(3) frames and leverages 1E3-transformer to exchange information across the protein protein-ligand interface (Krishna et al., 2024). CA REdiffusion simplifies RFdiffusionAA backbone representation to C. atoms only and directly operates the atomic features design ligand-aware protein backbones (Lauko et al., 2025). Along this line, RFdiffusion2 extends the original formulation by incorporating advanced conditions, such unindexed motifs, ligand orientations, and relative solvent accessibility, support enzyme de- sign tasks (Ahern et al., 2025). Despite these advances, all three models remain backbone-only generators; the diffusion process does not involve protein side chains. Without explicit modeling of intricate atomic interactions at the protein-ligan interface, these models rely on user- provided constraints obtain the desired spatial configuration. Such reliance on expert-crafted priors not only introduces strong biases in case-dependent manner, but also limits their general usability.
METHOD
3.1 A UNIFYING SCHEME FOR ENCODING PROTEIN-LIGAND HYBBRI SYSTEM
We adopt a unifying atom representation for encoding the chemical and structural information of ligand hybrid system. For small-ligand with atoms, its chemical complexity and struc- tural connectivity are directly encoded at the atomic level. Each amino-acid residue in the protein chain is modeled generic chemical entity containing 14 atoms according to Pallatom's atom14
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Token Representations
Token Conditions
Token Representations
Token Conditions
Trkes stngle
Tiker Zar
Token Pair
Protein Residues
Ligand Atoems
SS Token
Time Embedking
Atom Repnosentations
Arom Conditions
Joon Singl
Asoin
Token Level Full Attention
Token Level Full Attention
Updated Token Reporssnnattions
Updated Condition Embedding
Atom Level Block Sparse Attention
Atom Representations
Atum Cunditions
Atum Pair
Ligand SA
Time Emhalding
Atomss
Ligand Atoms
Embedding
Token Pair Triangle Attention
AdaLN
Atom Level Block Sparse Attention
Token Representatious
[itun
Denoised
Represescations
Coardinates
Alum Stage
Updated Atom Representations
Figure 2: Pallatom-Ligand Transformer Architecture (a) The core diffusion transformer con- sists of three attention modules (light pink). These modules jointly update token- and atom-level representations (dark and light blue), guided by their corresponding conditions (dark and light yel- low). The denoised coordinates (purpee) are decoded from the final atom representations. token-level full attention module (light pink) updates the token-level single representations (bluc) and condition embedding (yellow). integrates information from the token-level pair features (left) and the time embedding (right), with black arrows indicating information flow. (c) Similary, the atom-level block sparse attention module (light pink) updates the atom-level single representations (blue). This update is conditioned on the atom pair features (left) as well as the atom-level ligand SA and time embeddings (right).
(b) The
representation (Qu et al., 2024). Thus, protein-ligand complex with a L-residue protein and I- atom ligand is encoded as chemical system with 14L + atoms. General atomic features for both small molecules and proteins are initialized by atom's is element number, partial charge, etc. (see the full list in Appendix A.6). Specifically, per-atom ligand solvent accessibility(SA) feature is fused ligand atom representations for the purpose of conditional generation through self attention (Section 3.3).
To capture the overall spatial features of the complex structure, we use a coarse-level abstract token representation, following the approach of AlphaFold3 (Abramson et al., 2024). Every 14 atoms in the protein are aggregated into one token, while each ligand atom is kept as an individual token to emphasize the atomic interactions across the protein-ligand interface.
Within this unifying scheme, the atom representation learns the fine- mne-grained heterogencity unique to each atom in the system, where the ligand and the protein are treated equal entities. Meanwhile, the hybrid token representation places an emphasis onto ligand atoms and learns the coarse-level structural features sensitive ligand conformational changes.
3.2 PALLADOM-LIGAND TRANSFORMER ARCHITECTURE
We set out to design a network that not only captures the overall structural features of the protein- ligand complex but also learns the interatomic interactions across the protein-ligand interface. More importantly, the network design should enable a mechanism for information exchange between atom-level interactions and token-level protein backbone generation. With these considerations, we designed the triple-attention transformer architecture to jointly process token- and atom-level representations (Figure 2).

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FMN
D0G
LDP
580
BAD
SAM
Figure 3: Ligand-Binding Proteins Designed by Pallatom-Ligand (a) The chemical structures of eight small molecules are labeled with their three-letter CCD) codes. (b) Examples of designed complexes for each target (light purple) are aligned to their AlphaFold3 predictions (light pink). (c) Zoomed-in binding interfaces highlight the hydrogen bonds and electrostatic attractions (yellow dashed lines).
The overall network architecture of Pallatom-Ligand is based on Pallatom (Qu et al., 2024), with several key modifications. Following the Diffusion Transformers (DiT) design philosophy (Pee- bles & Xie, 2023; Esser et al., 2024; Geffner et al., 2025b), we streamlined the original Pallatom network by replacing its traversing mechanism with a modern transformer architecture. This new transformer operates with three attention modules: token-level full attention, atom-level block sparse attention, and token pair triangle attention (Figure 2a). To validate the necessity of this triple- stream design, conducted ablation study removing individual attention components. The results, detailing the contribution of each stream to the overall generative capability, are provided in Appendix A.16.1.
Denote the token-level single representation as cond.s E TR k C Cton (where 4 is the number of secondary structure (SS) tokens), the time em- bedding as E IR tems, the atom-level single representation as E IR 1114L+))xCatom the atom-level conditions as c E (14f++)) Cutum. the token-level pair representation as 2 E IR (I+++)))(( Cpair and the atom-level pair representation as p E ([44=)))((44++)))xaamm these features are described in Appendix A.6.
R(++()xC.o. the secondary structure conditions
The initialization of
Each transformer block performs the following operations sequentially:
Token-level Full Attention This module takes the token-level representation a and condition cond,s as input. It updates them with attention bias from token-level pair features 2, with adaptive layer normalization controlled by time embedding t (Figure 2b):
(a, cond (a, cond Attention[[a, cond,s), t,2). 2):
(a, cond.s] = (a, cond.s] + Transition( ([a, condas], 1).
(2)
This is followed by token-to-atom conversion:
+ Layernorm (Btok_to_atom)
(3)
where Otok to atom is indexing operation which puts token to their corresponding atoms.
Atom-level Block Sparse Attention This module operates directly on the atom representation
with the atom-level conditions using adaptive layer normallization(connrolle by f) and atom-level pair features p as attention bias (Figure 2c):
= + SparseAttemtion((gce
(4)
= + Transition t),
(5)
This is followed by an ttom-tottoken conversion:
+ Layernorm (eaerrormmmSegmmetMMnaangg
where SegmentMean is to mean atom features to token level based on their corresponding token index.
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Success Kates Lifferent Methods (1)
Ca (4)
protcin LLDDT (1)
ArUiitaiiAAA MUPNN
R8THifaaio?? MINN
Pelaumilggei
60'
Ligand
Paltoom-liaassO0
0.-
Pallcou Ligani (wr SKy MPNNS
ligand-Ecente: (4)
ligand Haand-LDDDT
Suaces Raie
15
ligand BaaddRMMSDH
(4)
25
20-
20-
15-
10-
Prooiin PN4
Eigond Phocket
Tigand
C
Tarpet Ligand Dose Sucves Rales (f)
0.15
Steccn Kie
0.10
0.00
Figure 4: Benchmark Performance of RFDiffusionAA, RFDiffusion2 and Pallatom-Ligand (a) Overall success rates are shown for protein fold, ligand pocket, and ligand pose generation. The color scheme is provided in the legend, with full experimental details Section 4.2 (b) Detailed metrics provide component-wise breakdown comparisons of protein scaffold (top row), ligand pose (middle row), and binding interface (bottom row). (c) Ligand-pon ponne-ssse success rates for each of the eight benchmark ligands demonstrate method consistency.
Coordinate Decoding and Token Pair Triangle Attention The network decodes the 3D coordi- nates for each atom directly from the updated atom representations q. Subsequently, the token pair representation z is updated by a token triangle attention module that incoorporates distance information between the center atoms of the tokens:
Σybf =Linear(dis((ramttr))
+ Triangle/Attentionn::: 2,bf,
(8)
The atom pair representation also updated using the pairwise atom distances via a linear layer. The detailed implementations of the "Attention" in Eq. "SparseAttention" in Eq. 4, "TriangleAt- tention" in Eq. 8 and "Transition" in Eq. 2,5 are described in Appendix A.6.
3.3 CONDITIONS ON GLOBAL AND ATOMIC SCALES
Leveraging the modular design of our network architecture (Section 3.2), we experimented with novel conditioning strategies at both the token and atom levels. These strategies enable global control over protein fold generation and local control over ligand solvent accessibility, respectively.
Global Conditioning: Protein Fold Via Alpha Ratio Natural proteins exhibit wide variety of tertiary structures with diverse a/B compositions, which underpins their vast array of functions in biological systems (Orengo et al., 1997). To design proteins with novel functions, is therefore sential to explore the protein structure space comprehensively. However, several generative models have been observed to favor all-helical structures (Watson et al., 2023; Geffner al., 2025b).

