FIGRDock: Fast Interaction-Guided Regression for Flexible Docking
Authors: Shikun Feng
Bicheng Lin
Yuanhuan Mo
Yuyan Ni
Wenyu Zhu
Bowen Gao
Wei-Ying Ma
haitao li
Yanyan Lan
Venue: NeurIPS 2025 poster ()
Keywords: Flexible Docking
Protein-ligand interactions
Primary area: machine_learning_for_sciences

Abstract: Flexible docking, which predicts the binding conformations of both proteins and small molecules by modeling their structural flexibility, plays a vital role in structure-based drug design. Although recent generative approaches, particularly diffusion-based models, have shown promising results, they require iterative sampling to generate candidate structures and depend on separate scoring functions for pose selection. This leads to an inefficient pipeline that is difficult to scale in real-world drug discovery workflows. To overcome these challenges, we introduce FIGRDock, a fast and accurate flexible docking framework that understands complicated interactions between molecules and proteins with a regression-based approach. FIGRDock leverages initial docking poses from conventional tools to distill interaction-aware distance patterns, which serve as explicit structural conditions to directly guide the prediction of the final protein-ligand complex via a regression model. This one-shot inference paradigm enables rapid and precise pose prediction without reliance on multi-step sampling or external scoring stages. Experimental results show that FIGRDock achieves up to 100× faster inference than diffusion-based docking methods, while consistently surpassing them in accuracy across standard benchmarks. These results suggest that FIGRDock has the potential to offer a scalable and efficient solution for flexible docking, advancing the pace of structure-based drug discovery.

FIGRDock: Fast Interaction-Guided Regression for Flexible Docking
Shikun Feng 2 Bicheng Lin Yuanhuan Mo Yuyan Ni Wenyu Zhu', Bowen Gao',
Wei-Ying Ma', Haitao Li Yanyan Lan 10.77
Institute for Al Industry Research (AIR), Tsinghua University, Beijing, China *Zhongguancun Institute of Artificial Intelligence, China School of Basic Medical Sciences, Tsinghua University, China
4 School of Software Engincering, South China University of Technology
AAcademy of Mathematics and Systems Science, Chinese Academy of Sciences Beijing Frontier Research Center for Biological Structure, Tsinghua University, Beijing, China Beijing Academy of Artificial Intelligence, Beijing, China
Abstract
Flexible docking, which predicts the binding conformations of both proteins and small molecules by modeling their structural flexibility, plays a vital role in structure- drug design. Although recent generative approaches, particu- larly diffusion- -aased models, have shown promising results, they require iterative sampling to generate candidate structures and depend on separate scoring functions for pose selection. This leads an inefficient pipeline that difficult to seale real-world drug discovery workflows. To overcome these challenges, we intro- duce FIGRDock, fast and accurate flexible docking framework that understands complicated interactions between molecules and proteins with regression-basee approach. FIGRDock leverages initial docking poses from conventional tools distill interaction-awant distance patterns, which serve as explicit structural conditions to directly guide the prediction the final protein-ligand complex via regression model. This one-shot inference paradigm enables rapid and precise pose prediction without reliance on multi-step sampling or external scoring stages. Experimental results show that FIGRDock achieves up 100x faster inference than diffusion- based docking methods, while consistently surpassing them accu- racy across standard benchmarks. These results suggest that FIGRDock has the potential to offer scalable and efficient solution for flexible docking, advancing the pace of structure-based drug discovery'
Introduction
Molecular docking refers to predicting the three-dimensional structure of a protein-ligand complex given the individual structures of the protein and the small molecule. This task is fundamental to structure-basse drug discovery, as it enables large-scale screening and mechanistic understand- ing of molecular interactions that underlie pharmacological effects. While conventional docking methods typically assume a rigid protein conformation, ficxible docking models the conformational adjustments of both the ligand and the protein, especially those arising from induced-fn fit effects. By capturing this dynamic binding process, flexible docking provides more biologically realistic framework, though also introduces significant computational and modeling complexity.
'Equal contribution.
Work was done while Bicheng Lin and Yuanhuan Mo were research interns at AIR. i *Correspondence to: Yanyan Lan Clanyanyan@airttsinnguuua "The code is open-sourced in httppp///gggiia..cmm///gggggagap
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Conditional L earning
Fine-tuning
GLoe visk DUL1
computational complex data
experimental complex data
apo pocket
Conditional Encoder
Regression -aaeed Docking Module
apo ligand
coarse
pair representation
final complex
Figure Overview of FIGRDock.. The model mainly comprises two modules: conditional encoder and regression-based docking module. The conditional encoder, which is pre-trained using computational complex data, aims at providing a coarse pair representation. The regression-bess docking module, fine-tuned on more accurate experimental complex data, is tailored to conduct flexible docking with efficiency and accuracy under the guidance of conditional pair representation.
Deep learning has recently brought significant advances to the flexible docking problem, offering data- driven alternatives to traditional physics-basee based methods. Among these, two major paradigms have emerged: co- co-folding approaches that predict complex structures directly from protein sequences, and generative approaches that operate on given unbound(apo) protein and ligand structures. Co-folding models, exemplified by AlphaFold [1, achieve impressive accuracy but remain computationally intensive due to the inherent complexity of protein structure prediction. In contrast, generative methods [5) 17 & leverage diffusion models to sample ligand poses, including global translation, rotation, and torsion angles of ligand rotatable bonds and protein side chains, conditioned on apo structures. By restricting the generative process to this product space, these methods significantly reduce the dimensionality of the prediction task, and offer substantial improvements in efficiency over co-folding approaches.
