PoseX: AI Defeats Physics-based Methods on Protein Ligand Cross-Docking
Authors: Yize Jiang
Xinze Li
Yuanyuan Zhang
Jin Han
Youjun Xu
Ayush Pandit
ZAIXI ZHANG
Mengdi Wang
Mengyang Wang
Chong Liu
Guang Yang
Yejin Choi
Yingzhou Lu
Wu-Jun Li
Tianfan Fu
Fang Wu
Junhong Liu
Venue: ICLR 2026 Poster (2026)
Keywords: AI docking
AI co-folding
protein-ligand interaction
cross docking
Primary area: datasets and benchmarks
TL;DR: a comprehensive benchmarking of various docking methods on a new curated dataset for self-docking and cross-docking

Abstract: Recently, significant progress has been made in protein-ligand docking, especially in deep learning methods, and some benchmarks were proposed, such as PoseBench and PLINDER. However, these studies typically focus on the self-docking scenario, which is less practical in real-world applications. Moreover, some studies employ complex frameworks that require extensive training, posing challenges for convenient and efficient assessment of docking methods. To address these gaps, we introduce PoseX, an open-source benchmark for evaluating both self-docking and cross-docking, enabling a practical and comprehensive assessment of algorithmic advances. Specifically, we curated a novel dataset comprising 718 entries for self-docking and 1,312 entries for cross-docking; secondly, we incorporated 23 docking methods in three methodological categories, including physics-based methods (e.g., Schrödinger Glide), AI docking methods (e.g., DiffDock) and AI co-folding methods (e.g., AlphaFold3); thirdly, we developed a relaxation method for post-processing to minimize conformational energy and refine binding poses; fourthly, we established a public leaderboard to rank submitted models in real-time. We derived some key insights and conclusions through extensive experiments: (1) AI-based approaches consistently outperform physics-based methods in overall docking success rate. (2) Most intra- and intermolecular clashes of AI-based approaches can be greatly alleviated with relaxation, which means combining AI modeling with physics-based post-processing could achieve excellent performance. (3) AI co-folding methods exhibit ligand chirality issues, except for Boltz-1x, which introduced physics-inspired potentials to fix hallucinations, suggesting that stereochemical modeling greatly improves the structural plausibility of the predicted protein-ligand complexes. (4) Specifying binding pockets significantly promotes docking performance, indicating that pocket information can be leveraged adequately, particularly for AI co-folding methods, in future modeling efforts.

Published as a conference paper at ICLR 2026
POSEX: AI DEFEATS PHYSICS-BASED METHODS ON PROTEIN LIGAND CROSS-DOCKING
Yize Jiang'." Xinze Li Yuanyuan Zhang",, Jin Han Youjun Xu*** Ayush Pandit Zaixi Zhang". .Mnngdi Wang", Menggyng Wang', Chong Liu", Guang Yang", Yejin Choi'. Yingzhou Lu Wu-Jun Li Tianfan Fu Fang Wu Junhong Liu Microocyt,, "Purdue University, Sttate Key Laboratory for Novel Software Technology at Nanjing University, AAnew Therapeutics, Sttanford University, "rincetonn University, "Peking University, *Central South University, 'Imperial College London "Equal Contributions, 'Correspondence
ABSTRACT
Recently, significant progress has been made in protein- ligand docking, especially in deep learning methods, and some benchmarks were proposed, such as PoseBench and PLINDER. However, these studies typically focus on the self-docking scenario, which is less practical in real-world applications. Moreover, some studies employ complex frameworks that require extensive training, posing challenges for con- venient and efficient assessment of docking methods. To address these gaps, we introduce PoseX, an open-source benchmark for evaluating both self-docking and cross-docking. enabling practical and comprehensive assessment of algorithmic advances. Specifically, we curated a novel dataset comprising 718 entries for self- docking and 1,312 entries for cross dooss-docking; secondly, incorporated 23 docking methods in three methodological categories, including thysics-based methods (c.g.. Schrödinger Glide), Al docking methods (e.g.. DiffIDock) and Al co-folding methods (e.g., AlphaFold3); thirdly, we developed a relaxation method for post-processing to minimize conformational energy and refine binding poses; fourthly, we established public leaderboard to rank submitted models in real-time. We derived some key insights and conclusions through extensive experiments: (1) Al-based approaches consistently outperform physics-bused methods in overall docking success rate. (2) Most intra- and intermolecular clashes of Al-based approaches can be greatly alleviated with relaxation, which means combining AI modeling with physics based post-pracessing could achieve excellent performance. (3) Al co-folding methods ex- hibit ligand chirality issues, except for Boltz-1x, which introduced physics-inspired potentials to fix hallucinations, suggesting that stereochemical modeling greatly improves the structural plausibility of the predicted protein- complexes. (4) Specifying binding pockets significantly promotes docking performance, indicating that pocket information can be leveraged adequately, particularly for Al co-folding methods, in future modeling efforts.
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INTRODUCTION
Protein-ligand docking crucial to drug discovery as it predicts how ligand interacts with protein, helping to identify potential drug candidates and accelerate the development of new therapeutics |Hann et al. (2022); ; Du et al. (2022); Fu et al.] (2022); Wu et al. (2023 2024). By understanding these interactions, researchers can optimize ligands for better binding affinity, specificity. and efficacy, ultimately accelerating the development of new therapeutics. Learning from known crystal protein-ligand complexes through machine learning, especially deep learning (DL) techniques, Al-based approaches have revolutionized protein protein-ligand docking and substantial progress has been made recently (Pei et al. 2023
Lu al. 2024
Lai et al. 2024 Cao al. 2024). In response

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Table Comparison of existing docking benchmark studies.
Benchmarks
PoseBuster
PoseBench
PLINDER
PoseX (Ours)
Code of dataset pipeline
X
Relaxation
X
coarse
x
well-designed
Self-docking evaluation
V
/
Cross-docking evaluation
X
X
# Open-source docking software
2
1
0
2
# Commercial docking software
0
O
# Physics- based methods
2
O
# Al docking methods
5
2
1
11
# Al co-folding methods
0
# Total methods
7
7
I
23
Real-time leaderboard
X
X
to the proliferation of new approaches, recent work has introduced several benchmarks, such as PoseBench (Morehead al. (025) and PLINDER (Durairaj et al.) (024), along with corresponding datasets and metrics for evaluating protein-ligand interactions. Despite the rapid progress, existing studies still encounter several challenges, summarized as follows.
1. Self-docking is an impractical setup. Most existing benchmarks, such as PoseBuster (Butten schoen al.) 2024) and PoseBench (Morehead al. 2025), focus the self-docking scenario, which is less practical in real-world applications. For instance, pharmaceutical chemists typically design new drug molecules and dock them into targets, whose conformations are extracted from existing crystal structures co-crystalizeed with other published compounds.
Heavy framework and low accessibility. Some benchmarks (e.g., PLINDER (Durairaj et al. 2024)) suffer from heavy evaluation frameworks that involve data splitting and training, which are hard to use. While studies such as PoseBuster that only concentrate on evaluation rather than training worthy of reference, they are lightweight and user-friendly.
3. Limited model selection for benchmarking. Existing studies often restrict their comparative scope to narrow set of models. For instance, PoseBuster evaluated only Al-based approaches and 2 physics-based methods, while PLINDER exclusively benchmarked against DiffDock (Corso) ct al. 2022), neglecting other notable algorithms.
Therefore, we propose several solutions to address these issues:
1. Cross-docking is more realistic. To better evaluate the capacity of various docking methods in more practical scenario, incorporate cross-docking, which involves docking various small molecules extracted from distinct complexes of the same protein with all the conformations except the native co-crystalized one.
Construction of new dataset. We curated new dataset named PoseX that collects newly found crystal structures of protein-ligand complexes in RCSB PDB, which contains 718 entries for self-docking and 1,312 entries for cross-docking.
3. Involving 20+ models. We evaluated 23 docking methods encompassing nearly all relevant models published in peer-reviewed journals and conferences alongside established commercial docking software across three different categories, including physics-bused methods such as Schrödinger Glide (Friesner et al. 2004), 11 A/ docking methods such as DifflDock, and 7 Al co-folding methods such AlphaFold3 (Abramson et al. 2024).
addition, we developed novel relaxation module (also known as energy minimization) that serves as post-processing step to refine Al-generated binding poses and improve structural plausibility, We also established a public online leaderboard that enables researchers to benchmark their models against standardized dataset, fostering transparency and facilitating straightforward, fair comparisons for the broader community. The key differences between the existing docking benchmarks and ours summarized in Table
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Table 2: Comparison of various docking methods.
Pocket Required
Pocket Changed
Avg. Runtime Sample
Method
Pub. Year
License
Physies-based methods
Discovery Studio
late 1990s
Commercial
14.4 min
Schrödinger Glide
2004
Commercial
7.2 min
MOE
2008
Commercial
X
50 scc
AutoDock Vina
2010, 2021
Apache-2.0
18
GNINA
2021
Apache-2.0
sec
AI docking methods
DeepDock
2021
MIT
X
2.7 min
EquiBind
2022
MIT
1.4
TankBind
2022
MIT
7.8 sec
DiftDock
2022
MIT
1.2 min
UMD V2
2024
MIT
24 sec
FABind
2023
MIT
X
8.8
DiffDock-L
2024
MIT
1.5 min 1.7min
DiffDock- Pocket
2024
MIT
DynamicBind
2024
MIT
2.4 min
Interformer
2024
Apache-2.0
0.6 min
SurfDock
2024
MIT
10.8
AI co-folding methods
NeuralPLexer
2024
BSD
1.5 min
RFAA
2023
BSD
9 min
AlphaFold3
2024
CC-BY-NC-SA 4.0
16.5 min min
Chai-1
2024
Apache-2.0
Boltz-1
2024
MIT
3 min
Boltz- 1x
2025
MIT
3 min
Protenix
2025
Apache-2.0
3.6 min
The running environment and parameters of each method shown in Appendix B
2 METHODS
We categorize all the docking approaches into three distinct categories: (1) physics-basee methods utilize physics- scoring functions and sampling algorithms to estimate pootein-ligante interactions, including Discovery Studio (Pawar & Rohane (2021), Schrödinger Glide (Friesner et al.] (0044). MOE (Vilar et al. (2008) AutoDock Vina (Trott & Olson 2010: Eberhardt et al. 2021). 1). and
GNINA (McNutt al. 202T); (2) Al docking methods produce ligand binding poses based the three-dimensional structure of proteins, including DeepDock (Méndez-l -ucio et al., (021))
EquiBind (Stärk et al.) 2022), TankBind (Lu al. (022). DifiDock (Corso et al. 2022). Uni-Mol
Docking V2 (UMD V2) (Alcaide et al. 2024), FABind (Pei et al. (023). DillDock-L. (Corso et al. 2024). DiffDock- Pockett (Plainer et 2023). DynamicBind (Lu al. 2024), Interformer LLai
et al. 2024), SurfDock (Cao et al. 2024); (3) Al co-folding methods predict both the ligand's d's
binding conformation and the protein's s conformational changes induced by ligand binding, which account for simultaneous structural adaptations of the protein and ligand, enabling more accurate modeling of their interactions; we involve Al co-folding methods, including NeuralPLexe (Qiao
ct a..) Roseoldoold All-Atom (RFAA) (Krishna et al.) (0024). AlphaFold3 (Abramson et al. 2024
Chai-1 (Discovery et al.) 2024). Boltz-1 (Wohlwend et al.] 2024), Boltz- 1x (Wohlwend et al., 2024
Protenix (Team et al. (025). For comparative analysis, summarize the methods compared in
T and the detailed settings of these methods are provided in Appendix
Relaxation as Post-processing Relaxation in molecular docking, also known energy minimization. a prot-processiig method used to refine and optimize docked protein-ligand complexes (Guedes) et al. 2014; Amaro al. 2008). It involves energy minimization and, when necessary, short molecular dynamics simulam lations to resolve steric clashes, refine interatomic interactions, and ensure that the system reaches stable, low-energy conformation. This step enhances the physical realism and accuracy of the docking results, thereby making the predicted binding poses more reliable for subsequent analysis experimental validation. In this paper, we introduce well-designed relaxation
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protein X
complex X-A: protein X bind with ligand
self-docking
cross dross-docking
complex X-8: protein X bind ligand
Figure 1: Illustration of two docking setups: (1) self-docking, vs. (2) cross-docking
module, the feature of which is summarized as: (1) Implemented an automated relaxation process for complexes based on OpenMM (Eastman et al. 2017). (2) Established a comprehensive automatic data processing pipeline for proteins and small molecules, including fixing missing chains, capping the N- and C-termini, adding formal charges to proteins and small molecules, and applying restraints to backbone atoms (CA, C, N, O). (3) Supports small molecule force field parameters from GAFF and OpenFF (Consortium) 2024). (4) Supports partial charge calculation methods for small molecules, including Gasteiger and MMFF94, (5) Effectively alleviates unreasonable predicted conformations, improving the pass rate of PB-Valid. The technical details of the relaxation process are provided in
Appendix C
3 DATASET
3.1 SELF-DOCKING VERSUS CROSS-DOCKING
Self-docking. Self-docking involves docking a ligand back into its native co-crystallized conforma- tion (Kawatkar al. 2009). This is typically used assess whether the docking software accurately reproduces the known binding pose, thereby validating the method. Most existing benchmarks only consider the self-docking setup.