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Protein Scaffold
Ligend Pose
Binding Interfues
Foo (1)
Nov. L.T
Tig center
lg CERDSDD
RISilfasionAA (mptmi)
5.72
81.52 11.17
0.21
0.74
6.71
6.25
63.06
13.02
7.67 3.28
RRiifission (mpont)
5.48
87.00 10.40
0.80
12.60
Das (wrost
$4.48
0.17
11.5029.0
83.5
12.73
5,90 2.94
Ous (wloss SA) (mpunl)
2.05
91.33 2 6.33
0.17
0.85
8.45 4 8.70
10.78 4: 7.99
73,40
10.61
4.07 4: 1,98
Outs (w/ SA)
3.95 4.61
61,57 10:45
0.14
0.81
8.57 à 8.01
10.59 4: 7:47
64.00 14.43
7.13 3.49
Das (wr SA) (mpon])
1.36 1.63
0.14
0.81
5.56
7.04
22.78
10.65
4:35 1.80
Table I: Benchmark Performance Designing Ligand-Binding Proteins The best-performing model is indicated in Bold, and the second-best is Underlined. Div. and Nov. denote Diversity and Novelty, respectively.
To mitigate such bias, we introduce the "a ratio" as a coarse-level control signal for balancing protein fold generation. This ratio is defined the number of residues in a-helices divided by the total number in a-helices and f-strands, ranging from 0-1. We implemented this global control over label.
protein tertiary structures via three steps: (i) Calculating per-residue recondary-structte (ii) Calculating the a ratio for each training sample and categorizing it into three groups: mainly 3 (0-0.2), a/8 mix (0.2-0.8), and mainly a (0.8-1). (iii) Injecting this categorical signal into the diffusion process via a concatenated self-attention mechanism at the token level (Figure 2b).
Atomic Conditioning: Ligand Solvent Accessibility Controlling ligand solvent accessibility (SA)
is crucial in designing ligand-binding proteins for real-world applications. The decision to expose or bury specific ligand atoms within the binding pocket directly impacts the protein's utility as biosensors, therapeutics, and diagnostics.
To enable atom- control over ligand solvent accessibility, we implemented two key modifica- tions: (i) We quantified the solvent accessibility of each ligand using Relative Solvent Accessibil- ity(SSA) (Ahern et al., 2025), which was then discretized into three categories: fully buried (0-0.1), 1.1, paritlly buried (0.1-1.0), and fully exposed (1.0). (ii) This discrete RSA feature was incorporated as an atom-level input via projected learnable embeddings, allowing it to directly influence structure generation at atomic resolution (Figure 2c).
3.4 TRAINING STRATEGIES
Data Sampling Compared to image or natural language data, high-quality protein structure data are not only limited by quantity but also suffer from severe distributional imbalance. This imbalance particularly pronounced in protein-ligand complexes. Some ligands are observed only with spe- cific protein folds, whereas other folds can bind to wide variety of ligands (Durairaj et al., 2024). Conventional data sampling strategies exacerbate this issue. Clustering samples by overall protein structure leads to uneven ligand frequencies, causing large performance discrepancies across differ- ent targets. Conversely, clustering the samples solely by -sand-prottiin interface results in model collapse with converged protein structures (Appendix A.17).
To address this dilemma caused by data sparsity, we introduced a dual-objective training frame- work with distinct data sampling strategies: (i) Learning protein folds: Samples are selected from structure- based clusters. In this training mode, we apply sequential and spatial cropping to preserve the whole-protein structural context. (ii) Learning protein-ligand interactions: Samples are selected via ligand-based clustering. In this mode, we crop the local region around the ligand, allowing the model focus on atomic interactions independent of the global fold. We use 1 sampling ratio between Mode (i) and Mode (ii) to balance the objectives, ensuring each ligand type was sampled equally without oversampling specific protein folds. We justify the choice of our balanced sampling strategy through comparative analysis against fold-centric and interaction-centric baselines. De- tailed ablation results demonstrating the necessity of this duje--oeective approach for generalizing to rare ligands are presented in Appendix A.16.2.
Condition Sampling To ensure compatibility when combining multi-level conditions, we imple- mented hierarchical sampling strategy. For the global fold condition(a ratio), the label is provided with probability of p = 0.5 and omitted otherwise (p = 0.5). For the atomic-level condition (ligand SA), we apply two-tiered strategy: (1) All SA labels are dropped with p 0.5; (2) With p 0.25, labels are provided for all ligand atoms; (3) With p = 0.25, labels are provided for a random subset of ligand atoms, where each atom is included independently with p 0.5.
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4 EXPERIMENTS
4.1 METRICS
AlphaFold3-Based Component-ppecitie Metrics Following the established in silico protocol of ranking generative models by their consistency with structure prediction model, existing ligand- binding protein design methods are evaluated using overall AlphaFold3 confidence scores (Cho al., 2025). We find these aggregate scores insufficient because they fail to reflect the multi-faceted nature of the design challenge. A successful ligand-binding design requires three components: stable protein scaffold, accurate ligand positioning, and a complementary binding interface; failure in any one aspect results in non-functional design. To enable a more discriminating evaluation that pinpoints methodological strengths and shortcomings, we introduce a set of component-specifie metrics derived from the alignment of the design models to their AlphaFold3 predictions!:
Protein Scaffold: RMSD of protein backbone C. atoms (Ca-RMSD) and the averaged pLDDT over all protein atoms (protein-pLDDDT), PLDDT).
Ligand Pose: Distance between ligand centroids Higand-Dcentedd RMSD of ligand atoms (liandd-MMSD), and the average pLDDT over ligand atoms (ligand-LLDDT).
Binding Interface: Interface predicted aligned error (ipAE).
Based on these numerical metrics, we further define three types success with increasingly strin- gent criteria:
Protein-Fold Success: Ca-RMSD 2Á and protein-pLDDDT > 80.
Ligand-Pocket Success: Protein-Fold Success and ligand-Deent center 4A and ligand-pLDDT 80.
Ligand-Pose Success: Protein-Fold Success and ligand RMMS < 2Å.
In all the tests below, use these metrics to evaluate the performance of RFdiffusionAA, RFdiffu- sion2 and Pallatom-Ligand For detailed description of all metrics used in this study, please refer to Appendix A.10.
4.2 BIGAND-BINDING PROTEIN GENERATION BENCHMARK
We benchmarked Pallatom- Ligand against RFidffusionAA and RFdiffusion2 on a set of eight small molecules selected for their diverse chemical properties (Figure 3, Appendix A.8), To our knowl- edge, this represents the most comprehensive benchmark for ligand-binding protein design, expand- ing upon previous four-target set to include molecules with smaller size, opposite charge, and hy- drophobic moietics. For each target, we generated 100 protein structures per method at a fixed length. We then designed single sequence for each structure using LigandMPNN (Dauparas al., 2025) and evaluated the resulting stracture-sequence pairs with our AlphaFold3- metrics (Ex- periments 4.1). Full benchmarking protocols are provided in Appendix A.9.
Since Pallatom-Ligand co-generates structures and sequences in its all-atom framework, we used LigandMPNN redesign only the sequences outside the ligand-binding interface, apply- ing GA atom distance threshold. We evaluated both Pallatom- Pallatom-Ligand's raw sequences and the LigandMPNN- -eenddMMN--eddeiinneed sequences. For fair comparison with RFdiffusion2 tioned on a globally buried ligand by default (Appendix A.9) - we performed additional Pallatom- Ligand inferences using our ligand SA control, setting the SA to 0 (fully buried) for all targets. In Figure 4, these four sets of inferences are denoted as: Pallatom-Ligan (w/out SA), Pallatom- Ligand (w/out SA) + MPNN, Pallatom-Ligand (w/ SA), and Pallatom-Ligam (w/ SA) + MPNN. All sampling parameters are listed in Appendix A.7.
which was condi-
In our benchmark, Dillatom-Ligand achieved higher silico success rates than REdiffusionAA and RFdiffusion2, outperforming them across most of evaluated metries (Figure 4, Table 1). To elucidate
We emphasize that these metrics quantify structural consistency only necessary condition for valid design and they do not guarantee biological activity.
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the source of this improvement, leverage component-specific metrics to analyze the results from two complementary perspectives:
All-Atom Generation of Protein-Ligand Complexes We first focus on dillatom-Ligande ' raw quences (light pink and light purple bars in Figure 4) to assess its capability for end-to-end, all-atom generation. In protein fold generation, Pallatom-Ligand's ss raw sequences achieve high performance, as indicated by the protein-fold success rate (first column of Figure 4a) and individual metrics, in- cluding Ca-RMSD and protein-pLDDT (first row of Figure 4b). When explicitly conditioned to bury all ligand atoms, our raw sequences exhibit a trade-off: a decrease in fold success rate along- side a slight increase in ligand-pocket and ligand-pos, success rates (light purple vs. light pink bars, Figure 4a). This inverse relationship between protein stability and function is well-established principle in protein science (Shoichet et al., 1995; Bloom et al., 2006; Tokuriki et al., 2008), and it encouraging to see our data-driven model recapitulates this trend. Regarding ligand-binding capabil- ity, Pallatom- Ligand's raw sequences surpass RFdiffusionAA in both ligand-pocket and ligand-post pose success rates (light pink vs. blue bars, second and the third columns, Figure 4a). In addition, our model achieves the highest ligand-pLDDT and the lowest ipAE among all baselines (Figure 4b), underscoring the advantages of joint all-atom modeling of ligands and proteins.