Despite these advances, current generative models still face notable limitations that hinder their practical deployment. They typically rely multi-step sampling to produce accurate protein-ligand complexes and require repeated generation-scoring cycles, where performance improves with more iterations [5] [7 8 Moreover, they depend heavily pre-trained protein language models, such as ESM2 10 to provide amino acid embeddings that serve as initial node features [16]5].
In contrast to diffusion-based models, regression-based approaches have been widely adopted in rigid docking frameworks such as EquiBind [99] and TANKBind [12,, offering superior efficiency via one-shot pose prediction. However, their performance in flexible docking scenarios is often suboptimal [5], as one-shot inference struggles to capture induced-fit effects and conformational changes in the ligand protein. This gap raises compelling question: Can we design regression- based docking framework that retains the efficiency of one-shot prediction while achieving high accuracy under flexible docking scenarios?
To the best of our knowledge, accurate interaction modeling is essential to realize the full potential of regression-based docking. Unlike generative approaches, which refine predictions through multiple iterations, regression models infer binding conformations a single pass. This one-shot approach demands precise interaction modeling, as there is no iterative correction process like in generative methods. In flcxible docking scenarios, where even subtle conformational adjustments are critical, any misrepresentation of interactions can lead significant deviations in predicted binding poses.
Building on this insight, we propose Fast Interaction- Guided Regression for Docking (FIGRDock). This method directly regresses to an accurate docking complex structure through single network inference, guided by interaction representations, enabling both higher precision and greater efficiency in the docking process. FIGRDock's 's training is organized into two stages, as illustrated in Figure [] The first stage involves conditional pair representation learning. We leverage the SIU dataset [ which contains substantial amount of synthetic computational complex data generated by docking software, as pre-training data to learn ineractiioninformen paired representations between the protein and ligand. Despite the lower precision compared to crystallographic data, computational structures

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compensate for the limited availability of experimental structures. In the second stage, this learned pair representation is used as ipput for the regression-bast based docking module, followed by fine-tuning on more accurate crystal complex structures. The regression approach requires only single network inference to predict the structure. Guided by the interaction pair representation, it produces more accurate predicted structures than iterative generative methods.
Experimental results show that when compared to generative methods, FIGRDock reduces inference time from the order of tens of seconds to hundreds of milliseconds-a nearly 100x speedup. Further- more, by leveraging pair representations as conditions, FIGRDock achieves superior performance across both holo and apo input test scenarios. To the best of our knowledge, this is the first regression- based method achieve comparable or even better performance than diffusion-basee methods. In the context of the dominance of generative models in flexible docking, our work offers a promising alternative approach that could provide valuable insights and solutions for future research in the field.
2
Related work
In this section, we briefly review related work on flexible docking, focusing two main approaches: co-folding methods and diffusion-based generative models.
2.1 Co-folding Methods
Co-folding methods aim to predict the three-dimensional structure of protein- ligand complexes in an end-to-end fashion. These approaches take as input a protein sequence and a molecular representation of the ligand, typically in the form of molecular graph SMILES string, and directly output the bound complex structure. Recent advances such as NeuralPLexer [8], Umol (2), AlphaFold3 [I]. and HelixFold3 [II] have demonstrated the effectiveness of this paradigm, achieving impressive accuracy in modeling protein- interactions from minimal input information. However, the high computational demands of training and inference in these models pose significant challenges, limiting their scalability and practicality for large-scale virtual screening applications.
2.2 Diffusion-based Generative Models
Diffusion-based generative models have emerged as a leading paradigm for flexible docking. These methods take as input the unbound (apo) structures of both the protein and ligand and generate the bound complex structure by modeling the joint conformational changes that occur upon binding. Instead of searching over large configuration spaces or simulating the full folding process from the sequence, these models leverage generative diffusion processes sample binding poses in data-driven manner. Representative methods such as DiftDock-Por -Pocktt (17), DifiBBnnFR [25], and g use diffusion or diffusion-bridgs frameworks to capture pocket side-chain flexibility. [3] further incorporate backbone flexibility using techniques such as unbalanced flow matching and geometric diffusion. While these approaches have shown strong accuracy in modeling flexible binding, they often suffer from inefficiencies due to iterative sampling and dependence on external scoring functions for pose selection.
Re-Dock
FlexDock and DynamicBind
Recently, there have been several initial attempts to alleviate the inefficiency problem. A representative example FABFlex (23). which directly predicts protein-ligand conformation with a regression model. Unlike diffuuion-bbl baasusdoon-bsedd methods that rely on iterative sampling, regression models aim to directly predict the final bound structure
single forward pass, offering significantly improved inference efficiency. FABFlex, which takes the apo ligand and protein backbone input and regresses the ligand pose along with the C. coordinates of binding site residues. While this approach greatly reduces computational cost, its accuracy still lags behind state-of-the-ae diffusion- Moreover, because it does not explicitly model side-chain flexibility, where much of the binding- induced conformational change occurs, its ability to capture fine-grained interactions remains limited. These limitations motivate the development of more accurate and interaction -ware regression- approaches, such our proposed FIGRDock.
models.