Cross-docking. Cross-docking refers to dock molecules extracted from distinct complexes of the same protein with all conformations except the native co-crystalized one. This approach is considered more versatile, as it accounts for the possibility that the receptor protein may undergo conformational changes and may not be fully optimized for ligand docking. The difference between self-docking and cross-docking is illustrated in Figure])
3.2
ASTEX
The Astex Diverse set (Hartshorn et al. 2007), ) published in 2007, is a set of hand hand-picked, relevant, diverse, and high-quality logein- complexes from the RCSB PDB. It comprises 85 unique and significant protein-ligand complexes. These complexes have been appropriately formatted for docking purposes and will be made freely accessible the entire research community via the website (http: www.:wwwww..ooo...o.ooo ccdc. cam. ac uk). The Astex Diverse set only supports self-docking evaluation.
3.3 PoseX: OUR CURATED DATASET
In this paper, we curated a high-quality protein-ligand ligand complex structure dataset, PoseX, designed to evaluate molecular docking methods. It comprises carefully selected crystal structures from the RCSB Protein Data Bank (RCSB PDB) (Rose al. (016)), with two subsets for evaluating self-docking and cross-docking tasks. The dataset includes only complex structures published from 2022 to January 1st, 2025, ensuring no overlap with the training data all Al-based approaches being evaluated (as shown in Table S3). The construction steps of the two subsets PoseX Self-Docking (PoseX-SD) and PoseX Cross- Dooss-D (PoseX-Cui --D) are shown in Table ST and Table S2 Ultimately, there are 718 entries for PoseX-SD and 1,312 entries for PoseX-CD, comprising 109 protein targets (a total of 371 structures) and 362 small molecules. The distribution of the number of conformation structures per target is shown in Figure SSa and the distribution of pocket similarity is shown in Figure STb
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4 EXPERIMENTS
4.1 EVALUATION METRICS
Performance evaluation of protein-ligand docking involves metries that assess both the quality of the predicted binding pose and the chemical validity as well as the structural plausibility, which are described in detail as follows.
RMSD. In accordance with most benchmarking studies, we evaluate the quality of binding poses using the Root Mean Square Deviation (RMSD), which measures the distance between the predicted and ground-truth complex structures. Lower RMSD scores indicate better binding poses.
PB-Valid. The physicochemical validity and structural plausibility of the generated binding poses are measured with the PoseBuster test suite (i.e., PB-Valid, This suite evaluates whether predicted ligand poses are consistent with known chemical and structural constraints. See Appendix foor more details.
Success rate. The docking success rate defined as the percentage of the top-1 ranked predictions satisfying either of the following criteria: (1) RMSD < 2A, or (2) RMSD 2 & PB-Valid. For PoseX-CD, we report the averaged success rate at the target level in view of the uneven distribution of docking sizes per target (as shown Figure Sla). Higher success rates indicate better performance.
4.2 OVERALL PERFORMANCE ANALYSIS
Figure 2 Figure 3 and Table S4 present comprehensive evaluation of various docking approaches on three benchmarks PoseX-Si SD, PoseX-CD, and Astex under RMSD < 2Á and PB-Valid criteria. From these results, we highlight several key observations and provide more detailed analysis.
1. Al-based approaches lead in success rate. The latest Al-based approaches, both Al docking meth- ods (e.g.. SurfDock) and Al co-folding methods (e.g., Alphafold3) have consistently outperformed physics- methods in overall docking pose and validity.
2. Relaxation significantly mitigates clashing. The intra- and intermolecular clashes of Al-based approaches can be greatly alleviated with relaxation, which means that the force field-based energy minimization step is very crucial to achieve excellent performance in real-world applications, particularly for Al modeling.
3. Chirality warrants further improvement. Most of the Al co-folding methods exhibit ligand chirality issues, such as AlphaFold3 and Chai-1, except for Boltz-1x, which introduces an inference time steering technique employing physics-inspired potential fix hallucinations and enhance structural plausibility.
Pocket information is crucial to docking. Explicit modeling of the binding pocket substantially improves docking performance, seen by the consistent performance gains of SaffDock-Pockee over its counterpart DiffDock across both self-docking and cross cross-docking indicating that pocket information be leveraged adequately, especially for Al co-folding methods, in future modeling efforts.
Astex Benchmark. The Astex benchmark represents an idealized docking scenario with high-quality co-crystal structures. Because most Al-based approaches use the PDBBind v2020 training set, which includes 16,379 protein-ligand complexes, we analyzed and found that 43 of the 85 complexes in the Astex Diverse Set are included in this set. In this setting, Al docking methods outperform all other categories overall. UMD V2 and SurfDock achieve the highest docking success rates (94.1%) when integrated with our structural relaxation protocol, surpassing physics-busel methods, such as Glide and Discovery Studio, by over 25%. HrtDock-Pocker Interformer, and DiffDook-LL also perform strongly, achieving success rates above 83.8%. While Al co-folding methods such as AlphaFold3, Protenix, and Chai-l deliver competitive results (over 80% success), they are marginally outperformed by beokinn-speeializeede architectures. Physics-based methods like AutoDock Vina and MOE plateau around 67.17.1.11 ,, even with induced-fit docking (e.g.. Glide IFD). These results illustrate substantial performance gains from AI models tailored to pose prediction.
PoseX-SD Benchmark. For PoseX-SD evaluation, SurfDock (78.0%) achieves the overall state-of- the-art performance, and UMD V2 takes the second place. DiffDock-Poode shows clear advantages
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over its pocket-agnostic counterpart, with success rate of 52.6%. Among Al co-folding methods, AlphaFold3 and Protenix perform well (60.5% and 56.3%, respectively), demonstrating their capacity to model close-range binding interactions. In contrast, earlier Al docking methods such as EquiBind and TankBind perform poorly (below 20%) and exhibit significant issues with structural plausibility. Physics- -hased methods such Glide and Discovery Studio remain clustered in the 40-65% 65% range. Most Al-based approaches benefit from the relaxation method we developed, and their intra- and intermolecular validity is significantly improved.
Taking into account that pocket information plays a key role in predicting binding structures, we further separate the methods into Pocker- Given (docking with specified pocket) and Blind-Docking Docking (docking without specified pocket) tracks and analyze, respectively, as shown in Figure 3 In the Pocket- Given track, SurfDock leads with 78.0% success rate, outperforming UMD V2 (72.4%). Interformer (66.6%)) and DiffDock-Pocket (52.6%). These methods leverage explicit pocket information to refine pose predictions, demonstrating superior accuracy in scenarios where binding sites are known a priori. Physics-based methods such as Glide (47.9%) and Discovery Studio (54.9%) ) are classical computational chemistry docking tools which inherently require pocket specification, while they were all surpassed by the Al docking methods. In the Blind-Dockin track, AlphaFold3 achieves the best success rate of 60.3%, followed by Protenix (56.3%) and Chai-1 156. These Al co-folding methods excel in modeling induced-fit adaptations without specification of pockets, solving more challenging problem than Pocket-Giver counterparts. Blind Al docking methods like Doff'Dock-L. (47.1%) and DynamicBind (27.0% perform well while still defeated by Al co-folding methods, which underscores the value of simultancous pooeiinllgande interaction modeling in Blind-Docking scenarios.
PoseX-CD Benchmark, For PoseX-CD evaluation, SurfDock (77.0%) and UMD V2 (69.2%) are still the top performers in all three categories of docking methods, as well as AlphaFold3, which achieves competitive performance (68.6%) against UMD V2. We observed that Al docking methods have developed rapidly in recent years, of which the latest models (such as SurfDock, UMD V2, Interformer, and DiffDock-Pocket) demonstrably surpass the earlier models (such as EquiBind, TankBind, and DeepDock). For Al co-folding methods, AlphaFold3 defeats other models (such as Chai-1, Boltz-1, Boltz- 1x and Proteinx) by narrow margin (1.6%- 7.5%).. Notably, physics-based methods struggle significantly in this scenario. For example, in the PoseX-SD task, only 3 A/ docking methods outperform the leading physics-based method, GNINA, in terms of the percentage of RMSD < 2A with relaxation. However, in the PoseX-CD task, 9 Al-based approaches (including 4 Al docking methods and 5 Al co-folding methods) surpass GNINA (54.1%). This underscores significant advantage of Al-based approaches over physics-basse based methods in the cross-docking scenario. Figure S12 and Figure S13 depict an illustrative example of the superior performance of Al-based methods. Relaxation yields consistent improvements across most approaches, emphasizing its role in resolving steric or geometric inconsistencies.