Advantages of All-Atom Modeling with LigandMPNN By examining the performance of sequences, we isolate the improvements specifically
Dillatom-Ligandee LigandMPNN-redesigee due to all-atom structure generation. Pallatom-Ligand with LigandMPNN achieves the highest in silico success rates and the best metrics for both protein folding and ligand binding (dark purple and rosy brown bars, Figure 4). This leading performance stems from a substantial improvement in protein fold quality, complementedy by a consistent improvement in ligand binding performance. Furthermore, Pallatom-Ligand with LigandMPNN demonstrates balanced performance across all eight targets (purple bars, Figure 4c), indicating that our training strategy is well calibrated (Method 3.4). Notebly, Mitattom-iggan successfully generated in silico binders for all targets, whereas the RFdiffusion2 failed for FAD and SAM, and RFdiffusionAA succeeded for only three out of eight (Figure 4c).
In summary, this comprehensive benchmark yields two key insights: not only demonstrates Pallatom-Ligand" superior in silico performance over previous state-of-the-art methods but also, by deconvoluting the overall improvement into specific components, provides clear direction for further work particularly in refining the learning of atom-level ligand-protein interactions.
4.3 CONDITIONAL GENERATION WITH RATIOS
Inspired by semantic conditioning in image generation (Rombach et al., 2021; Labs et al., 2025),
our model is trained to freely assemble secondary structure elements in order to match a target a ratio, thereby encouraging diversity in protein tertiary structures (Section 3.3).
To validate this strategy, we conducted experiments generating proteins across a spectrum of a ratios and evaluated their ligand-binding success rates well as secondary structure distribu- tions. For each target, we generated 40 samples under three different secondary structure conditions. Table reports the mean values of these metrics over all eight targets, evaluating Pallatom- Ligand's 's raw sequences.
Fold
Liaand Pocket
Limuod
7.59
RESiffuaon (apuzl)
30.55
0.31
614/1.1
RF-iifiuemn impeni)
$8.99
17.5%
3.5%
0199)
0.50
64.114.5
whrs cond.
71.3%%
4,3%
1,24
1117
033
794735
a 6(0-6.2)
S6.256
2.854
0.855
0.17
0.83
9.4/57.2
a (0022-0.3)
00,815
0,36
0.9%
0126
0.8
61.7/194
6000-1100 1.00
00,85
11.08
3.3%
0.15
0.89
$24/95
Table 2: Global Control of Protein Fold We report three success rates, diversity, novelty and the average a/3 ra- tios in our generated samples.
Our model correctly follows the conditioning signals, successfully producing proteins with distinct secondary structure preferences, shown in the a% / 13% columns of the table. These preferences further influence the design success rates, where a higher proportion of B structures corresponds to lower success rates. Moreover, the setting of a ratios (0.2-0.8) achieves the highest diversity and novelty, as it covers the broadest range. Figure 6 illustrates the diverse structures of Pallatom- I Ligand's 's output under different SS control.

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4.4
CONDITIONAL GENERATION WITH LIGAND SOLVENT ACCESSIBILITY
1.0
1.0
1.0
0..6
0.6
0.6
0.4
044
04
0.0g
40
0
20
60
0.
20
30
SASA (A+)
SASA (AP)
SASA
Figure 5: Atomic Control of Ligand Solvent Accessibility The top panel shows three selected small molecules with predefined ligand SA labels. Fully buried atoms are colored mint (SA label 0), half buried atoms are colored yellow (SA label =0.5), and fully exposed atoms are colored in red (SA label =l). The distribution of ligand solvent accessible surface area (SASA) across forty designs is plotted for each small molecule. The bottom panel shows zoomed-in views of representative binding pockets, with the protein surface contour displayed in purple.
To further evaluate the model's ' capacity for conditional generation at the atomic level, we designed proteins to bind specific ligands with predefined solvent accessibility (SA) labels. We defined per- atom SA labels for three target ligands (FMN, DOG and LDP in Figure 5) based on their chemical properties and binding patterns in natural proteins. For each ligand, we generated
proteins of fixed length. The distribution of the ligand's solvent-accessible surface area (SASA) in the gener- ated proteins (Figure 5) aligns closely with the design control, confirming that the model produces proteins with suitable binding pockets for all three tasks.
5 CONCLUSIONS
We present Pallatom-Ligand, an all-atom diffusion model for the end-to-end generation of protein- ligand complexes. Conditioned solely small-molecule target, our model generates complete complex structures with atomic detail for the protein backbone and side chains. In comprehensive in silico benchmark. Pallatom- Ligand outperforms the previous state-of-the-art. RFDiffusionAA and RFDiffusion2,, across a wide range of metrics. Furthermore, the all-atom framework enables more expressive design conditions both atomic and residue levels, as demonstrated by the model's control over the protein fold generation and ligand solvent accessibility.
By enabling end-to-en design ligand-binding binding proteins with minimal prior assumptions, Pallatom- Ligand opens new avenues for exploring protein function space. Its strong in silico performance and expressive controllability make directly applicable biosensor development and other biotechno- logical applications.
While Pallatom-Ligand represents significant advance, future work will focus on two key rections. First, aim to broaden the scope of supported biomolecular complexes beyond pro- smill molecule systems, including nucleic ceid-containing assemblies, covalent ligand bind- ing, and proteins with noncanonical amino acids. Second, plan to scale training to larger distilled protein-ligant datasets (Lemos et al., 2025), in order to further push the performance limits of all- atom ligand binding design.
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ACKNOWLEDGEMENT
We thank Yifan Qin, Ge Tian, Ruiyi Zhang. Jiakai Zhang and anonymous ICLR reviewers for helpful feedback and discussions, and the ShanghaiTech HPC platform for generous technical support. This work was supported by 2024 Shanghai Action for Science, Technology and Innovation Program of Natural Science Foundation of Shanghai (24JS2820100).

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A
APPENDIX
CONTENTS
1
Introduction
2 Related Works
2.1 All-Atom Protein Design Methods
2.2 De novo Design of
Proteins
3
Method
3.1
A unifying scheme for encoding protein-ligant hybrid system
3.2
Pallatom-Ligand transformer architecture
3.3
Conditions on global and atomic scales
6
3.4
Training strategies
4
Experiments
4.1
Metrics
4.2
Ligand-binding protein generation benchmark
4.3
Conditional Generation with a ratios
4.4
Conditional generation with ligand solvent accessibility
10
5
Conclusions
10
Acknowledgement
11
A
Appendix
15
A.I Ethics Statement
16
A.2 Reproducibility Statement
16
A.3 The Use of Large Language Models
16
A.4 Data Curation
16
A.5
Visualization of Fold Conditioned Samples
17
A.6 Architecture Details
17
A.7 Sampling Parameters
18
A.8
Small-Molecule Target Description
18
A.9 Details For Baselines' Evaluation.
20
A.9.1 REdiffusion-AA
20
A.9.2
RFdiffusion2
20
A.10 Definition of Evaluation Metrics
20
A.11 Detailed Results for Our ligand-Binding Protein Design Benchmark
21
A.11.1 REdiffusionAA
21
A.11.2 RFdiffusion2
21
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A.11.3 Pallatom-Ligand (w/out SA)
22
A.11.4 Pallatom-Ligand (w/out SA) MPNN
22
A.11.5 Pallatom-Ligand (w/ SA).
22
A.11.6 Pallatom-Ligand (w/ SA) + MPNN
22
A. Inference Latency and Sampling Efficiency
22
A.13 Generalization Analysis: Rare and Unseen Ligands
23
A.13.1 Generalization to Rare Ligands (Few-shot Regime)
23
A.13.2 Generalization to Novel Ligands (Zero-shot Regime)
................................
24
A.14 Training Objective
24
A.15 Training Details
25
A.16 Ablations
25
A.16.1 Transformer Architecture
25
................................
A. 16.2 Dual-Objective Training Strategy
26
A.17 Impact of Data Imbalance Model Collapse
26
A.1 ETHICS STATEMENT
This work focuses on developing machine learning methods for protein- ligand complex genera- tion. Our study does not involve human subjects, animal experiments, or private or sensitive data. All datasets used in this work are publicly available from prior publications and are widely used within the structural biology and machine learning communities. The research does not introduce new safety, security, ethical risks beyond those already associated with existing protein design methodologies. We acknowledge that downstream applications protein design may carry poten- tial dual-use concerns; however, contributions methodological and intended for advancing fundamental research.