3

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Pocket Encoder Pre-training
3D position recovery
random rotate sidechains torsion angles
Gaussian noise to all atoms
original pocket
0p Eacoder
f MLL
pocket atoms coordinate
(b) Conditional Pre-training
protein-ligund distance map prediction
MLP
perturb sidechain torsion angles
distancee map
Molecular databases
Protein
Bank
0g Encoder
Contact Module 9,
Docking Software
0:
Molecule Encuder
perturb ligand
puir representations
torsion ungles
(c) Fine-tuning
pocket node embedding pl phys
Pocket Coardisute
Head
ligand node embedding
Jr Lggand Coordinate Head
final complex
pocket A
Conditional Encoder
Regression- Ducking Module
Ligand Intra-atomic Distance Map Head
pairwise embedding
apo ligand
Map
Puckst-lipand Interotimii Distance Map Head
distance mapps
Figure 2: Illustrations of FIGRDock consist of three components. Apply combination of noise to the pocket, including perturbed dihedral angles and coordinates, then denoise it to train pocket encoder that is aware of side chains. b: Use coarse structures generated by docking software to learn conditional pair representation. Fine-tune on accurate crystal complex structures, using the coarse conditional pair representation to guide the regression- docking module during the fine-tuning process.
FIGRDock
In this section, we present our proposed method, FIGRDock. We begin by introducing the notations and formalizing the flexible docking task. We then describe the two key components of FIGRDock: (1) the conditional pair representation learning module, which captures interaction features between the protein and ligand, and (2) the regression-based docking module, which directly predicts the bound complex structure in a single forward pass.
3.1 Preliminaries and Problem Formalization
A protein-lignne complex be represented
(V, where V represents the set of atom types v; for the vertices i, and represents the set coordinates z; for each vertex. The complex can be divided into two parts: ligand and protein. The ligand part is represented G' = (V¹ X'). and the protein part is represented as GP = (V',P') consistent with those defined by the periodic table (e.g., C, N, O...). However, for the pocket atom types, to model information about side-chain variations, we treat the same element at different positions the side chain and backbone different types. For instance, for the element Carbon (C), we distinguish between C (backbone carbonyl), CA (alpha carbon), CB (beta carbon), etc. (Refer to the Appendix for the complete list of side-chain atom types).
XP). : The atom types for the small molecule (ligand)
In the flexible docking setting, the goal is to predict the structural changes of both the protein and the small molecule that occur during the binding process. Given the unbound apo structures of the ligand and the protein, represented g¹" (V' x( and GP" (VP, x²") respectively, the task is to

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predict their bound conformation: G' = (V*, for the ligand and g (VD, 2P) ) for the protein. This requires modeling the mutual conformational adjustments that occur upon binding, making the task significantly more complex than rigid docking.
3.2 Conditional Pair Representation Learning
Figure 2a illustrates the process for learning conditional pair representations. Initially, the protein pocket and the ligand are processed by two separate pre-trained encoders to obtain initial node representations: hp = Pp(GP) ) and h = (((g")) Here, Bg and 0 denote the encoders for the pocket and the molecule. respectively. For the ligand encoder 8,, adopt the pre- pre-trained molecular encoder from Uni-Mol [24).
To pre-train the pocket encoder lz. we design a side-chain denoising task using pocket data provided by ProFSA [6. Specifically, we apply a combined noise scheme the original pocket g": first, we perturb its rotatable dihedral angles, and second, we add Gaussian noise to the coordinates of all its atoms. This process yields the noised pocket GP. The learning objective for the pocket encoder pre-training can be represented as:
L = Eg ea (f (el_gg=gll1//000010
(20 x2
Among them, X and X represent the coordinates of G and G respectively, and fp represents an MLP (Multi-Layer Perceptron) structure, which is used to predict the coordinate noise of the pocket from the pocket representation.
Subsequently, using the initial node representations hy and hp obtained from the separate encoders, we learn the conditional pair representation utilizing complex data from SIU 7 (generated by docking software calculations). Specifically, for this stage, perturb the rotatable dihedral angles within the ligand and the side chains of the pocket in the complex data. This generates noised, approximately apo-like conformations denoted as G (ligand) and G (pocket), Let D represent the distance matrix of the holo pocket structure and the small molecule structure within the complex. As shown in Figure 20. the purpose of the interaction network module 0g is to take the noisy conformations G and G' as inputs, and learn the conditional pair representation by predicting Dpt Specifically, the loss function can be defined as:
C. = gi gf (k(p) Ppell..
Wheref; represents the MLP utilized for predicting the distance matrix, and has = (,(0 (GP)) represents the conditional pair representation learned by network 9, During the training of 0,, the parameters of f, and 6 are kept frozen.
3.3 Regression- based Docking Module
As shown in Figure the regression-based docking module 0, takes the unbound (apo) structures G and GP along with the learned pair representation hpt. inputs to predict the bound (holo) complex structures G and g".