Furthermore, also analyzed the performance according to whether the pocket information is specified, as shown in 3 In the Pocket-Given track, SurfDock is also the winner with the highest success rate of 77.0%, followed by UMD V2 (69.2%.) Interformer (60.2%). and DifffDoc- (58.5%). These methods benefit from pocket information to handle cross- conformttoaa variability. outperforming physics- based methods like Discovery Studio (43.7%), Glide (38.4%), and MOE (33.3%). This track highlights the advantages of pocket-guided Al docking methods in practical drug design scenarios involving non-native protein conformations. In the Blind- Blind-Docking track, AlphaFold3 leads at 68.8%, with Chai-1 (67.0%) and Boltz-Ix (64.4%) close behind. Al co-folding methods dominate here, as they inherently model protein flexibility without pocket information, addressing the harder blind-docking challenge. Blind Al docking methods such as DiffDock-LL (54.7%) and DynamicBind (32.8%) showed excellent performance but were generally outpaced, emphasizing that it is necessary to incorporate co-folding-lite like adaptability for Blind- Hhnd--Doking methods in future.
4.3 POCKET SIMILARITY BASED GENERALIZABILITY ANALYSIS
To further understand the generalization capacity of various docking approaches, we analyze the relationship between pocket similarity and ligand RMSD across different scenarios. In view of the cut-off time of the training data for each method (as shown in Table (3), the pocket similarity is calculated as the maximum TM-score compared to pockets extracted from crystal structures released before 2022 RCSB PDB, where the pocket is defined the residues within 10.0A of the ligand.

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77.1%
SurtDcc
77.01
UMD V2
Interforee
Inter form
58.5%
Docking
DifiDock4
147.31
Docking
Dif.Dcke
34.7%
billbock
Diflbock
Dynamictind
Dynemiclind
116.00%
Tenk-iid
DeepDock
16.490
DeepBock
NN317.1 117.18
FAdlnd
15.3%
Equitind
13.5%
Equitind
Appofrid3
600 6%6
Alphahiid
Protenk
79
Proten
61.0%%
Co-folding
Chei-]
Ca-felding
Chei-3
67.100
Belte1
296
Boltr-
15.35
Boltz 1x
Bottz 1x
8FAA
N.70-20
RAA4
131.6 9%.0%%
NeuraPLoxer
14435%
NeuraiPLoxer
12
GNINA
L164
GNINA
NS3 133-1%
Discovery Studio
736
Discovery Studio
14.19
Physica-base based
Glide
48 as6
Glide
22 50.5%
Glide (IFD)
45.2%
Glide (FF)
65500%
MOE
40.2%
MOE
35.35%
Mnn 28
Anno
140.00
With receation
AutDoccc Vino
26.1%
With relenaton
RM5D 2866 rewile
FA5D 28.6 re-wir
0%
20%
40%
80%
80%
100%
0%
20%
40%
60%
80%
100%
Docking
Docking
(a) PoscX-SD
(b) PoseX-CD
Figure 2: Performance PoseX-SD and PoseX-CD. Mean values of three independent reported here for each method, and detailed results with standard deviation are reported in Table SS4] Striped bars represent the proportion of predictions with RMSD < 2Å, with numerical values indicated using bar labels. Solid bars indicate predictions that additionally satisfy PoseBuster validation criteria (PB-valid). Results with and without relaxation are distinguished by different colors.
Figure S2 and Figure S3 present per-sample scatter plots of pocket similarity versus docking RMSD for self-docking and cross-docking, respectively. Each plot reports Pearson's correlation coefficient to quantify the strength and direction of the relationship. Figure complements these results by summarizing the average ligand RMSD separately for test cases with similar and dissimilar pockets. Figure (54 and Figure $5 illustrate the relationship between the ligand RMSD and the decreasing binding pocket similarity of Al-based approaches.
Self-Docking Observations. In the self- docking scenario, most Al-based approaches exhibit a moderate negative correlation between pocket similarity and ligand RMSD, indicating that the leakage of pocket information is associated with improved ligand pose accuracy. For example, Protenix and Chai-1 show stronger correlations -0.390 and -0.389, respectively). while other models
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MA
MOWN AOMA
Proriul
(a) PoseX-SD
Bline-Due Dackig
Pecket-Gives
(b) PoseX-CD
Figure 3: Performance of each model on the PoseX-SD and PoseX-CD datasets. Results are split according to whether the method requires pocket (Pocket-Given vs. Blind- Dooking)..
such as AlphaFold3 (r = -0.313) and Boltz-1 (r -.2766) exhibit similar trends. DiffDock and DiffDock-L display similar correlations (r = -0.283 and r -02278, respectively), suggesting that docking-speciffe models also benefit from the pocket leakage.
In contrast, physics-based methods show weaker near-zero correlations. Glide (r 0.010), AutoDock Vina (r -0.009), and Discovery Studio (r = -0.001) exhibit negligible correlations, suggesting consistent docking performance across varying pocket similarities.
Notably, SurfDock (r = -0.091) and UMD V2 (r = -0.134), which achieve top overall performance, show only weak correlation between pocket similarity and ligand RMSD. These findings suggest that their success likely stems from robust pose-prediction mechanisms that are less sensitive to pocket information leakage. These results highlight the importance of robust pose prediction in achieving high docking performance, even when pocket similarity is limited, in the self-docking scenario.
Cross-Docking Observations. The cross-docking setting reveals an overall stronger correlation between pocket similarity and ligand RMSD, particularly for A/ co-folding methods and Al docking methods. Chai-1 (r -0.526), Boltz-1 (r -0.521), and Protenix (r -0.553) exhibit strong negative correlations, suggesting that successful docking in cross- dooss-d is highly contingent upon correctly modeling the target pocket's conformation. DiffDook and its variants continue to reflect this trend (e.g., Diff r = - -0.505; DiffDock-L. -0.498), further confirming the influence of pocket leakage under receptor shift scenarios.
Models such as DynamicBind (r -0.576) and correlation between pocket similarity and ligand RMSD, reinforcing that flexible or dynamic Al docking methods also have constrained generalization. In contrast, physics-bused methods such as Glide (r = 0.015) and Discovery Studio (r = 0.053) again exhibit negligible correlation.
(r = -0.425) also show a strong
Even gegh-performing models like SurfDock (r -0.376) and UMD V2 (r -0.280) show stronger correlations in this setting than self-docking, indicating that pocket modeling becomes more critical in the presence of conformational variance. This further highlights the need for improved pocket-conditioned pose generation in cross-docking scenarios.
Performance Stratified by Pocket Similarity. Figure further stratifies the average ligand RMSD for each method, where the evaluation set is split into two groups, similar group (96 protein targets, Pocket Similarity 0.70) and dissimilar group (13 protein targets, Pocket Similarity
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Sinl
Bist Feckets
Anddtd
Ireu
Rpeedding
Ma
Adtedd
Docking
Co-todiigg
Physic bused
Figure 4: Cross-docking performance difference on "similar" and "dissimilar" binding pockets.
< 0.70). Across all the Al-based approaches, both Al docking methods and Al co-folding methods, docking into similar pockets consistently achieve lower RMSD. However, the degradation of different models in dissimilar pockets evaluation varies significantly. Physics-based methods such Glide, MOE, and Discovery Studio consistently show a very small gap between similar and dissimilar evaluations, indicating excellent generalizability and outperforming most Al-based approaches in the dissimilar pocket scenario. Earlier Al docking methods (e.g., TankBind) and most A/ co-folding methods (e.g., Chai-1, Protenix, AlphaFold3) suffer steep performance drops--TankBind degrades from 4.79A to 7. Chai-l degrades from 2.36Å 5.72A, and Protenix degrades from 2.53 to 6.30A highlighting their overreliance on pocket leakage and lack of adaptability. The latest A/ docking methods, particularly SuriDock (1.56A to A. and UMD V2 (2.08Á to 3.04A), demonstrate much smaller gaps and showcase robust generalization.
Overall Implications. These analyses collectively suggest that pocket similarity is key determinant of successful docking, particularly for the cross-docking scenario. Al co-folding method and Al docking methods reveal stronger dependence show little sensitivity. Notably, even the state-of-the-arr models such as SurfDock and UMD V2 exhibit varying levels of dependence on pocket fidelity, indicating that future improvements in docking may arise from synergistically enhancing both pocket modeling and pose prediction.
pocket information, while physics-based methods
4.4 IMPACT OF RELAXATION FROM A PHYSICALLY-BASEI VALIDATION PERSPECTIVE
We systematically evaluated the docking performance of various methods using the PoseBuster test suite, which comprises 20 physicochemical validation metrics assessing stereochemistry and intra- and intermolecular validity. Figures [S8 and S9 illustrate the failure rates of the PB-Valid metric before and after relaxation in self-docking and cross-docking settings, respectively.
Without Relaxation. In the absence of relaxation, most docking methods generate ligand poses that violate physicochemical constraints. Notably, models such as EquiBind, FABind, and DeepDock exhibit a high failure rate in intermolecular validity, especially the minimum distance-to-protein metric, with only approximately 10% of the predictions passing the test. Even SuriDock, which achieves the lowest RMSD, fails in nearly half of its predictions for this metric. Among the Al docking methods, UMD V2 demonstrates the best performance PB-Valid, but still exhibits chirality prediction errors. Among Al co-folding methods, NeuralPI.exe and RFAA perform poorly with respect intermolecular validity. AlphaFold3 and similar models show relatively stable performance, but are not immune to chirality errors. In comparison, the recently introduced Boltz-1x Ix model effectively addresses these issues, achieving the highest PB-Valid pass rate among all AI methods. Physics-base based methods consistently perform well in structural plausibility, achieving high pass rates.
With Relaxation. Most Al-based approaches benefit significantly from our relaxation protocol, which effectively mitigates intra- and intermolecular clashes. SuriDock emerges as the top-performing method on the benchmark with post-relaxation. However, relaxation does not resolve chirality errors and UMD V2 shows no performance improvement in this process. Our relaxation module (Appendix C refines atomic positions and resolves steric clashes (e.g., Figure S13), but it does not correct chirality errors, which involve incorrect stereochemical configurations at tetrahedral centers, requiring specific bond reconfigurations beyond energy minimization. For UMD V2, Figures S8 and S9 show that a significant portion of PB-Valid failures stem from tetrahedral chirality Because relaxation cannot address these stereochemical issues, the PB-Valid scores do not improve. Similarly, Al co-folding methods, including AlphaFold3, Chai-1, Boltz-1, and Protenix, exhibit limited

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improvement due to persistent chirality errors. For Al co-folding methods, tetrahedral chirality failures arise because these models rely on learned patterns from training data, which may not fully capture the precise stereochemical constraints required for correct chiral center configurations. For instance, (Childs et al. 2025) highlights that AlphaFold3 struggles with stereochemical accuracy for D-peptides due its training data bias toward L-amino acid structures. This limitation extends to other A/ co-folding methods in our study, except for Boltz-lx, which ensures stereochemical correctness by introducing physics-inspired potentials fix hallucinations. Figure SIT illustrates two representative cases of chirality errors in docking predictions.
Summary. Integrating relaxation with Al docking methods yields the state-of-the-ue performance. Concurrently, advancements in AI for biology are driving progress in docking methodologies. Boltz-Ix incorporates physical mechanisms to produce docking results that satisfy physical constraints without relying on relaxation. These findings highlight the critical role of combining physically informed generation with refinement procedures in docking pipelines, particularly in drug design scenarios that require atomic-level accuracy.