A.2 REPRODUCIBILITY STATEMENT
We have taken several steps ensure the reproducibility of work. All datasets used in our experiments are publicly available, and the details of dataset preprocessing are provided in Appendix A.4. The full model architecture, training setup, and hyperparameter choices are described in Section 3.2 and Appendices A.6, A.14, and A. Evaluation protocols are included in Appendix A.9. Sampling configurations are included in Appendix A.7.
A.3 THE USE OF LARGE LANGUAGE MODELS
used solely for editorial purposes in this work, specifically to polish language and improve the clarity of writing. They were not involved in generating ideas, designing methods, conducting experiments, analyzing results. The scientific contributions and findings presented in this paper are entirely the work of the authors.
Large language models (LLMs)
A.4 DATA CURATION
Protein-Ligand Complexes from from PDB. We curated our protein-ligand dataset based on the method- ology of AlphaFold3 (Abramson et al., 2024). Each biological assembly was treated as a single data point; for PDB entries containing multiple assemblies, only the first was used. The following filter- ing criteria were applied:
Experimental Method: Structures determined by X-ray diffraction or electron microscopy.
Resolution: A resolution better than 3 Á.
Sequence Length: A minimum of 32 residues.
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Complex Size: Complexes larger than MB were excluded.
Clashing Chains: Chains with more than 30
This procedure resulted in final dataset of 66,201 protein-ligand complexes. Protein Structure Distillation from AFDB. Following recent works (e.g., Genie2 (Lin et al., 2024), Proteina (Geffner al., 2025b), and Pallatom (Qu et al., 2024)), we trained our model on synthetic protein structures from the AlphaFold Database (AFDB). To ensure the quality of these distilled structures, we applied the following filters:
Quality and Length: An average pLDDT score greater than 80 and a maximum sequence
length of 256 residues.
Minimum Length: A minimum sequence length of 32 residues.
Disordered Regions: Disordered regions at the N- and C-termini were trimmed.
Redundancy Removal: Redundant structures were removed using the FoldSeek easy- cluster algorithm, with a TM-score threshold of 0.7 and a sequence coverage of 0.9.
Ultimately, this process yielded set of 495,322 protein monomers.
A.5 VISUALIZATION FOLD CONDITIONED SAMPLES
8S cond
SS cond
Figure 6: Fold Conditioned Higand-Bindingg Proteins Designed by Pallatom-Ligand via differ- ent ratio SS cond O refers to a ratio ranging from 0-0.2, SS cond refers to a ratio ranging from 0.2-0.8 and SS cond 2 refers to ratio ranging from 0.8-1.0.
A.6 ARCHITECTURE DETAILS
We use the following channel dimensions: 384 for tokens, 256 for atoms, 128 for token pairs, 16 for atom pairs, and 32 for the time embedding. For the initialization of our representations and condition embeddings, encode the following information:
Reference conformer positions, element types, charges, degree information, atom indices, and SASA features into the atom condition embedding
Noisy coordinates, the atom condition embedding, and the token condition embedding into the atom representation q.
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The aggregated atom representation into the token representation
Relative token/resiiuueChainn indices and the token pairwise distogram of noisy coordinates into the token pair representation 2.
Atom pairwise distances in the reference conformer, relative atom positions, and the ex- panded token pair representation into the atom pair representation p.
For token-level attention, we use 8 attention heads, dropout of 0.15, and no query-key (QK) bias. For atom-level attention, we also use 8 attention heads and a dropout rate of 0.15, but with QK bias enabled. the triangle block, we use 4 attention heads, a dropout rate of 15, and no QK bias.
The 'Attention' mechanism in Eq. and the "SparseAtttentoon mechanism in Eq. 4 share the same fundamental calculation. The only difference is the application 32 x 128 block mask in "SparseAtttntioon" The detailed implementation is provided in Algorithm 1.
Algorithm Attention with Pair Bias
Input: Single representation z, padding mask Tmask, pair representation condition embedding cond, (optional) attention mask B.
1:
Output: Update to the single representation Ar.
# Layer normalization of inputs
2: x < (a, cond)
D. Adaptive Layer Normalization
3: y +- LayerNorm(y)
# Compute pair bias from pair representation
4: Whiss E RCyH Initialized weights
H is the number of heads b Pair bias for attention scores
5: bias + EiEssumm( fiinsum('gkic, ch - hak', y, Whins)
# Prepare attention mask
6: padding mask + (1 Tmask) x -10
> Convert binary mask to attention bias Convert binary mask to attention bias
7: attention mask + 3 padding mask
# Multi-Head Attention with pair bias
8: Aa f- attention_mas _mask)
= x, key
H, value
x, bias bias, mask
Apply output gate
9: gate t (LLinaargae
a is the sigmoid function Element-wii product
10: Ax +- Az @ gate
11: return Ar
For "Attention" (Fq. 1), the inputs to the algorithm are x [a,, cond y and cond t. For "SparseAttention" (Eq. 4), the inputs are x q. y = p. and cond = c, with an additional block mask input 8 of size 32 128. This setup is consistent with that of AlphaFold3 (Abramson et al., 2024) and Pallatom (Qu et al., 2024).
The details of the "TriangleAttentionn mechanism (Eq. 8) described in Algorithm 2.
The "Transition module," referenced in Eq. and Eq. 5, a conditioning- aware feed-forward net- work designed to update input representation a based conditioning vector s. The details are described in Algorithm 3.
A.7 SAMPLING PARAMETERS
Our sampling scheme is adopted from Pallatom (Qu et al., 2024), and we keep the notation consistent for clarity. The hyperparameters were set as follows: an initial noise level of tu 0.4, step scale of n 1.75, and a total of N 500 sampling steps. We utilized the same noise schedule as the original work without further tuning. The inference time is reported in Appendix A.
A.8 SMALL-MOLECULE TARGET DESCRIPTION
IAI (C22H1 is mid-sized (442.4 Da), neutral polar molecule with balanced hydrophobicity. Its hydrogen-bonding capacity includes 2 donors and 7 acceptors. As phosphodiesterase inhibitor,
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Algorithm/ 2 Conditional Triangle Attention
Input:
Pair activations rbf activations Zehr. condition embedding t, pair mask m. Output: Updated pair activations z'.
# Normalize inputs.
1: z < LayerNorm(z)
2: 2rb f- AdaLN t, use uis_bias=True)
Construct attention bias from pair mask.
3: bias (m x 10
4: bias +- ExpandDims (bbibaas,,,,, (bias, axis = [1,2))
shape IN,
# Compute non-batched bias from affine activations.
5: Initialize weights W2a E RC,xH with std 1/vC.
6: nonbatched.i biis +- Einsum( gkc, ch - hig', 2 W2a) # Multi-head attention with affine bias.
7: 2 t MultHeeaAAttention(
2, 2, = 2, bias = bias, extra bias nonbatched bias)
8: return
Algorithm
The Transition Module
Input:
Input representation conditioning vector s.
Output: Updated representation Aout
# 1. Adaptive Layer Normalization.
1: 7, B +- Linear(ss) (s), Linear(s)
Produce gain and bias from condition s.
2: awerm f- 7 LayerNorm(al a) B
D O denotes element- wise product.
2. Gated Feed-Forward Layer (SwiGLU).
3: gate t- Swiis(Linearr (anorm) ))
4: val + Linear, (Anom)
5: b gate val
# 3. Final Gating based on condition.
6: output gate + (
7: output val f- Linear,(b)
8: aou <- output-taat output_val val
9: return acut
IAI has been investigated for treating neurological conditions like schizophrenia and Huntington's 's disease.
SAM (S-Adenosylmetthionnne)) is moderately large (398.4 Da), neutral molecule with high polarity and strong hydrophilicity, containing 4 hydrogen-bonding donors and 10 acceptors. Its primary biological role is as a universal methyl donor in one-carbon metabolism. This function extends gene regulation via riboswitches in bacteria and the regulation of nucleic acid methylation and amino acid metabolism in eukaryotic cells.
OQ0 (22cH2CCIFNo is relatively large (532 Da), nominally neutral molecule that exhibits charge resonance, enabling ionization in different environments. It displays moderate polarity and balanced hydrophobicity, with yydrogen-bonding capacity of donor and 8 acceptors. OQO has been investigated as FXIa inhibitor for treating stroke and myocardial infarction.
FAD FMN Flavin mononucleotide (FMN) and flavin adenine dinucleotide (FAD) are flavin- derived cofactors that serve as prosthetic groups in numerous redox enzymes. Both are derivatives of vitamin B2 (riboflavin), formed via phosphorylation
FMN further adenylylation to FAD. FMN has molecular weight of 456.3 Da and contains 6 hydrogen- cating strong hydrophilicity. FAD is larger, with a molecular weight of 785.5 Da, 9 hydrogen- donors, 20 acceptors, reflecting very high polarity and aqueous solubility. Overall, both molecules are highly polar, mid- to large-sized cofactors with substantial hydrogen-bondinn potential, consis- tent with their roles enzyme- mouude prosthetic groups in redox biochemistry.
donors and 10 acceptors, indi-
DOG (Digoxigenin C23H240;) is a widely used small molecule biochemical assays. It is typ- ically conjugated to biomolecules, such proteins nucleic acids, to enable detection via high-
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affinity, specific anti- anti-digoxigenin antibodies. From physicochemical perspective, DOG is a rel- atively small (390.5 Da), moderately polar molecule with balanced gydrogen-bonding capacity(3
hydrogen-bon bond donors and 5 acceptors).