To enable direct coordinate regression while capturing both intra- and inter-molecular structural constraints, we construct three distance matrices:
"Di: the intra-ligand atomic distance matrix,
. DDp: the intra- intra-protein atomic distance matrix,
Dpt: the mnter-molecular atomic distance matrix between ligand and protein atoms.
These matrices are computed from the predicted coordinates and serve as targets in our training objective. Specifically, the coordinate prediction loss for the ligand is defined by comparing the predicted intra- intra-ligand atomic distances with the ground truth, encouraging the model to preserve realistic molecular geometry.
Liggand Egr', go (Il)ie(hi) (x' - x² 11l (If(((hw) - Dr 1.))
(3)
5

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Similarly, the coordinate prediction loss for the pocket can be expressed as follows to enforce physically realistic geometry within the binding pocket:
Spocke Riger "g" pll/pe (hp) - (x x" )12 + (1)pa(hp))) - Dpl
(4)
Lastly, the following loss is defined to penalize deviations between the predicted and ground-truth mnter-molecular atomic distance matrix across the protein-ligand interface:
Linter face = Eeg= .ge" (IlSnta (hp) Pwll)).
In the above losses, hu, hpp hN (G" G htpp) denotes the resulting node embedding of ligand, node embedding of pocket and pairwise embedding encoded by 0,, respectively. The fie- fid. fpe- fpd. and fnid represent the head network for the prediction of the coordinate matrix and the distance.
The total regression docking loss is the sum of these three components:
f Lligand + poocket + Linter face.
Experiments
4.1 Main Experiments
Experimental Setup For pocket encoder pre-training, we use pocket data provided by ProFSA (6] to perform sidechain-aware pre-training. Since the noisc-adding process involves perturbing sidechain torsions, we filtered the dataset to remove samples with incomplete sidechains, reducing the total number of samples from million 4.8 million. The pre-training epochs with a batch size of 64, taking approximately 6 days to complete.
conducted GPUs for 10
For conditional pre-training, we pre-train on the SIU [7] dataset, which consists of 5.34 million complex conformations generated by docking software. The training was conducted using A100 GPUs with batch size of 16, and the pre-training took approximately 20 days to complete.
In the fine-tuning stage, we fine-tune FIGRDock on the commonly adopted PDBbind v2020 (2taset(22). which contains 19K crystal complex structures. We employ the time-split of PDB- bind with 17k complexes from 2018 or earlier for training and validation, and 363 test structures from 2019, ensuring consistency with previous works 19 4 The input apo ligand conformation generated using RDKit with random seed, while the input apo protein structure predicted by ESMFold [10]. The fine-tuning is performed on 4 A100 GPUs for 100 epochs with batch size of 16, taking approximately days to complete. Detailed hyperparameters can be found in the Appendix 6. 6
Evaluation Metric We evaluate FIGRDock on the PDBbind test and the PoseBusters V2 3 test set. The PoseBusters V2 Benchmark is a curated collection of 308 high-quality, drug-like protein-ligand crystal complexes released after 2021, specifically designed assess docking methods not only in terms of RMSD but also based on chemical and geometric plausibility through RDKit- based quality checks. The primary evaluation metric the RMSD of Cartesian coordinates. We report the percentage of samples with RMSD below different thresholds, specifically 2Å and 5Á for ligands, along with the median RMSD value across all samples. We also report the average runtime to evaluate the model's efficiency. Finally, for the PoseBusters benchmark, we report the Valid score, which reflects the model's ability to generate chemically and structurally reasonable conformations.
Baselines For the PDBbind benchmark, compare FIGRDock with cearch-based models SMINA 9 and GNINA [4]. which are traditional methods employing scoring functions and search algorithms to effectively explore ligand poses considerable computational cost. We also compare FIGRDock with generation model- based pocket-leve docking methods, DiffDock-Pocker Pocket [17], ReDock [8] and Flex (4) For the PoseBusters V2 benchmark, we measure FIGRDock against search-based models GOLD [21 and VINA 20), generation model-based Flex [4) and co-folding models UMol 2 and AlphaFold3 1
4.1.1 PDBbind
As shown in Table T we compare FoGR's performance and runtime with search-based models SMINA [9] and GNINA [4], sidechain flexible models DiffDocc--Pcket [17] and ReDock (8).

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Table 1: RMSD performance and runtime comparison of different methods on the PDBbind dataset.
The best results are highlighted in bold. FIGRDock demonstrates a significant advantage both accuracy and efficiency.
Holo Crystal Proteins
Apo ESMFold Proteins
Average Runtime (s)
Models
%<2 f
%<5
Med.