5 CONCLUSION
This paper proposed PoseX. a comprehensive benchmark for protein-ligand docking, Specifically, we curated new dataset with newly released protein-ligand complex crystal structures focusing on both self-docking and cross-docking, and incorporated 23 docking methods across three main research lines (physics-based methods, Al docking methods, and Al co-folding methods) to make an exhaustive comparison. We also designed a novel relaxation module to refine the Al-generated binding pose through energy minimization. Furthermore, we developed an online leaderboard that fosters transparency and facilitates straightforward, fair comparisons in mootein-ligand docking. By conducting thorough empirical studies, we drew several key conclusions: (1) Both A/ docking methods and Al co-folding methods have outperformed physicx-based methods in overall docking success rate. (2) Most structural plausibility (except chirality) of Al-based approaches can be enhanced with relaxation, which means combining Al modeling with physics-base post-processe processing may achieve excellent performance. (3) Almost all the Al co-folding methods are plagued by ligand chirality, except for Boltz-1x, which introduced a new inference time steering technique to fix ballucinations, pointing out the direction of incorporation of advantages of Al and physics. (4) Pocket information can be leveraged adequately, especially for Al co-folding methods, to further promote the performance in real-world applications.
LIMITATION AND FUTURE WORK
Here, we summarize the limitations of this work and present some directions for future research.
1. Evaluation of binding affinities on downstream tasks. While we focus on pose prediction and structural plausibility, binding affinity prediction remains an underexplored but complementary objective. Joint evaluation of structure and affinity on downstream tasks, such drug-target interaction and enzyme-substrate interaction, would enable a more holistic assessment of docking algorithms and also remain an exciting direction for future research.
2. Benchmarking on multi-ligand systems. So far, most existing benchmarks focus the evaluation of single-ligand docking, while multi-ligand docking is also practical in real-world applications such as enzyme engineering, where enzymes usually catalyze substrates together with co-factors. Thus, it is worth being assessed exhaustively in the future.
3. Evaluating DynamicBind with apo structures. There is an approximation for DynamicBind evaluation where holo structures were employed as input to maintain a uniform input format across the benchmark rather than the apo structures which were supposed to be. However, DynamicBind is explicitly designed to handle apo structures and such approximation may constitute an out- of-distribution setting for the model, potentially failing to capture its intended utility unfairly penalizing its performance. Therefore, we will supplement the evaluation results for apo structures in DynamicBind in the future, making them more rigorous.
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REPRODUCIBILITY STATEMENT
The code used in this paper can be found in CoapeiibbbcoomCCaaa////S//X process PoseX-SD and PoseX-CD, well the experiments carried out in this work, could be reproduced by following the instructions in README. The corresponding parameters of all the methods are shown in Appendix B
The construction
ACKNOWLEDGEMENTS
Tianfan Fu supported by Young Scientists Fund (C Class) of the National Natural Science Foundation of China (Grant No. (2506154) and Nanjing University International Collaboration Initiative.
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A DATASET CONSTRUCTION AND STATISTICAL ANALYSIS
A.1 DATASET CONSTRUCTION PROCESS
Table S1: Construction process of PoseX Self-Docking (PoseX-SD).
# proteins
ligands
Selection Step
(unique PDB IDs)
(unique CCD IDs)
PDB entries released from January 1st, 2022 to January 1st, 2025 feature refinement resolution of 2 Å better and include at least one protein and one ligand
13207
6877
Remove unknown ligands (e.g.. UNX, UNL)
13202
6875
Remove proteins with sequence length greater than 2000 Ligands weighing from 100 Da to 900 Da
11771
6442
9768
6196
Ligands with least 3 heavy
9706
6163
Ligands containing only H. C. O. P. CI atoms Ligunds that not covalently bound protein Structures with unknown
9030
5741
8383
5185
(e.g., element X)
8349
5166
Ligand real space R-factor Ligand real space correlation coefficient at least 0.95 Ligand model completeness 100%
0.2
7521
4476
5734
3426
5645
3358
Ligand starting conformation could generated with ETKDGv3 ligand SDF files can loaded with RDKit and pass its sanitization PDB ligand report does not list stereochemical errors PDB ligand report does not list any atomic clashes Select single protein-ligane conformation Intermolecular distance between the ligand(s) and the protein is at least
5638
3351
5634
3345
3317
2541
3971
2541
3945
2527
0.2 Á
Intermolecular distance between ligand(s) and other small organic
3889
2477
molecules at least 0.2 Á
Intermolecular distance between ligand(s) and ion metals in complex is at least 0.2 Á
3889
2477
Remove ligands which are within 5.0 Å of any protein symmetry mate Get a set with unique pdbs and unique cods by Hoperoft-Kann matching algorithm
2451
1598
1587
1587
Select representative PDB entries by clustering protein sequences
718
718
The first conformation chosen when multiple conformations are available in the PDB entry.
2 Clustering with MMseqs2 is done with sequence identity threshold of 0% and a minimum coverage of 100%.
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Table S2: Construction process of PoseX Cross-Docking (PoseX-CD)
# proteins (unique PDB IDs)
ligands
Selection Step
(unique CCD IDs)
PDB entries released from January 1st, 2022 to January 1st, 2025 feature refinement resolution of 2 Å better and include at least one protein and one ligand
13207
6877
Remove unknown ligands (e.g.. UNX, UNL)
13202
6875
Remove proteins with sequence length greater than 2000
11771
6442
Ligunds weighing from 100 Da 900 Da
9768
6196
Ligunds with least heavy Ligands containing only
9706
6163
O, P, CI atoms Ligands that are not covalently bound to protein Structures with unknown atoms (e.g., element X)
9030
5741
8383
5185
8349
5166
Ligand real space R- facttoo at most 0.2
7521
4476
Ligand real space correlation coefficient at least 0.95 Ligand model completeness 100%
5734
3426
5645
3358
Ligand starting conformation could generated with ETKDGv3 ligand SDF files can loaded with RDKit and pass its sanitization PDB ligand repurt does not list stereochemical errors PDB ligand report does not list any atomic clashes Select single protein-ligant conformation
3351
5634
3345
3317
3971
2541
3971
2541
Intermolecular distance between the ligand(s) and the protein is at least
3945
2527
0.2 Á
Intermolecular distance between the ligand(s) and the other ligands is least 5.0 Á
2232
1536
Remove ligands which are within 5.0 Å of any protein symmetry mate Cluster proteins that have at least 90% sequence identity Structures can be successfully aligned to the reference structure in each cluster
1240
908
890
708
371
362
The first conformation chosen when multiple conformations are available in the PDB entry.
2 Clustering with MMseqs2 done with sequence identity threshold of 90% and minimum coverage of 80%.
Each candidate protein is structurally aligned to the reference protein via the superposition of C. atom of amino acid residues using PyMOL, A candidate PDB entry is removed if the RMSD of the protein alignment greater than 2.0 Á and candidate ligand is removed if is 4.0 Å away from the reference ligand.
A.2 STATISTICAL CHARACTERISTICS
PowD
Pasex CD
Eount di "trge
Prequem uentyy (N1
30
10
5
0.0
0.2
0.4
0.6
0.8
10
Numbor of Structures per Target
Pockect Similbrity
(a) Distribution of structures per target
(b) Distribution of pocket similarities
Figure S1: (a) The distribution of structures per target shows that every protein adopts at least two distinct conformations, and about half of the targets are represented by just two. (b) The distribution of pocket similarities.
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B
DOCKING METHODS AND EVALUATION SETTINGS
This section presents the docking methods employed in evaluation and illustrates the corresponding
setups.
B.I PHYSICS-BASED METHODS
Physics-based methods employ physical forces and geometric complementarity model molecular interactions, predicting ligand binding to the target protein. Usually, the atomic coordinates of the protein's binding site remain fixed, while the ligand undergoes flexible conformational changes. This schema reduces the computational complexity of docking simulations by neglecting the protein's s dynamic flexibility. Although computationally efficient, this method may fail fully account for the inherent flexibility of proteins, as biological systems often exhibit conformational changes upon ligand binding. We include physics-based methods in this paper. including Discovery Studio (Pawar & Rohane 2021). Schrödinger Glide (Friesner et al. 2004), MOE (Vilar al. 2008), AutoDock Vina (Trott Olson 2010 Eberhardt et al. 2021) and GNINA (McNutt al. 2021
B.1.1 SCHRÖDINGER GLIDE
Schrödinger Glide is leading provider of biomolecular simulation software, and Glide one of its flagship products, focusing on precise molecular docking simulations (Friesner et al. [0044] Bhachoo & Beuming! 2017). Glide adopts unique hierarchical docking approach, starting with coarse screening and then performing fine optimization on high-scoring results to improve prediction accuracy.
Software Version: Schrödinger Suite 2022-1, Build 141
Docking Workflow
1. Use PreaWizard to preprocess the protein files by adding hydrogens and optimizing with the OPLS3 force field at pH 7.4.
2. Use LigPrep preprocess small molecules, preserving the chirality of the input ligand. Use Epik to predict the рKa and protonation states of small molecules at pH 7.0. Optimize the mmall-molecule conformations using the S-OPLS force field, and output one small-molecule conformation as the input for docking.
3. Define the INNERBOX dimensions as 10 x A, and the OUTERBOX dimensions as:
Size.
Tmax Zmin + 20
Size, Size.
Vmax
Vmin + 20
2max
Emin + 20
The force field is set to OPLS3, and all other parameters are set by default. Generate grid file.
4. Perform molecular docking using Glide SP (Standard Precision), and output one small molecule pose the docking result.
Runtime Environment: Run on an Intel 19-10XX0X CPU using cores.
B.1.2 DISCOVERY STUDIO
Discovery Studio (Pawar& & Rohane 2021), developed by Dassault Systèmes BIOVIA, is
prehensive life sciences research platform that covers molecular modeling, virtual screening, and more. For protein-ligane binding, Discovery Studio performs conformational sampling around given binding site and ranks potential poses using physics- based scoring functions like CDOCKER (which combines grid-based molecular dynamics and CHARMM force fields).
Software Version: 1..21.1.
Docking Workflow:
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1. Use the Proteins Preparation components in Discovery Studio to process the protein files. The protein was protonated at pH 7.4 with solvent ionic strength of 0.155 M. Minimization performed using the CHARMm force field to optimize the protein structure, and all other parameters are set by default.
2. Use the Ligands Preparation components in Discovery Studio process the ligand files. Enumerate ionization states for cach ligand within a pH range of 6.5-8.5. Enumerate automeric forms for each ligand with a maximum of 10 tautomers per ligand. Fix the bad valencies by adjusting formal charges, and all other parameters set by default.