LDP (L-Dopamine) is ncurotransmitter that transmits chemical signals between neurons and plays central role in reward processing. From a physicochemical perspective, LDP is small (153.18 Da), polar molecule with balanced hydrogen- bonding potential(3 donors and 3 acceptors).
SRO (Serotonin 5-HT) is monoamine neurotransmitter with broad functions in both the central nervous system (CNS) and peripheral tissues. From a physicochemical perspective, 5-HT has a molecular weight of 176.21 Da, contains hydrogen-bond donors and acceptors.
A.9
DETAILS FOR BASELINES' EVALUATION
A.9.1 RFDIFFUSION-AA
For the evaluation REdiffusion-AA, we closely followed the protocol from the Hem binder design study (Krishna et al., 2024). The procedure consisted of the following steps:
Conformer Sampling: We first sampled 10 ligand conformers. This step was necessary because REdiffusion-AA does not alter the input ligand's conformation.
Backbone Generation: For each of the 10 conformers, we designed 10 protein backbones using the default configuration of RFdiffusion-AA. yielding total of 100 backbones.
Sequence Design: Subsequently, for each backbone, we designed a single sequence using Lig- andMPNN, providing the corresponding ligand structure conditional input.
Finally, the structures for all 100 designed sequences were predicted using AlphaFold3 without MSAs. For each sequence, we generated structures using fixed random seed of 1.
Structure Prediction:
A.9.2 RFDIFFUSION2
Our evaluation protocol for RFdiffusion2 followed the small .mmoeccll binder rasa buried example from the official open_source
json file.
Backbone Generation: The initial data preparation involved selecting high-quality protein-ligand complexes for eight distinct small molecules from the Protein Data Bank (PDB). In cases where a protein bound multiple instances of the same ligand, one complex was arbitrarily selected. The geometric center of the ligand's atoms was defined as its origin point (ORI) and included the PDB file to guide scaffold generation. To promote ligand encapsulation, we adopted the recommended settings from the example, setting the RASA value to Following this procedure, we generated 100 scaffolds for each of the eight ligands.
Sequence Design Structure Prediction: The subsequent steps of sequence design and structure prediction were identical to the protocol used for RFdiffusion-AA: sequences were designed using LigandMPNN, and their corresponding structures were predicted with AlphaFold3.
A. DEFINITION OF EVALUATION METRICS
To enhance the accessibility of our paper for readers across different backgrounds, we provide de- tailed definitions of the technical metrics used in our evaluation (Section 4.2).
Protein Scaffold Metrics:
Ca-RMSD: The Root-Mean-Squar Deviation (RMSD) of the protein backbone Co atoms
between the generated structure and the reference ground truth, measured in Angstroms
(A). Lower values indicate higher structural fidelity to the native conformation.
Protein PLDDT: The average predicted Local Distance Difference Test (pLDDT) score calculated over all protein residues. This score ranges from 0 to 100 and serves confidence metrie for the local structural quality predicted by the folding model (e.g., Al- phaFood2/EESFFodd) Higher values signify greater confidence.
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We assess the structural diversity of the generated samples by clustering them using Foldseek (van Kempen et al., 2024). The metric reports the total number of distinct clusters formed; a higher count indicates that the model can generate more diverse set of structural solutions.
Diversity:
Novelty: This metric quantifies the dissimilarity of generated samples relative to known structures in the PDB. For each generated sample, we compute the maximum TM- score (Zhang & Skolnick, 2005) against the entire PDB database. The reported novelty the average of these maximum TM-scores. Since higher TM-score implies similarity, lower average score indicates that the generated proteins represent novel folds unseen in the training distribution.
Ligand Pose Metrics:
The Euclidean distance between the geometric centroid the gener- ated ligand and that of the reference ligand. This metric measures positional consistency, effectively identifying large translational deviations of the generated ligand relative to the binding pocket.
Ligand Deenter:
Ligand RMSD:
The Poot-Mean-SSqaal Deviation of the ligand's s heavy atoms between the generated sample and the reference structure. Crucially, this is calculated after superimpos- ing the generated protein backbone onto the reference backbone. A smaller value indicates high accuracy in both the internal conformation and the relative orientation of the ligand within the pocket.
Ligand PLDDT: The average pLDDT score calculated over all ligand atoms. Similar to the protein score, this reflects the model's confidence the predicted coordinates of the ligand.
Binding Interface Metries:
ipAE (Interface Predicted Aligned Error): This metric estimates the positional error (in A) between pairs of residues and ligand atoms across the protein-ligand interface. Lower ipAE values indicate higher confidence in the specific interaction geometry and the relative packing of the ligand against the protein surface.
A.11 DETAILED RESULTS FOR OUR LIGAND-BINDING PROTEIN DESIGN BENCHMARK
A.11.1 RFDIFFUSIONAA
Ca-RMSD
protein-pl.DIT
Divenity
Novelt
ligund-Deenter
Biand-RMSD
ligind-plDDIT
IpAR
Fold
Success Rate
Pocket
Foee
PMN
445=42
T0res
D0G
6.755675
7930 11.33
0.79
4.332
12.57 K440
62.22
12.04
3.53
30.0%
300%
0.0%
1MP
421 4405
$4.5
10.72
0.33
0.73
6.56
6.14
N0.1 13.42
6:00 3.35
36.72
707%
00%
SKO
1 4.95
51
11.93
0.28
0.75
738 7.02
9005
67.75 11.69
639 3.62
36.0%
7.0%
2.0%
FAD
4.11
3.77
80.25
$ 8.81
0.13
0.73
6.42 4,50
11.31 4,45
56.19
3.34
26.0%
0.0%
0.0%
SAM
44.95
50.95 12.00
0.15
0.73
6.39 4.95
4,45
63.55 11.41
2.20
30.0%
0.0%
0.0
4.44
83.36 10.00
0.32
0.75
10.23 7,21
12.09 6.35
02.17 12.95
7.37
27.0%
2.0%
0.0%
Avg.
A.11.2 RFDIFFUSION2
Ca-RMSD
poDtein-pL.DDT
Divenity
Novelty
Figond-Dentter
Egand-RMSD
Rigind-L.DDD
Success
FMN
4.17
$4.432 11.75
0.34
0.79
3.32
6.77 3.35
66,90 14.50
6.32
57.0%
16.0%
6.0%
DOG
1.38 1.55
$7.83 11.35
0.49
0.81
3.39 4:5.1
6.76 5.25
72.87 13.56
5.76 3.59
65.0%
24.0%
3.0%
TDP
1.624.22
89.6 1044
0.39
0.30
4.50
4.16
77.33 12.97
61.0%
100%
40%
1AI
2,68 = 3.99
90.19
0.59
0,50
2.94
6,95 3.64
70.41 10.81
5.48 1.97
68.0%
11.0%
400%
FAD
5.0
S1.07 10.18
0:29
0.79
1.9
9.47 = 3.78
$8.85 11.16
7.52 2.71
33.0%
1.0%
0.0%
000
10.36
6,30
Avg
3.61
$7.00 10.76
0.30
0.30
4:07
7.12.44.13
N0.37 13.79
7.98&222.00 2.08
55.5%
17.5%
3.5%
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A.11.3 BALLATOM-LIGAN (W/OUT SA)
CaRRMSD
protein-pL.DDt
Divensty
Novelty
ligand-Deenter
ligand RMSD
lignddRMMSD
ligand-pLDirt
iPAE
Success Rate
Fold
Pocket
Fosc
FMIN 4.44
S433 10.01
0.16
0.34
12.7 10.74
66.87 12.39
6 2.86
5910S
7.0%%
4.17%
0.36
12,45 10.35
9,45
67.45
10.73
535
75.0%
707
1.0%
TDP
107 2.17
90.9 5.67
0.15
0.39
1295 a K:4K
1365 K.02
74.24
4.18 1.78
71.0%
200
0.05
SRD
2,25
2.77
89.50 6.22
0.15
0.91
1205
13.85 1.74
15.55
4.35 2.00
71.0%
5078
2076
IAI
2.39
2.55
$6.36 8.84
0.18
0.83
4
14.93 8:25
65,66 11.69
2.79
65.0%
0.0%
0.0%
PAD
1
13.37
3.22
$1.0%
0.78
SAM
3.00
2 4.002
#4 10.87
0.15
0.82
10.42 4 9.43
12.65 4 8.62
71.57 12.37
5.87233.1
60.0%
6.0%
2.0%
0g0
2.76 3.65
56.26 9.89
0.17
0.82
10.97 4 9.72
13.34 8.79
64.84
4 13.62
0.30
2.92
63.0%
8.0%
1.0%
Avg.