%x21
%<5
Med.y
(riid(
32.5
54.7
4.5
6.6
22.5
7.7
258
SMINA
19.8
47.9
5.4
3.6
20.5
7.3
1914
GriNA(rigid)
42.7
67.0
2.5
9.7
33.6
7.5
260
GNINA
27.8
54.4
4.6
6.6
28.0
7.2
1575
DiffDock -oockeec)))))))(
49.8
79.8
2.0
41.7
74.9
2.6
61
ReDock(40)
53.9
80.3
42.9
76.4
2.4
58
FlexDock
39.7
2.5
11
FIGRDock
57.2
82.3
1.6
46.6
76.8
2.3
0.4
and all-atom flexible model Flex 4 We report results for both rigid and flexible versions of SMINA and GNINA. Overall, FIGRDock outperforms existing methods in both accuracy and inference efficiency. terms of RMSD performance, metric %RMSD<2Á is a crucial metric as the predicted structure is considered successful when it meets this criterion. FlexDock 4 is the only generative model that has modeled all atoms, making the most equitable model for comparison. FIGRDock significantly outperformed FlexDock 4 in metric %RMSD<2Á by nearly 7% (46.6% vs. 39.7%) with apo input. Furthermore, FIGRDock improves upon the previous best rest-performing method, ReDock [8] in metric %RMSD<2A, by nearly 4% with both holo and apo input (57.2% vs. 53.9% and 44.6% vs. 42.9%). the same time, in terms of model efficiency, FIGRDock significantly accelerates inference compared to ReDock (8), achieving over a 100-fold speedup (0.4s vs. 58s). This demonstrates that under the guidance of conditional pair embeddings, the regression-basee module, which avoids repetitive sampling and iterative reasoning, not only substantially enhances efficiency but also maintains state-of-ther oh-the-art accuracy.
4.1.2 PoseBusters
On PoseBusters, shown in Figure 3 rigid docking methods including DeepDock 11], Uni- Mol [24], GOLD 21 and VINA 20 receive holo pockets as input. While flexible docking methods, apo input generated by ESMFold. We also report the result that FIGRDock uses holo as input. Finally, co-folding methods like Umol [2] and AlphaFold3 [ take sequences input. FIGRDock performs significantly better than Flex [4] as well other
including Foek and FIGRDock
deep learning based rigid docking models like DeepDock [13] and Uni-Mol 24 Compared to search-based methods like GOLD [2I] and VINA (20), although FIGRDock with apo input falls slightly behind, it is much faster and takes on a prominently harder task. FIGRDock achieves better performance than GOLD and Vina with holo input. AlphaFold3 MI significantly outperformed all methods, as it is trained using a larger volume of data. FIGRDock can generate more physically plausible conformations, achieving 99.5% and 96.7% PB Valid for apo and holo input. Details of validity checks for the PoseBusters V2 benchmark are deferred to Appendix 6.4
4.2
Ablation study
4.2.1
Comparison with ESM embedding
In this study, we compare our approach with the traditional method that uses protein language model-generated embeddings conditions, reported in Table 2 'Without condition' refers to the model trained without any conditional pre-training, while "With ESM' denotes the use of amino acid-level node embeddings extracted from ESM2 [IO]. Our method, FIGRDock, employs the proposed conditional pair representation. All settings use the same network architecture and fine-tuning strategy ensure fair comparison.
Results shown in Table 2 demonstrate that our interaction sererddtionguidedd approach provides substantial bene- fits for molecular docking. First, the significant performance improvement of our method over the
7

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69.8
60
54.8
47.5
84.4
67.5
54.5
55
DeepBook
UniMol
Umol
Reetack
RGRDosklape)
GOLD
ViNA
RORDock(halo))
AlphaFold3
<
apo
sequence
Figure 3: Results of the PoseBusters V2 benchmark with known pockets. FIGRDock outperforms the flexible docking method FlexDock with apo input. Meanwhile, FIGRDock outperforms search-based methods (Gold and Vina) with holo input. For methods marked with *, we demonstrate results reported by the FlexDock paper 4
Table 2: RMSD performance comparison of different protein representations for docking. "Without condition' uses no conditional pre-training; 'With ESM uses ESM2-based residue level node embeddings; FIGRDock employs interaction-awar conditional representations. The best results in bold, showing the advantage of our approach over general protein features.
Holo Crystal Proteins
Apo ESMFold Proteins
Models
%<2 f
Med.j
%<21 f
Med.
Without condition
46.2
2.2
37.8
2.8
With ESM
49.0
2.0
37.8
2.7
FIGRDock
57.2
1.6
46.6
2.3
unguided baseline validates the effectiveness of our conditional learning strategy. Moreover, FIGR- Dock consistently outperforms the 'With ESM setting across both holo and apo inputs especially espeillesye for apo ESMFold proteins, where the success rate (%RMSD<2A) increases by nearly 9%. This performance gain is particularly notable considering that our approach incurs significantly lower training costs than ESM2. These findings suggest that representations capturing interaction-speeific knowledge offer more relevant and efficient guidance for docking tasks compared to general-purpose protein representations.
4.2.2 Impact of Apo Structure Prediction Methods on Docking Performance
Table 3: RMSD performance of FIGRDock models trained apo structures predicted by different folding methods (AlphaFold2 vs. ESMFold), evaluated on both AlphaFold2- and ESMFolddpredicted apo test sets.
Apo AlphaFold2 Proteins
Apo ESMFold Proteins
Models
%<2 1
Med.j
%<2 f
Med.j
FIGRDock (Training by AlphaFold2)
47.5
2.1
36.7
2.6
FIGRDock(Training by ESMFold)
48.1
2.1
46.6
2.3
Apo protein conformations can be predicted using either ESMFold AlphaFold2, both of which generate 3D protein structures from amino acid sequences. However, prediction accuracy varies

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between methods, and few studies have explored how different predicted apo structures influence downstream docking performance. Here, we evaluate the robustness of our model when provided with apo structures predicted by different folding algorithms. This experiment is critical to determine whether our model's 's performance depends on specific conformational inputs.