3. Dock the prepared proteins and the corresponding prepared ligands using the CDOCKER components Discovery Studio. The docking site was centered at:
xc Yo
Zmuaz H-MPanii
Minaxe Menin
Zzc
Define the binding sphere radius as:
R max ((Fmax - Fmin), (ymax
Mmnin) (2max Zmnin)
The docking simulations were performed using the CHARMm force field. Assign the partial charges to the ligands via the Momany-Rome method, and all other parameters are set by default. 10 top docking poses output each docking run, and the best-scored pose was selected as the final docking result.
Runtime Environment: Run on an Intel Ultra 125H CPU using cores.
B.1.3 MOLECULAR OPERATING ENVIRONMENT (MOE)
Molecular Operating Environment (MOE) Vilar al. 2008 developed by the Canadian company
Chemical Computing Group, is a commercial drug discovery software platform that combines visualization, modeling, simulations, and methodology development into single, unified package.
Software Version: MOE 2024.06.
Docking Workflow
1. An SVL script automates the docking pipeline.
2. The StructurePreparatte function is employed to preprocess protein structures.
3. The binding site defined by reference ligands.
4. The Triangle Matcher algorithm is utilized to generate initial ligand poses.
5. The scoring function is configured London dG, with maximum of 30 poses generated.
6. Poses are refined using fixed receptor, optimizing only the ligand's position and conforma- tion, with the re-scoring function configured as GBVI/WSA dG and maximum of poses retained.
Runtime Environment: Run on an AMD EPYC 9554 CPU.
B.1.4 AUTODocK VINA
AutoDock Vina (Eberhardt et al. 2021) is one of the fastest and most widely used open-source molecule docking programs. It combines global search (to identify potential binding modes) with local optimization (to refine these modes).
Software Versions
AutoDock-Vinal 1.2.6
MGLTools: 1.5.7
Reduce: 4. 4.14.230914
OpenBabel: 3.1.0
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Meeko: 0.6.1
Docking Workflow
1. Use Reduce to add polar hydrogens to the protein structure.
2. Use OpenBabel to add non-polar hydrogens and normalize atom names, exporting the protein in a format recognizable by MGLTTolls.
3. Use the receptor receptor_prepareeeee preparre.pppyp script from MGIToolss to convert the hydrogen-added protein PDB file into PDBQT file.
4. Use OpenBabel to add hydrogens to the ligand molecule pH 7.4.
5. Use the mk prepare ligand.py script from Meeko to convert the hydrogen-added ligand SDF file into a PDBQT file.
6. Define the docking box center and size as follows:
Eminn
Vo Zc
=
Minax Viiin
2
Smax Smnin
,ize,
Fmax
min + 20
Size,
Vmax
Vmin + 20
Size.
Fmax
Zmin +
7. Perform molecular docking using the prepared protein and ligand PDBQT files.
8. Use vina vina_split to split the output file, extract the best-scored pose for each ligand, and convert the resulting PDBQT file into an SDF file using Meeko for the final output.
Runtime Environment: Run on an AMD EPYC 9554 CPU, with no specified core limit and to 256 cores available.
B.1.5 GNINA
GNINA MMNutt et al. 2021 2025) is a relatively new project that introduces DL techniques into the field of molecular docking, particularly leveraging convolutional neural networks (CNNs) scoring functions to improve docking scoring. is an open-source software.
Docker Image:
Running Parameters: The command used is:
gnina -r rec. sec. -1 lig. -autobox_rett sdf -o out. out.sdf,
where lig. adf is ppa_cco_ligand_starr_oonff PDB_CCD PDB_CCD_ligande lig_ndigadd.. sdf.
and ref.edf is
Runtime Environment: Run on Nvidia A6000 GPU.
B.2 AI DOCKING METHODS
Al docking methods utilize SMILES strings of ligands and three-dimensional structures of protein targets input to predict energetically favorable ligand conformations bound target proteins. These methods systematically explore the conformational space of small molecules to identify low-energy configurations that optimize the binding affinity to proteins. By sampling diverse ligand conformations, Al docking methods enhance the optimization of spatial arrangements to maximize interactions with protein active sites, including hydrogen bonds, hydrophobic interactions, and electrostatic complementarity. We involve 11 AT docking methods in this paper, including DeepDock (Méndez- Lucio et al. 2021), EquiBind (Stärk et al. (002)), TankBind (Lu et al. 2022). DiffDook (Corso et al. 2022), UMD V2 (Alcaide et al.] 2024), FABind (Pei et al. 2023). Dick
(Corso et al. 2024) DiffDock-Pockee (Plainer et al. 2023). DynamicBind (Lu et al. 2024).
Interformer (Lai al. 2024 and SurfDock (Cao al. 2024).
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B.2.1 DEEPDOCK
DeepDock (Méndez-Lucio al. 2021 geometric DL model that learns a statistical potential based on the distance likelihood.
GitHub Repository: http://///ot//tatooo.oo///a/aaaa
GitHub Commit Hash: abl 40666644444444444044444400444466
Running Parameters: Default parameters used in evaluation.
Runtime Environment: Run on Intel(R) Xeon(R) CPU E5-2620 v4.
B.2.2 BQUIBIND
EquiBind (Stärk 2022 an -quivvaiiiaarian geometric DL model designed for direct-shot prediction of both i) the receptor binding site (blind docking) and ii) the ligand's bound pose and orientation.
GitHub Repository:
GitHub Commit Hash: 4bbo0tttcollsssicccccddod0o4oa6a
Running Parameters: Default parameters are used in evaluation.
Runtime Environment: Run on Nvidia A6000 GPU.
B.2.3 TANKBIND
Tink (Lu et al. 2022) incorporates trigonometric constraints robust inductive bias into the model, and explicitly examines all potential binding sites for each protein by dividing the entire protein into functional blocks. establishes efficient diffusion process within this space.
GitHub Repository: ptteas//gthaab.ccm/////////////1
GitHub Commit Hash: #1855511db1 1e5f51ddb11e h4BB330bbb.llfscccc4022200066660
Running Parameters: Use the structure of the entire protein as input for prediction, rather than
chains within 10Å of the ligand in the default setting.
Runtime Environment:
Run on an AMD EPYC 9554 CPU.
B.2.4 DIrrDock
DiflDock (Corso et al. 2022) is a diffusion-based generative model defined on the non-Euclidean manifold of ligand poses. It maps this manifold to the product space of the degrees of freedom (translational, rotational, and torsional) relevant to docking and establishes an efficient diffusion process within this space.
GitHub Repository:
GitHub Commit Hash: Ao06111444uus30eec07770022222020
Running Parameters: Default parameters are used in evaluation.
Runtime Environment: Run on Nvidia A800 GPU.
B.2.5 DIFFDocx-L
Diff (Corso et al. 2024 is variant of DiffDock that scales up data and model size by integrating synthetic data strategies.
GitHub Repository: httpss//gthhbb.co////sss///////t
GitHub Commit Hash: bFT24/9kee77082222000ccc33333233
Running Parameters: semplles_per_complee is changed from the default value of 10 to 40.
Runtime Environment: Run Nvidia A800 GPU.
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B.2.6 DIrrDock-PocKE
DiflDock-Pocket (Plainer et al. 2023) variant of DiflDock with additional binding pocket specification.
GitHub http:///w...oss.............aaar
GitHub Commit Hash: 39DD0d44222224477744000000222222
Rumning Parameters: Default parameters used in evaluation.
Runtime Environment: Run on Nvidia A6000 GPU.
B.2.7 DYNAMICBIND
DynamieBind eLu al. 2024) utilizes cquivariant geometric diffusion networks to generate a smooth energy landscape, facilitating efficient transitions between various equilibrium states. DynamicBind accurately identifies ligand-specifes conformations from unbound protein structures, eliminating the need for holo-structures or extensive sampling.
GitHub Repository: htttpe///ghhab..ccmm///vv///////
GitHub Commit Hash: abded83f3 11D0
Running Parameters: Input proteins are holo structures provided by the PoseX dataset, and internal relaxation is disabled.
Runtime Environment: Run on Nvidia A800 GPU.
B.2.8 FABIND
FABind (Pei et al. 2023) is an end-to-end model that integrates pocket prediction and docking to achieve precise and efficient -igand binding predictions. It involves ligand-informed pocket prediction module, which is also utilized to enhance the accuracy of docking pose estimation.
GitHub Repository:
GitHub Commit Hash: bobb11144725500ee387922220ee1
Running Parameters: Default parameters used in evaluation.
Runtime Environment: Run on Nvidia A800 GPU.
B.2.9 UNI-MoL DOCKING V2
UMD V2 (Accide et al. 2024) represents Uni-Mol Docking v2. It combines the pretrained molecular and pocket models to learn the distance matrix and then uses coordinate model to predict the molecule's final coordinates.
GitHub Repository:
unimol unimol_docking v2
GitHub Commit Hash: 48661665560017224499911702222222
Running Parameters: Default parameters are used prediction. About one-fifth of the molecules in
the model output will encounter RDKit's sanitization check errors. This issue is resolved by reading in the correct molecular topology and then assigning the coordinates predicted by Uni-Mol Docking v2 the molecules with the new topology
Runtime Environment: Run on Nvidia A6000 GPU.
B.2.10 INTERFORMER
Interformer Lai et al. 2024). unified model based on the Graph- Traph-Transoomerr architecture, is specifically designed to capture non-covalent interactions using interaction- mixture density network. Furthermore, employs negative sampling strategy to adjust the interaction distribution, thereby improving affinity prediction accuracy.
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GitHub | http://ppp////aaaaccccaa/aaaaaaa
GitHub Commit Hash: Sacce//Haaaaaaa7777sss6671101060
Running Parameters: Use PDB_
obtain the pocket, perform UFF optimiza-
tion on PDB_CCD htt.ccc_iaannn_srrt_oooooo -uff 1 ligand option during prediction.
and replace in the uff folder, and use the
Runtime Environment: Run on Nvidia A6000 GPU.
B.2.11 SUREDoCK
SurfDock (Cao et al. 2024) combines protein sequences, ttree-dimensional structural graphs, and surface-level features within equivariant architecture. It leverages generative diffusion model on a non-Euclidean manifold to optimize molecular translations, rotations, and torsions, producing accurate and reliable binding poses.
GitHub Repository:
GitHub Commit Hash: 22422foeccittccccooll11aa166666.
Running Parameters: Default parameters are used in evaluation.
Runtime Environment: Run on Nvidia A6000 GPU.
B.3 AI CO-FOLDING METHODS
Al co-folding methods represent significant advance in computational biology by simultaneously predicting the conformation of both the protein and its associated ligand, which sets them apart from physics-based methods and Al docking methods. In contrast to physics-basee baased methods, which typically assume fixed protein structure and focus on optimizing ligand placement, or Al docking methods that may still rely predefined protein conformations, A/ co-folding methods adopt more holistic strategy-taking only the protein's 's amino acid sequence and ligand's SMILES strings as input. These methods aim to capture the dynamic interaction between proteins and ligands by predicting their structures in tandem, enabling more accurate representation of how these molecules interact in biological systems. In this paper, we involve Al co-folding methods, including NeuralPLexer (Qiao et ). Tosol-FFFod--
(RFAA) (Krishna et al. (2024). AlphaFold3 (Abramson et al. (2024]
Chai-1 (Discovery et al. 2024). Boltz-1 (Wohlwend al. (204)). Boltz- 1x (Wohlwend al. (0024) and Protenix (Team et al. 2025). It should be noted that in our evaluation of Al co-folding methods,
we did not consider nost-translational modifications and used unmodified protein sequences as input.