2 3.84
84.48
0.17
9.08
13.58 8.85
07.955 12.73
2.34
55.7%
3.9%
0.9%
A.11.4 PALLATOM-LIGAND (W/OUT SA) MPNN
Ca-RMSD
protein-pL.DTT
Diversity
Novelty
ligand-Dhenteer
ligand RMSD
ligand-pLDDbT
iAAE
Fod
Pockes
Pose
FMN
148
$9.89 7.76
0.16
0.84
8.55
10.56 9.38
7260 T1.00
4.43 =:24
93.0%
15.0%
9.0%
DOG
8:30
7.41
14.0%
TDP
1.91
2,50
0.13
8.30
11.1=
04.0%
17.0%
20%
SKO
1.31
1.06
94,25 1.81
0:15
0.91
1..1029.54
13000 838
79.77
2.82 0.52
85.0%
11.3%
1.0%
TAI
1,20
1.20 1.22
92 00.601 3.88
0.18
0.83
8.31 + 8.69
11:15 8.05
3.99 1.33
90.0%
14.0%
1.0%
FAD
3.34
86.00
0.19
7.22
10:1==000
12.51
91.0%
20%
SAM
91.46
0.15
10:20
78.04:8.511
3.36
93.0%
19.0%
6.0%
000
1.11+1.77 1.71
0.17
0.82
6.38+6.36
8.97 - 6.01
69.83
10:97
4.59
1.73
90.05
16.0%
2.0%
A0g
1.39
91.55 + 6.33
0.17
0.85
8.75
10.78 7.99
73.40 10.61
4.07
1.98
89.6%
13.8%
3.0%
A.11.5 PALLATOM-LIGAND (W/ SA)
pootein-pl.DbbT Ca-RMSD
Diversity
Novelty
Lggand-Deenter
ligand Baand-RMSD
ligund-pL.bbbx
IpAE
Pocket
Powe
FMN
4315
NI 10.38
0.13
0.81
7X3 X.05
VI2 7.68
6301 = 12.99
733 5.24
43.0%
5.0%
2.0%
D00
360 4.288
K2.12 7.72
0.10
0.82
K.57 x 9.066
1081 8
63.66 x: 10:20
2.99
30.15%
370%
0.7P
73,26
2.45
33.0%
100%%
SRD
7:51
38.0%
8.0%
30%
TAT 4.99
70.15 12.28
0.15
0.80
8.7% a 7.56
11:37 7.06
60.54 16.43
7,88 3.67
38.0%
7.0%
3.0%
PAIS
71,09 11.89
0.24
0.78
130 + 7:49
3011 12.27
10:47 = 3333
13.0%%
070%%
00%
36353665
14.13
7.36
11:36=7.0
08.66 11.36
0.35 2.74
46.0%
4.0%
008
000
4.39 4.56
79.44 10.31
0:15
0.78
6.28 5.35
9.53
59.78 = 15.10
8.06 3.25
41.0%
800
3.0%
Agg
3.95+4.0
81.84 + 10.45
0.14
0.81
8.37 + 8.01
10.59 7.47
64.00 + 14.43
7.13 3.49
47.4%
4.9%
1.4%
A.11.6 CALLTTO--LIAANE (w/ SA) + MPNN
Ca-RMSD
Polein-pl.IDDD
Diversity
Novelty
ligand-Deenter
ligand-p..DDT ligand-RMSD
ipAF
Suevess Rate
FMN
R0.0++
1,2++466
5.31
7261
17.0%
DOG
1
0.10
0.32
4.12
1.26 5.38
7194=
4.45
85.0%
9.0%
LDP
1.26 1.50
94,46 4.533
0.11
0.85
4,60 5.662
6.15 5.08
80.02 = 7.47
2.4 1:35
90.0%
38.0%
9.0%
0.93
93.08 3.98
0.84
6.80
6.18
79.39 6.74
3.36 1.30
36.0%
IAI
2.67
3.13
0.15
17.0%
FAD
1.80
1.89
85.84 6.39
0.24
0.78
4.81 a 4.89
4.3825:22
65.06 = 11.12
5 1.94
78.0%
6.0%
2.0%8
SAM
1 2 1.79
90.58&1.5.2
0.13
0.82
6.82 7.03
6..51
77.25 23.17
4.13 1.83
87.0%
22.0%
7.0%
000
1.22
90.35 3.89
0.15
0.78
2.14
6.19
68.89 & 12
4.97 16
90.0%%
4.0%%
K0PS
Avg.
72.78 10.6
A. 12 INFERENCE LATENCY AND SAMPLING EFFICIENCY
In this section, we evaluate the computational cost from two perspectives: the raw inference latency per sample and the total computational budget required to achieve successful designs (sampling efficiency).
Inference Latency. First, we compare the inference speed for generating a single protein-ligand complex. The sampling protocol for each method follows the configuration described in Ap- pendix A.9. All benchmarks were performed on single NVIDIA RTX 5880 GPU with batch size of 1. As shown in Table 3. our method incurs a marginal increase in latency (135s) compared to RFdiffusion2 (118s).
Sampling Efficiency. While diffusion models are inherently iterative, practical usability should be evaluated based on Sampling Ffficiency- defined as the total computational time required to yield
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Method
RFdiffusionAA
RFdiffusion2
Ours
Inference Time (s/sample)
247s
118s
135s
Table Comparison of inference time per sample (batch size 1).
fixed number of successful designs, Although our per-sample latency slightly higher, our method significantly reduces the total computational cost due to superior design success rate.
We quantify this efficiency by calculating the total time Toaal needed to obtain 100 successful can- didates:
100
Twaal
Vinference
(9)
Success Rate
Based on the success rates reported in the main text (3.5% for REdiffusion2 and 6.4% for Ours):
RFdiffusion2: To obtain 100 successes, it requires 2, 857 attempts.
2, 857 attempts x 118 s/sample zs 337, 126
Ours: To obtain 100 successes, it requires R 1, 1..622 attempts.
1, 562 attempts 135 s/sample 210, 870
Consequently, despite a 14% increase in per-step latency, our model reduces the total computational
time by approximately 37 % compared REdiffusion2. This demonstrates that for practical design campaigns, our method is the more computationally efficient choice.
A.13 GENERALIZATION ANALYSIS: RARE AND UNSEEN LIGANDS
critical challenge in protein-ligand design is the scarcity of training data and the long-tailed dis- tribution of ligand types in the PDB. In this section, evaluate the generalization capability of Pallatom-Ligand from two perspectives: few-shot learning on rare ligands and zero-shot learning on completely novel small molecules.
A.13.1 GENERALIZATION TO RARE LIGANDS (FEW-SHOT REGIME)
The training dataset exhibits highly imbalanced distribution. To assess whether model merely memorizes abundant protein-ligand pairs or learns generalizable physical patterns, we analyzed the occurrence frequency of the 8 ligands used in our main benchmark (Figure 4 in the main text).
As shown in Table 4, the distribution is extremely skewed. Common cofactors like FAD and FMN appear hundreds thousands of times, whereas ligands such DOG, IAI, and OQO extremely rare. Notably, IAI and OQO appear only once in the entire training set. Despite this scarcity, the results presented the main text demonstrate that Pallatom-Ligand achieves high design success rates on these rare targets, comparable to its performance on data-rich ligands. This suggests strong few-shot generalization capabilities enabled by our dual-sampling strategy.
Ligand
Count
Ligand
Count
FAD
1826
SRO
23
FMN
1569
LDP
13
SAM
599
DOG
TAI
1
OQO
Table 4: Frequency of benchmark ligands in the training dataset The distribution highlights the few-shot nature of the task for ligands like LAI and OQO.
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A. 1312.2 GENERALIZATION TO NOVEL LIGANDS (ZERO-SHOT REGIME)
To rigorously test the scalability of our method to unseen chemical structures, we conducted addi- tional experiments on four targets that were completely absent from our training set. These ligands represent amall-molecule ligands distinct from standard PDB entries:
Janelia Fluor Dyes (JF585, JF635, JF711): Janelia Fluor dyes are synthetic chemical compounds. Their chemical structures have been optimized for fluorescence brightness, photostability and cell permeability.
JF585: An orange fluorescent dye featuring xanthene scaffold modified with 5,3-difluoroazctidines, representing a distinct structural motif compared to natural metabolites.
JF635: A far-red dye built on silicon-rhodamine (Si-rhodamine) scaffold. The in- corporation of a silicon heteroatom introduces unique steric and electronic properties. JF711: A near-infrared dye characterized by a phosphine oxide-containing rho- damine scaffold. Its bulky structure poses significant challenge for binding pocket design.
Deschloroclozapine (DCZ): Deschloroclozapine (DCZ), a metabolite of Clozapine, is a highly potent small-molecule ligand widely used in chemogenetics.
We applied the same evaluation protocol in the main benchmark. Table 5 compares the Ligand- Pose Success Rate of our method against state-of-the-eee baselines.