To this end, we constructed two dataset variants for training and evaluation. In addition to the main experimental setup using ESMFold, we created variant based on AlphaFold2-prediceee apo structures. The data processing and split strategy follows the same protocol as FABFlex [23]. We trained two models separately using apo structures predicted by ESMFold and AlphaFold2, and evaluated each model on both ESMFold- and AlphaFold2 predicted test sets. Results are shown in 3
We observe that when the training and testing apo structures come from the same folding method, FIGRDock performs well in both cases. Interestingly, the model trained on ESMFold data generalizes well to AlphaFold-predicteee test structures. In contrast, the model trained on AlphaFold2 data performs poorly on the prreeeedpredicteed test set. We hypothesize that this due to AlphaFold2's higher prediction accuracy, which may result in less noisy apo conformations in the training set, thereby limiting the model's ability to generalize noisier samples in the ESMFold test set.
4.2.3 Scaling Study of Pre-training Data
In this section, we investigate how the scale of pre-training data influences model performance by varying the dataset size used for conditional pre-training, ranging from 0 (as noted earlier, this corresponds to the 'Without condition' setting in Table i.e., no conditional pre-training) to 5 million samples (the full SIU 77 dataset). Figure degonsaseesssssalees positive correlation between the scale of pre-traine pre-training data and docking performance across all evaluation metrics, under both apo and holo test scenarios. This consistent improvement with increasing data scale validates the effectiveness and flexibility of our framework, as well as its potential to benefit from even larger datasets. Due to computational constraints, we currently report full experiments only on the 5-million sample setting. Future work can explore larger-scale datasets to further enhance performance.
Docking Performance on Holo Crystal Proteins
Docking Performance on Apo ESMFold Proteins
60.0
1.5
50.0
2.2
RRMSDD 2
$5.0
RMSDDD A
45.0
52.5
1.8
42.5
2.5
30.0
1.9
40.0
2.6
Mediun
45.0
35.0
42.5
2.2
32.5
2.9
40.0
2.3
30,0
3.0
10m
160m
500w
18w Pre-training Scale
100m
500w
Pre-training
Figure 4: Comparison of docking performance with different scales of pre-training data. Larger pre-training datasets lead to better performance across both holo (left) and apo (right) settings.
Conclusion
In this work, we present FIGRDock, fast and accurate regression-based framework for flexible molecular docking. Unlike mainstream generative methods that rely on repetitive sampling, scoring, and pre-trained protein embeddings. FIGRDock adopts an interaction-awan conditional representa- tion to guide direct regression of pootein-ligane complex structures. By decoupling the learning of interaction patterns from the final docking prediction, FIGRDock achieves high docking accuracy with single forward pass, significantly improving inference efficiency. Extensive experiments on both holo and apo settings demonstrate that FIGRDock not only outperforms previous diffusion- based models in accuracy but also achieves nearly 100x faster inference. These results highlight the promise of regression-bast based docking under interaction guided supervision and open new directions for efficient and scalable structure-bes based drug design.

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Acknowledgments and Disclosure of Funding
This work is supported by Beijing Academy of Artilicial Intelligence and Beijing Frontier Research Center for Biological Structure Fundings.
References
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6 Technical Appendices and Supplementary Material
6.1
Implementation Details
The training of FIGRDock consists three stages: Pocket encoder pre- rre-training conditional pre- training, and fine-tuning. The hyperparameter settings used in all stages listed in Table
Table 4: Hyperparameter settings for Pocket Pretraining, Conditional Pretraining, and Fine-tuning stages.
Pocket Pretraining
Conditional Pretraining
Fine-tuning
Batch Size
64
16
16
Training Epochs
10
12
100
Learning Rate
x 10 10-4
3 x 10
3 x 10
LR Scheduler
volynomial_dest
pol-ymoiial_
polymomial__ecas
Warmup Ratio
0.01
0.06
0.06
Optimizer
Adam
Adam
Adam
Weight Decay
1 10
0
0
GPU Number
4
4
4
For pocket encoding, to enhance the model's sensitivity to side-chain variations during the docking process, we treat atoms with the same elemental type but different structural roles, such as backhone versus side-chain atoms, distinct atom types. In particular, for side-chain atoms, we define comprehensive
atom types capture their structural specificity, including:
C, CA, CB, CD, CD1, CD2, CE, CE1, CE2, CE3, CG, CG1, CG2, CH2, CZ, CZ2, CZ3, N, ND1, ND2, NE, NE1, NE2, NH1, NH2, NZ, 0, OD1, OD2, 0E1, DE2, DG, DG1, OH, SD, SE
We employ a Transformer- based architecture to encode molecular and protein structures. Specifically, the encoding schemes for atom types and 3D positions, along with the design of the Transformer layers, are adopted from Uni-Mol[24]
6.2 Evaluating Model Generalization Beyond Structural Memorization
The docking structures used in pre-training provide only coarse carse-grained structural information. Initially, we did not consider their similarity to the test set in experiments. To further investigate whether the model demonstrates true generalization ability rather than memorizing recurring patterns between the training and test data, analyzed the structural similarity between the SIU pre-training set and the PDBbind test set.