B.3.1 NEURAI.PLEEER
NeuralPLexer (Qia et al.) a physics-inspreed flow-based generative model for biomolecular complex structure prediction based sequences only. NeuralPLexer combines protein language model with graph encoding to learn sequence information and represent 3D molecular structures and bioactivity information.
GitHub
GitHub Commit Hash: 2c5000/000044333366o6ccccl666660
Running Parameters: Default parameters are used in evaluation.
Runtime Environment: Run on Nvidia A6000 GPU.
B.3.2 RosETTAATOLD-ALL-TTOO
RoseTTAAFlld-AlAtAtt (RFAA) (Kishhaa et al., 2024 is generalized foundation model for
all-atom biomolecular structure prediction and design, including protein, nucleic acid, and other small molecules. RoseTTAFold-All Aol----l is 3-track based architecture incorporating equivariant neural networks for all atomic structure prediction. Mcanwhile, it integrates with RFDiffusion for molecular design.
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GitHub Repository:
Bttp:r//gthubb..mo/bbeeee-eo
RoseTTAFold-Al1
GitHub Commit Hash: 0a114443/////oooo0eaaaaaaaaaaaa0
Running Parameters: Default parameters are used in evaluation.
Runtime Environment: Run on Nvidia A800 GPU.
B.3.3 ALPHAFOLD3
AlphaFold3 (Abramson et al. 2024), developed by DeepMind, represents the latest advancement in protein structure prediction technology. Building on the successes of its predecessor AlphaFold 2 (umperr et al. (02T)), AlphaFold3 adopts diffusion model instead of structure module in
AlphaFold2. not only improving the aceuracy of protein folding but also supporting the structure prediction of complexes (e.g., protein-RNA, protein- ligand,, which enables its usage in protein-ligand docking.
Software Version: 3.0.0
Running Parameters: Except for the number of seeds being set 1, the rest the predictions are made using the default parameters. We finally select the top 1 result for evaluation.
Runtime Environment:
Run on Nvidia A800 GPU.
B.3.4 CHAI-1
Chai-1 (Boitreaud et al. 2024 is multimodal molecular foundation model that can also predict structures with single sequence. By leveraging the decoder-only Transformer framework, which is widely used in Large Language Models (LLM) like GPT, Chai-1 encodes sequential information without database search. Moreover, Chai-1 accepts various chemical or biological constraint features input predict more accurate molecular structures.
Software Version: 0.5.2
Running Parameters: Use the online MSA server to obtain MSA information, keep the rest as default settings, and select the top result for evaluation.
Runtime Environment:
Run on Nvidia A800 GPU.
B.3.5 BOLTZ-1
Boltz-1 (Wohlwend al. 2024) aims to reproduce AlphaFold3 and release all code (model architecture, training, and inference), achieving competitive performance. Additionally, Boltz-1 I introduces several architectural innovations, including a novel reverse diffusion process and revamped confidence model, enhancing its predictive accuracy and robustness.
Software Version: 0.4.0
Running Parameters: Use the MSA online server to obtain MSA information, set diffusion samples to 5, and select the top 1 result for evaluation.
Runtime Environment:
Run on Nvidia A800 GPU.
B.3.6 BOLIZ-Ix
Boltz-1x (Wohlwend et al. 2024) is advanced version of the Boltz-1 model. It introduces a novel inference-time stecring technique, which enhances the physical quality of predicted poses by reducing hallucinations and non-physical predictions. This ensures more reliable and biologically plausible structures.
Software Version: 1.0.0
Running Parameters: Use the MSA online server to obtain MSA information, set diffusion samples to 5, and select the top 1 result for evaluation.
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Runtime Environment: Run on Nvidia A800 GPU.
B.3.7 PROTENIX
Protenix (Team et al. 2025 is comprehensive and open-source reproduction of AlphaFold3, developed by ByteDance. introducs several architectural innovations, including modular PyTorch framework that facilitates full training and inference, and optimizations such as custom CUDA kernels and BF16 training to enhance computational efficiency.
Software Version: 0.4.2
Running Parameters: Use the MSA online server to obtain MSA information, and the seed set to
101.
Runtime Environment: Run on Nvidia A6000 GPU.
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B.4
Training DATA CUTOFF TIMES
Table S3: Training Data Cutoff Times for Different Methods
Method
Training Data Cutoff Time
Traditional physics- based methods
Discovery Studio
Pawar & Rohane
2021
N/A
Schrödinger Glide [Freenner et al. 2004)
N/A
MOE (Vilar et al. 2008)
N/A
AutoDock Vina ( Toott & Olson 2010
Eberhardt et al. 2021
N/A
GNINA (McNutt al. 2021)
2018- 12
AI docking methods
DeepDock Méndez-Lueee et al.
2021
2018-12
EquiBind
Stark ct al. (0022)
2018-12
TankBind Lu et al. 2022)
2018-12
DiffDock Corso et
2022
2018-12
UMD V2 (Alcadee ct al.
2024
2018-12
FABind (Pei al. 2023
2018-12
Dif (Corso et al.
2024)
2018-12
DiffDock (Plainer et
2023
2018-12
DynamicBind (Lu et al. [2024)
2018-12
Interformer (Lai al. 2024)
2018-12
SurfDock CCoo et al.
2024
2018-12
AI co-folding methods
NeuralPLexer QQao al.)
2018-12
RoseTTAAFold-AllAAtoo Krishna et al.
2024
2021-11
AlphaFold3 (Abramson et 2024
2021-10
Chai-1 (Discovery et al.) 2024)
2021-02
Boltz-1 Wohlwend al. 2024
2021-10
Boltz-1x Wohlwend et a.
2024
2021-10
Protenix Team et 2025
2021-10
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C TECHNICAL DETAILS OF RELAXATION PROCESS
Our relaxation is based on the following software: OpenMM 7.7 Fastman et al. 2017). PDBFixer
1.8 (Eastman al., 2012-2025), RDKit 2023.09 (Contributors) 2006-2024). AmberTools 23, and OpenFi 2.1.0 (Consortium 2024
contains the following essential steps:
Structure preprocessing and integrity restoration. Use PDBFixer (v1.8) to handle the initial structure files:
Parse complete protein sequence information from CIF files, retaining water molecules and metal ions within a range of the ligand in Al-predicted models.
Standardize non-canonical amino acids to canonical forms (e.g.. SEP to SER), simultaneously correcting the protein sequence database.
Detect structural deficiencies using the and apply the Atosss module complete atoms (including N-terminal ACE and C-terminal NME capping).
algorithms,
Molecular topology construction and validation. To address the lack of bond order information in PDBFixer:
Integrate Amber fF1SSB force field atom types and topology bond parameters to establish bond order matching rules.
Build a molecular graph model with RDKit (v2023.00 .0) and perform SanitizeMol standardization checks (including charge correction and stereochemistry validation).
-Apply the RDKit AddHs module for protonation, optimizing the spatial arrangement of hydrogen atoms.
Force field parameterization. Employ a multi-scale force field combination strategy:
-oor protein systems: Generate Amber f114SB force field parameters using OpenMM 7.7.
For ligand systems: Perform OpenFF 2. 1.0 (Consortium) 2024) parameterization using the
OpenFF 1.0 toolkit, including mmff94s charge calculations and XML topology generation.
Constrained molecular dynamics optimization. Implement energy minimization the OpenMM 7.7 platform (Fastman et al. 2017):
Apply additional forces (0.5 * ((x - x0)² + (y yo) + (z - 20) (where k 10, Ho. No. original 3D coordinates) to constrain backbone atomic positions in the protein structure, keeping newly added atoms free.
Constraints:
IIntegration parameters:
Langevin thermostat (300 K, friction coefficient ps ps-)), time step
0.004 ps.
Convergence criteria: Energy gradient convergence threshold S 10 KJ/mol/mm.
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D DESCRIPTION OF VALIDITY
The validity checks for the structures analyzed in this study were conducted using PoseBuster (Butten- schoen et al. 2024). tool for assessing the reliability and accuracy of molecular poses. The validation process encompasses chemical validity and consistency, intramolecular validity, and intermolecular validity, each assessed with specific criteria detailed below. In this study, we define structural plausibility as stereochemical correctness and intra- and intermolecular validity.
D.1
CHEMICaL VALIDITY AND CONSISTENCY
File loads:
The input molecule be successfully loaded into molecule object by RDKit.
Sanitisation:
The input molecule passes RDKit's chemical sanitisation checks, ensuring adheres to basic chemical rules.
Molecular formula:
The molecular formula of the input molecule is identical to that of the true molecule.
Bonds: The bonds in the input molecule are the same in the true molecule.
Tetrahedral chirality: The specified tetrahedral chirality the input molecule is the same in the true molccule.
Double bond stereochemistry: The specified double bond stereochemistry in the input molecule the same as the true molecule.
D.2 INTRAMOLECULAR VALIDITY
Bond lengths: The bond lengths in the input molecule are within 0.75 of the lower and 1.25 the upper bounds determined by distance geometry.
Bond angles: The angles in the input molecule are within 0.75 of the lower and 1.25 of the upper bounds determined by distance geometry.
Planar aromatic rings: All atoms in aromatic rings with or members are within 0.25 Á of the closest shared plane.
Planar double bonds: The two carbons of aromatic carbon-carbon double bonds and their ring neighbours are within 0.25 Á of the closest shared plane.
Internal steric clash: The interatomic distance between pairs of non-covalente non-covalently bound atoms above 0.7 of the lower bound determined by distance geometry.
Energy ratio: The calculated energy of the input molecule is more than 100 times the average energy an ensemble of 50 conformations generated for the input molecule. The energy is calculated using the UFF in RDKit, and the conformations are generated with ETKDGv3, followed by force field relaxation using the UFF with up to 200 iterations.
D.3 INTERMOLECULAR VALIDITY
Minimum protein-ligand distance: The distance between protein-ligamt atom pairs is larger than 0.75 times the sum of the pairs' van der Waals radii.
Minimum distance organic cofactors: The distance between ligand and organic cofactor atoms is larger than 0.75 times the sum of the pair's 's van der Waals radii.
Minimum distance to inorganic cofactors: The distance between ligand and inorganic cofactor atoms is larger than 0.75 times the sum of the pair's covalent radii.
Volume overlap with protein: The share ligand volume that intersects with the protein is less than 7.5%. The volumes defined by the van der Waals radii around the heavy atoms, scaled by 0.8.
Volume overlap with organic cofactors: The share of ligand volume that intersects with organic cofactors is less than 7.5%. The volumes are defined by the van der Waals radii around the heavy atoms scaled by 0.8.
Volume overlap with inorganic cofactors: The share of ligand volume that intersects with inorganic cofactors is less than 7.5%. The volumes are defined by the van der Waals radii around the heavy atoms scaled by 0.5.