Method
JF585
JF635
JF711
DCZ
RFdiffusionAA (mpnnl)
0.0%
0.0%
0.0%
0.5%
RFdiffusion2 (mpnn1)
10.2%
11.1%
4.5%
2.0%
Ours (w/out SA)
5.0%
0.0%
1.0%
0.5%
Ours (w/out SA) (mpnn
11.1%
7.0%
4.0%
1.0%
Ours (w/ SA)
3.0%
2.0%
4.0%
2.0%
Ours (w/ SA) (mpnn1)
22.4%
16.0%
17.0%
12.0%
Table 5: Ligand-Pose Success Rate on unseen (zero-shot) targets RFdiffusionAA and RFdif- fusion2 serve baselines. Our method (especially with MPNN sequence redesign) significantly outperforms baselines on these novel chemotypes.
observed, baseline methods like REdiffusionAA struggle with these unseen synthetic scaffolds, yielding near-zero success rates. In contrast, our full pipeline (Ours w/ SA + mpnn1) achieves the highest success rates across all four targets. This demonstrates robust zero-shot generalization, confirming that Pallatom-Ligant learns fundamental atomic-level interaction patterns rather than overfitting to specific ligand types seen during training.
A.14 TRAINING OBJECTIVE
Our diffusion framework is primarily based on the Elucidating Diffusson Model (EDM) (Karras al., 2022). Let R (N,)aa denote the atomic coordinates of protein-ligand complex, where N₂ = 14L+1, is the number of protein residues, and / is the number ligand atoms. Furthermore, let be the standard deviation of the noise, and let (x) represent the binned pairwise distance between the central atoms of tokens i and
Our composite training objective is defined
Loty Lai + 0.1 + O. smmoth + 0.03 distogram
(10)
where the individual loss terms are defined as follows:
Laif 0² + 07 data
N.
[|xdenoised
denoised
xx1112
(11)
Toeg 1(0 x.1) CrossFntropy(aatypeeee aatype
(12)
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distogram ErossEntropyyDD... (x). Dr, (x))
(13)
and LsmoothJä8 is the smooth-LDDT loss as described AlphaFood3 (Abramson et al., 2024).
The atom-specific weights in the diffusion loss are set follows: w; = 10 for ligand atoms, wy for protein atoms within 4 Á of any ligand atom, and wy = for other protein atoms. This weighting scheme prioritizes the accurate placement of the ligand and its binding pocket.
A.15 TRAINING DETAILS
The model was trained for 70,000 steps using the AdamW optimizer. We set the learning rate to x 10-3, betas to (0.9, 0.95), and weight decay to 0.001. The gradient norm was clipped at 10. Training was conducted on 14 NVIDIA H20 GPUs with a per- batch size of 54 and 2 gradient accumulation steps, resulting in an effective hatch size of 108. To balance performance and effi ciency, utilized mixed-precision strategy: all token-level operations, except for the QK? ein- sum, were performed in b ofloatle while all atom-level operations were performed in An Exponential Moving Average (EMA) of the model parameters was maintained throughout train- ing with decay rate of 0.999.
A.16 ABLATIONS
In this section, we perform ablation studies to validate the design choices of our model architecture and training strategy.
A. 1.11 TRANSFORMER ARCHITECTURE
As detailed in Section 3.2, our proposed model employs a "triple-attention" transformer architecture that integrates token-, atom-, and pair-level streams. To elucidate the individual contributions of these components and verify their necessity, we conducted ablation studies on a scaled-down version of the model.
We compared the Baseline (consistent with the proposed full architecture) against three variants: (1) w/o Token Attn, where the token-level attention is removed: (2) w/o Atom Attn, where the atom-level attention is removed; and (3) w/o Triangle Attn, where the triangle attention mechanism for pair updates is omitted.
The training dynamics are illustrated Figure 7, and the quantitative evaluation of sample quality presented in Table 6. For evaluation, we generated 50 structures for each target in the benchmark with a fixed sequence length of 192. We evaluated the raw generated sequences directly without the intrinsic generative capability of the architecture.
applying MPNN refinement
Model
Ca-RMSD (Å)
Lig-RMSD (Å)
Baseline
6.78
12.00
w/o Token Attn
7.87
14.35
w/o Atom Attn
9.19
13.35
w/o Triangle Attn
9.91
13.69
Table 6: Ablation study architecture com- ponents Metrics are evaluated on the raw out- puts from each model.
Figure 7: Training dynamics. The full archi- tecture (Baseline) exhibits faster and more sta- convergence compared to ablated variants.
As shown in Table 6, the Baseline architecture yields superior sample quality compared to all ablated variants. These results confirm that the synergistic integration all three streams is essential for the model's performance.
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A.16.2 DUAL-OBJECTIVE TRAINING STRATEGY
As discussed in Section 3.4, protein-ligand dataset exhibits a severe distributional dichotomy: clus- tering by global fold biases the model against diverse ligand interactions, while clustering solely by local interfaces risks structural collapse. To investigate the necessity of our dual-objective formula- tion, we compared our balanced approach (1:1 ratio) against two extreme sampling regimes:
Fold-Centric (Mode I only): Training exclusively with structure-based clustering.
Interaction-Centric (Mode 2 only): Training exclusively with ligand-based clustering.
Crucially, we evaluated performance separately on "Abundant" targets (e.g., FMN, FAD) and "Rare" targets (e.g., DOG, IAI) to highlight generalization capabilities across different data densities. The results are summarized in Table 7.
Ca-RMSD (Å)
Ligand-RMSD (A)
Strategy
Abundant
Rare
Abundant
Rare
Mode 1 only (Fold-Centric)
5.67
5.16
13.24
14.50
Mode 2 only (Inter.-Centric)
8.23
8.04
12.35
12.91
Baseline Dual (1:1)
6.79
6.85
12.93
10.59
Table 7: Impact of Training Sampling Strategy. We report Ca-RMSD (backbone quality) and Ligand-RMSD (binding pose quality) on abundant and rare target subsets. The balanced strategy offers the best trade-off, crucial for generalization rare ligands.
The results clearly demonstrate the distinct roles of each sampling mode and the synergy of the dual-objective framework:
Limitations Fold-Centric Training: While Mode I achieves the lowest Ca-RMSD, ensuring high backbone quality, it generalizes poorly to ligand binding on Rare targets (Lig- RMSD degrades to 14.50 Á). This confirms that relying solely on global fold clustering exacerbates data imbalance, causing the model to neglect rare interaction patterns.
2. Limitations of Interaction-Centrre Training: Conversely, focusing exclusively on bind- ing interfaces (Mode 2) leads to a deterioration in backbone geometry, evidenced by in- creased Ca-RMSID (>8 Å) across both groups. This lack of stable global fold prevents the formation of coherent binding pocket, which indirectly compromises interface stabil- ity (Lig-RMSD worse than Baseline).
3. Effectiveness of Balanced Sampling: The proposed 1:1 ratio effectively acts a syn- ergistic compromise. It maintains reasonable structural integrity (comparable Ca-RMSD Mode 1) while significantly narrowing the performance gap on rare targets (Lig-RMSD 10.59 v.s 14.50 A). This validates that the co-existence of both sampling modes is decisive factor for generalizable design.
A.17 IMPACT OF DATA IMBALANCE ON MODEL COLLAPSE
Data imbalance critical challenge that impairs model generalization and can lead model col- lapse. This issue manifests on two primary levels within protein-ligand structural datasets. Firstly, while the protein universe encompasses a vast and diverse folding space. its representation in ex- isting data is highly non non-uniform. As illustrated in Figure 6(a). the size distribution of structural clusters exhibits a pronounced long-tail pattern: small number of extensively studied superfami- lies (e.g., kinases, immunoglobulins) account for disproportionately large fraction of the entries. In contrast, thousands other clusters that constitute the bulk structural diversity contain only handful of members. This structural-level skewness causes the model to be excessively trained on the features common folds while severely under-exposed to the vast number of rarer ones. Secondly, similar imbalance exists within the chemical space of ligands and their associated bind- ing patterns. Figure 6(b) demonstrates that the most frequently observed Chemical Component IDs (CCDs) predominantly common metal ions (e.g., MG, ZN), biological cofactors (e.g., HEM,
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ATP), crystallization artifacts (e.g.. GOL). In stark contrast, vast number of ligands with spe- cific biological activities or those representing potential drug candidates are sparsely represented (Figure 9). This dual imbalance- spanning both structural and chemical space-forces the model to learn statistical artifacts from high-frequency samples rather than the generalizable physicochemical principles of interaction. Consequently, when confronted with novel targets or ligands from the long tail of the distribution, the model's 's predictive power degrades sharply, leading to the phenomenon of model collapse.