To minimize potential data leakage, removed all training samples whose structural similarity to any test sample exceeded 0.5. After this filtering, the training set retained 5,018,392 entries. We then repeated the pre-training and fine-tuning procedures using this filtered dataset.
Table 5: RMSD comparison of FIGRDock models trained with and without structural-similarity filtering.
Holo Crystal Proteins
Apo ESMFold Proteins
Models
%<2 f
Med.
%<2
Med.
FIGR FIGRDock (similarity-fiteree)
57.2
1.6
46.6
2.3
57.2
1.7
45.7
2.2
As shown in Table 5 after removing highly similar structures from the training data, FIGRDock maintains nearly identical performance compared to the original model. Although the proportion of predictions with RMSD < Á slightly declines on the apo test set, the model still achieves competitive results and substantially outperforms all baselines. This result indicates that the model has indeed learned transferable and generalizable representations, rather than simply memorizing structural patterns seen during training.
12

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6.3 Visualized Examples
6cjj, L-RMSD: 0.91, P-RMSD: 0.97 AH-RMSD: 1.36
6uwy L-RMSD: 0.94, P-RMSD: 0.98 AH-RMSD: 1.29
6nv7, L-RMSD: 1.30, P-RMSD: 0.81 AH-RMSD: 1.10
6oxp, L-RMSD: 1.05, P-RMSD: 0.67 AH-RMSD: 0.84
6gzy, L-RMSD: 3.74, P-RMSD: 4.95
6e6v, L-RMSD: 4.11, P-RMSD: 5.20
predicted pocket
predicted ligand true ligand
holo pocket
Figure 5: Visualized examples of complexes 6cjj. 6uwv, 6nv7, 6oxp, 6gzy and 6e6v in PDBbind test dataset. L-RMSD measures the RMSD between the predicted ligand and the ground-truth ligand. P-RMSD denotes the RMSD between the predicted pocket and the ground-truth holo pocket. AH-RMSD represents the RMSD between the input apo pocket and the ground-truth holo pocket.
6.4 Additional Experiment Results
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100
99
96
95
93
92
File loads
Santtization
Molecular Formula
Tetrahedral Chirality
Double Bond Stereochemistry
Bonds
Bond Lengthe
Bond Angles
Internal Steric Clash
Planar Aromatic Rings
Planar Double Bonds
Minimum Protein Ligand Distance
Minimum Distance to Organic Cofactors
Energy Ratio
Minimum Distance to Inorganic Cofactors
Volume overlap with Proteins
Volume overtap with Organic Cofactors
Volume overtap with Inorganic Cofactors
holo
apo
Figure 6: Detailed plausibility checks for predictions by FIGRDock on PoseBusters V2 benchmark with holo and apo input. FIGRDock achieves 99.5% and 96.7% PBVal for apo and holo input, generating physically reasonable conformations.
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Reviews:
[{"review_id": "4NVggJIezu", "reviewer": "Reviewer_bFKc", "summary": "This paper introduces FIGRDock, a new flexible docking framework pretrained on synthetic protein-ligand complexes and fine tuned to complexes derived from laboratory experiments.  FIGRDock takes apo pocket and ligand as inputs and uses regression to match the intra-ligand and intra-protein changes as well as the interfacial interactions between protein and ligand.  The resulting trained model is much faster and more accurate than typical diffusion-based or search-based docking methods, though less accurate than AF3 and its variants.", "questions": "Could the authors also run GOLD/VINA starting with apo structures in figure 2?  Also, why use ESMFold and not AF2 for the apo structures---is that simply a result of the analysis in paragraph 4.2.2 or were there additional considerations?\n\nCould the authors test their hypothesis in page 9 paragraph 2 about the origin of the lack of generalization for the model trained on AF2 structures by perhaps augmenting the dataset with artificially noised structures?\n\nWhat is the x-axis notation in Fig 4?", "limitations": "yes", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "This paper demonstrates that a simple, MSA-free approach of machine-learned docking can reach high performance, competitive with traditional software used in drug discovery.  This is an important recent area of research and the solution by the authors is notably faster during inference than any of the previous flexible docking approaches.  The presentation of the design of the model and its training protocol are clear.\n\nThe main weakness of this work relates to the challenge in evaluating it for its intended purpose: helping drug discovery.  Although the results on time-split datasets are useful and desirable for the sake of comparison to past approaches, it would be useful to also present different, harder data splits and, perhaps most importantly, to stratify the evaluation by similarity to the training set data, for example as done in Fig 1A,B,C of Škrinjar et al, 2025 (https://doi.org/10.1101/2025.02.03.636309).  This is critical, given that AF3 (superior to FIGRDock in accuracy though much slower) struggles with novel molecules and pockets that matter the most in practical contexts.  Addressing this generalization gap would significantly improve this work.", "was_revised": true, "final_justification": "The authors addressed my main concerns although the origin of the generalization is still not very clear.  I assume that the authors will further improve during the remaining review period and I am willing to optimistically raise my score in response.", "rebuttal": "> Response to the weakness\n\nThank you for your suggestions. We conducted an evaluation of the model's generalization ability by examining the similarity between the pre-training data and the test data. Due to the limited time available during the rebuttal period, we removed samples from the 1M pre-training dataset that have a sequence similarity greater than 50% with the test set. The results are shown in the figure below.