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E ADDITIONAL TABLES AND FIGURES FOR MODEL EVALUATION
PoseX-SD Result
AlphaFold3
Dock Vina
Docking
Ducking 0.27%
Douking
01229
RN50
Pocket Similanty
Poc Smilanty Dock
Pocket Simlnttt DiffDock
Pocket Similanity DiffDack-L
Chal-1
Docding Samples ru-d
Bocking Samppees ra 4.010
Dacking Samples
Dooking Samules 4.278
0,281
Aoooa (A)
RoCC (A)
RMRR
Poobet Similentt
Pre Simlirity Discovery Studio
24
Simlinity DynamicBind
Porket Similerty EquiBind
DortDack.Pocket
Docking
Ducking
RMSS (A)
Rooo (
Pocket Similantty FABind
P 5 milintty
Pockitt Simiarty GNINA
Pockect Similanty Interformer
Glide
Decidng Camples p 236
Deeking Samplex
Decting Samples
Docking no Samples 4.112
0.015
tie 4,053
RMED (A)
FINEO AA
ROMED (A)
Pockict Similanty
Pocket Simlianty
Pocket Simllatty Protenix
Pocket Similbnity
Samples, NeuralPLaxer
Semples
RONSO Aeb
Pocket Similarity
Poc kot S mifrrity
Pockat Similrity
Pocket Similarit
SurtDock
TankBind
UMD V2
Docidng Samples
Bocking Samples
Dacking Samplees
0,246
ru d0.33
Similanity
Simliaity
Similnttt
The performance of most Al-based approaches is significantly influenced by pocket similarity under self-docking setup. Among them, Protenix (Team et al.) 2025) exhibits the strongest negative correlation (r= -0.390), whereas SurfDock (Cao et al. 2024). an Al-based model, demonstrates minimal statistical association. In contrast, physics-based methods, such as Auto Vina and Glide, are relatively unaffected by protein similarity.
Figure S2:
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PoseX-CD CD Result
Alphafold3
Auto Vina
Bolta-1
Boltz-1x
Decdnn Samples
Becking Samples
Darting Samples
Docking Samples 4.535
0.436
0,204
ro 4.521
EE Similrity DiffDock
En Similerky DiffDockel
Siiiarity
Chal-1
Deep
RASD
BMSD
Pocket Similarity DiffDock-Pocket
Poc kot S iisarity Discovery Studio
Pocket Similaiiy DynamicBind Docking Samples 4.5%
Pocket Similakky
EquiBind
Dociing Samples
Docking Samples 0,053
Dock no Samplees 4.274
0.425
RN05O 04
meso 0l
meso
21
20
23 Simliaity
Simlinity
Doceing Sumppee FABind
Glide
GNINA
Interformer
Docking Sumplees
Docking Samples
Docking
RASD
Pocket Similenity
Pox bet 5 mimiritt NeuraiPLexer
Pocket Similerity
Pocket Similerky
Protenix
Dociting Sumples
Docking Sumples 0:342
Docking Samples
Dodking Samplies 0.299
0.070
0,553
Rit5o (A)
S
2
Simibnnty
Smilantt TankBind
Similntty UMD V2
Similantty
SurfDock
Docidng Sampltes 0.3
Decking Sumples
Dockiing Samples
Pocket Pocket Similarity
Pockat 5 siilartty
Pocket Similrity
Figure S3: The performance of most Al-based approaches is significantly influenced by the pocket similarity in cross-docking scenario, where similar conclusions as the self-docking scenario can be derived.
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Posel 50 RXSSO Vakuee Santed Focon: Simttart
Batoo
Callia
M000
20
Figure S4: Performance on the PoseX-SD dataset sorted by pocket similarity. Samples are sorted by pocket similarity in descending order, and the RMSD results are processed with moving average (window size: 100). can be seen that most Al-based approaches degrade as pocket similarity decreases, while physics based methods perform relatively stably.
Valuea by Phocktt Eiilartty (M422)
5-10 Aurus
Syvenadid
10220-04
Figure S5: Performance on the PoseX-CD dataset sorted by pocket similarity. The results are similar those on PoseX-SI (window size: 20).
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Poeax-33 5D RVID Value Sented by Medeodaar ovaghe (VA220]
Usean cale
TAED 54
Figure S6: Performance the PoseX-SD dataset sorted by molecular weight. Samples are sorted by molecular weight in ascending order, and the RMSD results are processed with moving average (window size: 100). It can seen that most approaches exhibit a clear performance drop for ligands heavier than 450 Da.
Reatinal
Figure S7: Performance on the PoseX-CD dataset sorted by molecular weight. The results are similar those on PoseX-SI (window size: 20).
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Taatne sus sol b-4 s008
lindied
Dyvenittedd.o 5.08 sad 0033
Dynenitteddd 2.00
sotoodie 2..0 050 0:00
UND V2-10 140 14 1434
KENA. 14 sa34 s020
Ba 2.00 5:00 5000
2011 1:01:0
Chill
SrAh 2014 Mlanccetery
PINTA JAI
drch 2014 (144141
polkmao Po with prone
SIn Jangtt
PISTH chan
2011
Flroree TO POrDAR COAAte
Figure S8: The proportion of models filtered out based on various filtering criteria in PB-Valid (PoseX-SD).
hex aedd suv sue
Dynenisteed
Dynanictind
DilDeok-
Arteect4 4. 3232
pitocck- 5.00 020 5:10 5235
5233 20 5215
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5035 5215
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201
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MOC
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SHENO ARL PROERR COlEze
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Figure S9: The proportion of models filtered out based various filtering criteria in PB-Valid (PoseX-CD).
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Table S4: Performance (success rate) on PoseX-SD, PoseX-CD and Astex
Powk-SD
PoRkCD
Asios
Merhook
RXSD
24
RMSD LA
FB-Vaid wridas
RM50
2A
RMSD 2AA
PB-Vii
RMSD
24
RMSD e 2A& PB-NAR
Wridas.
wibriaas
Wirddas.
Wil volix.
w/rdlas.
Suribbck
41 13..8.1133
16/920005
700420003
3108-1335 127
14,3 14.35.002
BUSSIRLY
LMD V2
75 18040100
22 22
59. 0019920.00
09.240000
57:355000
58-25-8000
96.22000
04,1211000
ES 15.00:0000
85.71-1000
Inse kemeer
86 5814.
64
00.19:0044
46.522100
47. 12222887
54892009
20:99-000
52.0720033
16.022225
26.2222777
75.2537771
84 4.88:0054
BiilDock
2600220
36 51
16.22 16.72:0.99
10:00:00
47.15:1133
4507-11-27
76.152105
Dysertatindd
24.41001
27:0020:77
1995:1559
25-77:0:00
32:77
0.5025
31.05:1:0
6600921778
59.02-11999
DespDook
19 0015-055 56
5:3-0030
15/99-0.87
17:11-070
16.71-0041
16.35-13 1:10
10.66-1111
10:000154
FAfind
153510775
17 17.27-0.0
17:11-0.11
3471-0000
18:18-0441
40010000
31:19-2.15
441:001
Tipiliiat
4:40 100000000
0.20
4:13-10:0
51110000
6.111000
0:001000
10.990000
1136-1800
4-1 0.00
Theenis
100010001
56.22110.94
14.31 0.77
47 0010000311
1041
AUN 10445
41.45
0.01
46.5 0.55
62.4011179
11:991042
Chai-1
6641100 49
16 161011.33
0:53
08 08.81130 0000
07:00000 1045
47110000
15.13
1:21
5015 1109
$2.720099
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64.9-00.5
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37.544033
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(a) PoseX-SD
Aarto Vte
(b) PoseX-CD
Figure S10: PLIF-Valid (Errington et al. methods. PLIF-Valid assesses recovery rates of ligaid interaction fingerprints, which identify the protein residue, the interaction type, and, optionally, the ligand atom involved in the interaction. Overall, the ranking of methods on PLIF-Valid closely follows the docking success rate ranking in Figure 2 and Figure 3 with SurfDock achieving the best performance. The relaxation module consistently improves PLIF-Valid for nearly all methods. Notably, physics-basee based methods show better interaction preservation than their geometric success rates suggest (e.g.. PoseX-SD: DiffDock-L success rate 47.1% > Glide (IFD) 46.5%. but PLIF-Valid 51.7% < Glide (IFD) 55.2%; PoseX-CD: DiffDDok success rate 47.1 % > Glide IFD 44.8%, but Vali- 49.9% < Glide IFD 58.8%). likely due to their implicit consideration of protein- ligand interactions. The detailed results with standard deviation are reported in Table 55
2025 evaluation PoseX-SD and PoseX-CD for all
33

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Published as a conference paper at ICLR 2026
Table S5: Performance (PLIF-Valid) on PoseX-SD, PoseX-CD and Astex
RoeN-5D
PocNCC
Methobs
PLIF-wdil
50%
100% FLIF-vald
PLIF-wald
505
PLIF-wid e 100%
PLIF-ald
50%
FLIF-wdid e
1995.
wirdas.
w/o rdlax
wrrdda
rdlas.
whedks.
wib rdax.
w.lekxx
wiboollx
w/rela
1.5224077
259420011
14.1
33,5-0055
50.522055
LMD V22
66.082000
09 2020..0
19.7450.00
26 38.8000.00
65.32200
70.44:000
23.4-000
2508=0000
70.72ad00
60.5120.00
30.4020000
Jake Katter
6171-20
07 4222) 49
23877111
5572-033
65:12
2245-106
56472814
26.5341.011
20002110 Li
DillDockL
1282
12.0420.54
1805-086
21:92:045
Dillbok
33.4380 55
40 3022l1 38
9.
12.79
4645m1 56
1400m
15.35:0.73
60.36e1011
53.00e1 102
162666 75
2585-0055
Pyromectind
5.45 10.4
0.36-1110
W121100
1146.
7811000
1857L110
7-911
3 52000222
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1100110:3 29
22 1230..0
198-11641
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21
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15.112010
66331100
150610000
1838
Eqpitiadd
4.4:0000
10 10
102:0000
2:10:0000
5852000
100920000
21211000
35720000
106.000
11:06:044
7500:1000
Clas-
56. 08.79220.0
57.362130
23 25
19381:0.2 26
65.13:0055
28.31-0.9
2::21:0211
14.1::110
500011 12
Beie-1
50.5240
52 52.911100
10:11:000
04:521113
25:00:001
2500-2004
152520177
7287111 16
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6445-003
3447:200
71226:11-0
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35 18221.
1.27:0.55
7.87210.36
11128.52
04130.00
2073022
C3015500
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14.1220
23 25
4.75:10.29
2408:0554
32:342022
25/52114
15
4,3325.31
24.4:00444
23:60-0733
27:2022588
28.521157
17300000
Glide
52.5620.0
25.1120000
200020.000
21 11.02:0.00
48385500
43.31:000
19.76.0000
10:5:0000
62.302000
62302000
182291000
25 25.0120000
Clide (IT))
00:1510111
55 13.21.0.0
20 6m
69.13 1010
19:00 0000
11.17 0.00
11:14 1000
61.71 10:0
15:11000
11:5.1000
15 (1:00
05
41:07 0034
6.11
11:11
41:4
13
17.14:0.25
80GX VGO
AlphaFold3
Chai-1
Boltz- 1x
Reference
8FLV ZB9
AlphaFold3
Chai-1
Boltz-1
Boltz-1x
Reference
Figure S11: Case study AI co-folding methods in chirality validation. We compared AlphaFold3, Chai-1, Boltz-1, 1, and Boltz- 1x models on the 8OGX VGO and SFLV_ZB' complexes. The figure illustrates the docking results, with chiral centers marked by red circles, revealing that all co-folding models except Boltz-Ix exhibit chirality errors.