Imbalance Protein-Ligand Structural
(a) Highly Skewed Distribution 8477 Structural
Frequency of Top 30 Most Common Ligands
bril P3tsc 35342
Total Unique CEDs: 28026
10'
1CE60
Frequcnty Datiset
00
2005
10"
1000
2005
3005
ADDG
5005
50CC
700C
3000
5 g 4602388 23888 88808 Chen ca Component (ec0)
scturral Cluster Parkc (Sottid Sizc3
Figure 8: Imbalance in the Protein-Ligand Structural Dataset This figure illustrates the severe skewness of the data from two perspectives. (a) The size distribution of protein structural clusters. The x-axis represents structural clusters ranked by size descending order, and the y-axis (log scale) shows the number of PDB structures within each cluster. The distribution exhibits characteristic long tail, where few very large clusters dominate the dataset, while the vast majority of clusters contain very few members. (b) The frequency distribution of the top 30 most common Chemical Component IDs (CCDs). This distribution is also highly imbalanced, with common ions, cofactors, and crystallization agents (e.g., NAG, MG, ZN) occurring far more frequently than other functional molecules. This dual imbalance at both the structural and ligand levels is a key factor that can bias model training towards ligh-frequency samples and potentially lead to model collapse.
In our initial experiments, we observed that training the model with protein-ligand combination weight- ing scheme caused it to collapse, resulting in the generation of protein backbones with very low di- versity, as shown in Figure 10. We hypothesize this is because diffusion models have a tendency to learn the mean of the data distribution. Compared to the global architecture of the entire protein, the local ge- ometry of the ligand and the logein- interface represents a much higher-frequent signal, causing the model to overfit this dominant feature.
Figure 10: Model collapse leading low- diversity backbone generation.
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Cluster Size vs. Ligand Diversity
cluster_siae
400
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total ccdd
1000
Number of Unique Ligands (CCDs)
1250
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0
0 250
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Cluster Size (Number of PDBs)
Figure 9: Correlation between Structural Cluster Size and Ligand Diversity This scatter plot in- vestigates the relationship between the representation of protein families and their associated chem- ical space. The x-axis represents the size of each structural cluster (i.e., the number of PDBs), while the y-axis indicates the number of unique ligands (CCDs) found within that cluster. general posi- tive correlation is observed, suggesting that greater number of known ligands. However, the wide dispersion of data points reveals a deeper im- balance in "binding patterns": some large clusters exhibit relatively low ligand diversity (lower-right region), whereas some medium-sizze clusters are hotspots of high chemical diversity. This uneven exploration of chemical space across protein families poses a significant challenge for learning gen- eralizable protein- ligand interaction principles.
extensively studied protein families tend to have
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Reviews:
[{"review_id": "RgM0l3rNmZ", "reviewer": "Reviewer_HKE3", "summary": "The paper presents Pallatom-Ligand, an all-atom diffusion model for generating ligand-binding proteins directly from atomic coordinates. It jointly models all atoms in protein–ligand complexes using a triple-attention transformer and introduces two conditioning mechanisms: global control of protein fold (α/β ratio) and atomic-level control of ligand solvent accessibility. Benchmarked against RFdiffusionAA and RFdiffusion2 on eight ligands, Pallatom-Ligand achieves higher in silico success rates and better control over structure and binding geometry.", "questions": "Can Pallatom-Ligand generalize to unseen ligand chemotypes or novel binding motifs outside the training distribution, and how does its performance degrade in such zero-shot settings?", "limitations": "", "rating": 6, "confidence": 2, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1. Unlike backbone-only or inverse folding models, Pallatom-Ligand directly generates protein–ligand complexes at the atomic level, capturing fine-grained chemical and spatial interactions critical for ligand specificity.\n2. The model introduces multi-level conditioning (fold ratio and solvent accessibility), enabling programmable structural control—something that earlier diffusion-based models lack.", "weaknesses": "1. The training relies on a relatively small number of protein–ligand complexes with strong bias in ligand–fold distribution. Although the authors introduce a dual sampling strategy, scalability to rare or novel ligands may be limited.\n2. All-atom diffusion transformers with block-sparse attention and multi-level conditioning are computationally intensive, potentially restricting usability for large complexes or extensive sampling.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "1Z7ORICmuM", "reviewer": "Reviewer_zHoF", "summary": "This paper proposes a new diffusion model that jointly generates protein-ligand complexes operating at the atomic resolution. The key contributions are a novel diffusion transformer architecture for protein-ligand complexes and a novel conditioning framework that allows conditioning on global protein properties and atomic-level properties. Extensive experiments are presented to showcase the strong performance of this model.", "questions": "1. What is the Transition module used in Equations 2 and 5? I couldn't find any explanations in the paper.\n2. The atom-level pair features $p$ used in Equation 4 should be defined along with the other variables at the start of that section.\n3. In Table 1, I assume Nov. refers to novelty. If so, why is a lower value better? If not, please clarify what it means. Also, Div. is not formally introduced in the paper.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "* The paper is well-written and the key ideas are clearly presented.\n* The idea of modeling the joint distribution of all atoms in a protein-ligand complex to improve generation quality and data efficiency is sound and promising.\n* The presented experiments are quite extensive and showcase the superior performance of the proposed model compared to baselines.\n* The introduced set of component-specific metrics that reflect the multi-faceted nature of the protein design problem is reasonable and should better evaluate the different models.", "weaknesses": "* The reliance on LigandMPNN to redesign the generated sequences hints at some limitations in the proposed model, especially because the model with MPNN outperforms the one without quite significantly (Figure 4). The authors should discuss the problems with the structures directly generated by their model and explore possible solutions to address them.\n* The model achieves a quite poor diversity rate compared to the baselines (0.14-0.17 vs 0.30). In the global control setting, this seems to be improved, but at the cost of lower success rates. The authors should discuss this and some potential fixes.\n* The paper assumes that the reader is familiar with the field of protein design. It would greatly improve the accessibility of the paper if some technical terms were explained, e.g., the metrics discussed in section 3.1: pLDDT, ipAE, etc.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "8n38b2fAne", "reviewer": "Reviewer_HUmp", "summary": "This paper presents Pallatom-Ligand, an end-to-end all-atom diffusion model for generating ligand-binding proteins. The key idea is to unify the representation of small molecules and protein residues at the atomic level and to jointly model their spatial and chemical interactions. The model introduces a ligand-aware all-atom diffusion transformer that integrates atom- and token-level representations to capture both global and local dependencies within protein-ligand complexes. Furthermore, a conditioning framework is proposed to control global protein fold diversity (through α/β ratio conditioning) and atomic-level ligand solvent accessibility (SA). Evaluation on eight chemically diverse small molecules using AlphaFold3-based component-specific metrics demonstrates improved in-silico structural plausibility over RFdiffusion2 and RFdiffusionAA.", "questions": "See Weaknesses", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "1. The idea of a unified atomic representation for both proteins and small molecules is conceptually elegant and well-motivated. Modeling all atoms within one generative framework could in principle enhance data efficiency and the precision of atomic interactions.\n2. The paper proposes a multi-level conditioning framework that allows explicit control over protein fold topology (via α/β ratio) and ligand solvent accessibility. This design makes the generation process more interpretable and programmable.\n3. The authors conduct a broader benchmark than prior work, testing on eight ligands with diverse physicochemical properties, and systematically report AlphaFold3-based component-specific metrics. This gives a relatively thorough in-silico comparison.", "weaknesses": "1. The architecture introduces a “triple-attention” transformer combining token-, atom-, and pair-level streams, but the paper does not include any ablation study to clarify what each component contributes. Without that, it is unclear which part is essential for the observed performance gain.\n2. The paper describes two sampling modes for dual-objective training, but there is no experimental analysis of how the chosen ratio between them influences training dynamics or the final results, as no sensitivity or ablation study is provided.\n3. The evaluation framework relies entirely on AlphaFold3 predictions as pseudo–ground truth. This is acceptable for initial screening, but the paper could be clearer that these metrics measure structural consistency rather than true binding affinity or physical validity.\n4. The presentation is difficult to follow for readers outside the protein-design community. Several evaluation metrics (e.g., pLDDT, ipAE) are introduced as part of the “AlphaFold3-based component-specific metrics,” but the paper provides little intuition about their meaning or justification. While these values come from AlphaFold3 predictions, the manuscript does not clarify how they reflect the underlying biophysical objectives or why they are appropriate for assessing generative quality. This lack of context makes it difficult for readers from a machine-learning background to interpret the reported results.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "X6DSW9G81Y", "reviewer": "Reviewer_4PX2", "summary": "This paper proposes a method for generating protein–ligand complexes based on small-molecule ligands. The approach outperforms existing baseline methods on in silico evaluation metrics and, to some extent, addresses the challenge of generating protein–ligand complexes with non-helical protein structures.", "questions": "Why was the SA experiment conducted on only three ligands?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 4, "presentation": 3, "contribution": 3, "strengths": "1. The proposed method outperforms the current state-of-the-art on most evaluation metrics and, to some extent, increases the proportion of designable non-helical structures in the generated results.\n2. The paper introduces a well-designed benchmarking strategy for this problem, which enriches the evaluation process.\n3. The writing is clear and well organized.", "weaknesses": "1. In terms of novelty, the model architecture is almost identical to those of AF3 and P(allatom), with the only additions being the alpha ratio and solvent accessibility (SA) as inputs. There are no significant differences in the generation or training methods, indicating a lack of machine learning novelty.\n2. Although the paper claims to generate more non-helical structures, this aspect is not compared against other models; the evaluation is only performed through self-comparison.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)