34

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Published as a conference paper at ICLR 2026
PDB entry 8V6Y with its ligand Yi8
Transfer Vi8 to SV71
Highlight key residues
Overkss
No evarlap
Ground true
input protein
Figure S12: Analysis of 8V71_Y18 in PoseX-CD. When transferring the ligand from its co-crystal structure to the protein structure used for docking through structural aligmment, steric clashes arise between the ligand and the protein, underscoring the challenges associated with cross-docking, In this case, all physics-based methods failed (RMSD 2 2Å), while the top-performing A/ docking method and Al co-folding method (SurfDock and AlphaFold3, respectively) accurately predicted the pose. The rightmost column illustrates the conformational variations in residues that overlap with the ligand across the two protein structures.
Pred. w/o relax. &
Pred. relax. GT
Highlight residues
Overtp
overta
GNINA
Piedi without relx..
Pred. with relax.
X
Residuc
reloid
Weliid
Glide|IFD)
Hallild
FBAbild
SurlDock
PS-tald
AlphaFoldB
SMID
RS-Woldd
FD-blld
Figure S13: Analysis of docking results for 8V71 YI8. The physics-basee based methods GNINA and Glide (IFD) generate ligand conformations that substantially deviate from the ground-truth structure. In contrast, SurfDock and AlphaFold3 generate docking poses that closely align with the ground-truth structure. SurfDock's docking poses exhibit steric clashes, which resolved through relaxation, whereas AlphaFold3's poses are sterically compatible. The rightmost column demonstrates that, for both SurfDock and AlphaFold3, key residues shift toward their corresponding positions in the ground-truth structure after relaxation.
35


Reviews:
[{"review_id": "53nBGwTtOZ", "reviewer": "Reviewer_Az7C", "summary": "PoseX is a new benchmark for a new variant in docking task using a series of new \"co-folding\" methods. Compared to previous benchmarks, PoseX have more design related to the recent changes in docking methods.", "questions": "1. Authors can report more details about other affecting factors, including the docking performance affected by molecule size, hard-ness of protein target, etc.\n2. The detailed parameters of the molecular dynamics simulation should be showed, which force field, the time length, the maximum round and the strategy, treatment of hydrogen bond, restrictions, solvent information and solvent model, etc.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "Since the development of AlphaFold3 and related methods, this task has drawn wildly attention. Old benchmark like PoseBusters show limitation in such tasks. Compared to traditional docking benchmark, cross-docking is harder, and able to detect more hallucinations cases which is often existed in AlphaFold 3 like models.", "weaknesses": "There is little to show the difference between co-folding methods and traditonal methods, since co-folding methods might have strong advantages in docking on protein with native conformations. I suggest authors to add more examples and highlight these tasks including docking on AlphaFold3 predicted native conformation or solved native conformations to stress this difference.", "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": "lZLvTKyyZA", "reviewer": "Reviewer_DUgZ", "summary": "An open-source benchmark that evaluates self-docking (SD) and cross-docking (CD) across 23 methods (physics-based, AI docking, and AI co-folding), using RMSD and PoseBusters validity (PB-Valid), plus an OpenMM-based relaxation module. The authors report that 9 AI methods surpass GNINA on CD (vs only 3 on SD), and that performance in CD strongly depends on pocket similarity (TM-score of residues within 10 Å, compared to pre-2022 pockets).\n\nPoseX curates 718 SD and 1,312 CD entries (2022–2025 PDB releases), applies filtering (e.g., ligand/protein proximity, symmetry-mate removal), clusters by sequence, aligns conformers by alpha carbons, and in CD evaluates a ligand against other conformers of the same target; success is top-1 RMSD < 2Å (optionally with PB-Valid), averaged at target level to correct for uneven per-target counts.\n\nCompared to the previous benchmarks, it has well-designed data processing workflow, leaderboard, and extensive comparison.", "questions": "- **Ground truth in CD is geometry-only**. CD \"truth\" is defined by transferring the ligand after receptor alignment; there is no check that interaction patterns are conserved. Did authors consider any kinds of interaction pattern comparison such as interaction-fingerprint between co-crystal structure and CD structures?\n- **Data-processing thresholds need justification**. How did authors choose the criteria of 0.2A in intermolecular distances in selection step (Table S1,2)?\n- **How did authors run DynamicBind?** Although DynamicBind needs apo protein structure, authors did not clarify any information about this.\n- **Relaxation policy**. For methods that already implement internal relaxation/refinement, did you disable their internal step before applying your OpenMM relaxation, or apply both?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 2, "strengths": "- **Timely scope & coverage**. Large-scale comparison over 23 methods spanning physics-based, AI docking, and AI co-folding models; inclusion of commercial tools improves external validity. Also, the authors used dataset with PDBs deposited after 2022.\n- **Cross-docking emphasis**. Clearly motivates CD as the practical setting and provides a concrete construction.", "weaknesses": "- **Pocket-conditioning fairness**. CD results show pocket-conditioned AI docking models (e.g., SurfDock, UMD-V2, DiffDock-Pocket) against co-folding models that do not receive an explicit pocket mask. However, as pocket information is already known as very important information in predicting binding structure, I think comparing co-folding models' structure prediction performance (Figure 2) without pocket information with AI docking models are not a fair comparison. Furthermore, Pocket-based methods operate inside a pre-specified cavity/box, so even when the true pocket is dissimilar (apo$\\to$holo shift, induced fit), their search space, and thus the worst-case ligand RMSD, is tightly bounded by the pocket box. Co-folding models, by contrast, must first localize the site over the entire protein surface/volume and then place the ligand, facing an orders-of-magnitude larger search space and unbounded RMSD penalties for site mislocalization. Consequently, direct RMSD comparisons systematically favor pocket-conditioned docking, and are not information-matched or fair.\n\n- **Missing confidence interval**. The paper reports overall means (and references Table S4/Fig. 2), but it does not present thorough confidence intervals, per-target bootstraps, or seed-sensitivity analyses; important because many methods sample multiple poses and CD averages at the target level. It is therefore hard to tell whether SD$\\leftrightarrow$CD deltas are robust to sampling/seed counts and the number of receptor conformers considered.\n\n- **Per-method relaxation settings unclear**. While the OpenMM relaxation is described, it is not fully explicit whether built-in refinements for methods that include their own relaxation were disabled or double-applied, which can tilt PB-Valid and RMSD outcomes. Appendix B lists settings but the policy for overriding internal refinement isn’t crisply stated.\n\n- **Novelty of relaxation module**. The introduction of PB-Valid and OpenMM-based relaxation is a helpful addition, but it is not clear that these components represent a substantial methodological contribution. They appear to extend existing evaluation or refinement tools rather than introduce fundamentally new ideas.", "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": "Omrz4EF7S1", "reviewer": "Reviewer_xw3v", "summary": "The paper introduces PoseX, an open-source benchmark for evaluating self-docking and cross-docking across 23 methods spanning physics-based docking, AI docking, and AI co-folding. It analyzes pocket-similarity effects and a standardized post-processing relaxation pipeline that mitigates steric clashes. On both self- and cross-docking, several AI methods outperform physics-based tools on top-1 success. Relaxation often improves PB-Valid while not fixing chirality. A public leaderboard is announced.", "questions": "Please refer to the above weaknesses.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "+ The scope is important. The benchmark emphasizes cross-docking, which better reflects real-world use than self-docking alone, and covers 23 diverse methods.\n+ The evaluation is informed. Success combines RMSD ($<2 \\AA$) with PB-Valid to penalize chemically implausible poses, and analyses of relaxation and chirality are informative.\n+ A leaderboard and an automated relaxation pipeline (e.g., OpenMM-based) improve accessibility and reproducibility.", "weaknesses": "- It is unclear whether relaxation and other post-processing steps are applied consistently and fairly across method categories, since many\n  physics tools already perform internal minimization. Please list which methods received additional OpenMM relaxation and justify comparability.\n- The current success definition is top-1 only and couples RMSD $< 2 \\AA$ with PB-Valid. Reporting top-$k$ (e.g., $k = 5,10$) pose-ranking metrics, and separate PB-Valid vs. RMSD breakdowns with confidence intervals would better reflect screening use and decouple geometry from plausibility.\n- Pocket-similarity is computed against pre-2022 pockets to avoid training overlap, but more detail is needed to ensure ligand- or\n  pocket-level leakage is not reintroduced. Please provide formal leakage checks and family-level analyses.", "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": "w1NfdjLxwX", "reviewer": "Reviewer_yfAx", "summary": "The authors introduce PoseX, a deep learning benchmark for protein-ligand self and cross-docking. The benchmark is well built and comprehensive in terms of method selection, and empirical results match up to expectations. Nonetheless, some concerns remain regarding the novelty and presentation of these results in the context of a machine learning conference.", "questions": "1. How extensible is PoseX's codebase? For example, how hard is it for current users to build on top of the benchmark to add new datasets or prediction tasks for evaluation? Do you have any case studies in this direction?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 2, "strengths": "1. The authors' proposed benchmark is comprehensive and introduces results for protein-ligand cross-docking.\n2. Metrics reported for the benchmark are standardized and informative.\n3. The results of the benchmark match expectations overall, offering a few subtle insights, e.g., about how to improve ligand chirality prediction.\n4. The presence of an online leaderboard is a plus.", "weaknesses": "1. The benchmark, at first glance, reads like an expanded version of PoseBench extended to protein-ligand cross-docking. For example, in Line 149, the authors highlight post-prediction relaxation as a contribution of their work, though a version of this technique was already heavily explored in PoseBench (with support for multi-ligands as well).\n2. Introducing cross-docking seems like a marginal change over previous works. It is simply a matter of selecting different protein conformations. From a biological perspective, it is interesting to study, but from a machine learning perspective, it is practically identical to what has been done before.\n3. The way the authors present their benchmarks' results (as well constructed as they are) is ideal for a scientific journal but less common for a machine learning venue. For example, with the font size and scale of Figure 2, it is hard to quickly discern which machine learning method performs \"best\" in the self and cross-docking contexts (with all caveats in mind). Maybe highlighting the \"most notable\" results with a box around them would be a way to address this? Also, the methods in Figure 2 are unorganized (i.e., without category labels), so it is hard for a machine learning person (without expertise) to know which method is a model based purely on deep learning and which is a physics-based method.\n4. Why did the authors select Astex Diverse as a benchmark dataset instead of the PoseBusters Benchmark dataset (which is arguably more well-known now and less prone to show up in each method's training set)?", "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)