\n\n| PDBBind (APO)                      | RMSDs < 2Å ↑ | Median RMSD ↓ |\n|-----------------------------------|--------------|----------------|\n| Without condition                 | 37.8         | 2.8            |\n| 100k                               | 40.4         | 2.5            |\n| 1M                                | 44.1         | 2.4            |\n| 1M (erase > 50% seq sim)          | 42.6         | 2.5            |\n\nWe observed that although removing similar samples leads to a slight drop in performance, the model trained on the filtered 1M dataset still outperforms both the setting pre-training with 100K dataset and the setting without pre-training. This indicates that the model maintains strong generalization ability. In future work, we will further validate these findings using the full dataset and include additional results in the paper.\n\n\n\n> GOLD/VINA starting with apo structures\n\nWe test GOLD/VINA with apo structures as inputs on PoseBusters V2 benchmark. As shown in the table below, our approach significantly surpasses traditional search-based methods, GOLD and VINA.\n| Method   | RMSD < 2Å | RMSD < 2Å & PB-valid |\n|-------------|----------|----------------------|\n| GOLD (apo inputs)    | 10.4%    | 9.4%                 |\n| VINA (apo inputs)    | 13.7%    | —                    |\n| FIGRDock (apo inputs)| 54.8%    | 54.5%                |\n\nNote: The PB-valid metric for VINA requires further validation due to unresolved technical problems; this analysis is ongoing.\n\n> why use ESMFold and not AF2 for the apo structures\n\nWe apologize if there was any confusion. The apo structures predicted by ESMFold are intentionally adopted to make a fair comparison with baselines.  The experiment in Section 4.2.2 shows that different cofolding methods used to generate apo data can lead to varying performance on downstream tasks. Previous methods typically test with ESMFold-predicted apo structures. To ensure consistency with previous work, we also employ ESMFold-predicted apo structures for testing.\n\n>  Response to hypothesis in page 9 paragraph 2\n\nThe reason we proposed this hypothesis was that the RMSD of the apo protein pockets predicted by AlphaFold2 compared to the holo form is smaller than that predicted by ESMFold. Therefore, the model finetuned with AF2 data took on an easier task and failed to generalize to noisier ESMFold generated structures. To further validate this hypothesis, we attempted to augment the structures predicted by AlphaFold2 by introducing random perturbations to pocket side-chain dihedrals during the fine-tuning process. The experimental results are as follows:\n\n| Method                                      |   Apo AlphaFold2 Proteins                   | Apo ESMFold Proteins           |\n|---------------------------------------------|-------------------------|----------------------|\n|                                             | %<2 ↑ &nbsp;&nbsp;&nbsp;&nbsp; Med.↓    | %<2 ↑ &nbsp;&nbsp;&nbsp;&nbsp; Med.↓    |\n| FIGRDock(Training by AlphaFold2)            | 47.5 &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 2.1      | 36.7 &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 2.6      |\n| FIGRDock_sc_aug(Training by AlphaFold2)     | 44.9 &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 2.2      | 34.4  &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 2.9      |\n\nAlthough the experimental results deteriorated after adding noise, we speculate that this is because the way we added noise was too crude and naive, and it could not simulate the complex low-energy conformational changes of proteins in reality. In order to dive into this problem, we plan to add more refined noise, for example sampling side-chain conformations from rotamer libraries. But due to limited time, we cannot present results during the rebuttal period. Thank you for the question you raised. We will continue to explore this issue in the future.\n\n> Response to notation in Figure 4\n\nSorry about any unclear explanation for Figure 4. The x-axis notation of Figure 4 refers to different data scales we used for pre-training, while the y-axis notation represents docking performance, which includes two types of metrics, the percentage of ligand RMSD below 2 Å and the Median RMSD. Therefore, Figure 4 depicts how good the docking performance is as the data scale increases.\n\n> Response to fonts of elements in Figure 2\n\nThanks for your valuable feedback! Due to NeurIPS policy \"Because of technical complications, we need to stop supporting the global response with PDF\", we cannot update our paper PDF immediately. And we will adjust the font sizes in Figure 2 for better readability."}, {"review_id": "F8AmqXx1hE", "reviewer": "Reviewer_4soF", "summary": "This paper proposes a regression-based method for flexible docking, pretrained on computational data and fine-tuned on crystal data. The method achieves comparable results in terms of both accuracy and efficiency.", "questions": "1.\tSection 3 would be strengthened by a more detailed explanation of the modules. Could the authors elaborate on their implementation or architecture?\n2.\tFor a fair and direct comparison, the baseline models should be trained and evaluated on the same datasets as FIGRDock. The current comparison is weakened by this discrepancy.\n3.\tFigure 1 appears to be redundant, as it repeats information already present in Figure 2. Additionally, the framework diagram should be annotated with the mathematical notations for each module (e.g., labeling the \"Ligand Coordinate Head\" with f_lc) to improve clarity and connect the diagram to the text.", "limitations": "Yes", "rating": 3, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Strengths:\n1.  This work applies a regression-based method, typically used for rigid docking, to the task of flexible docking and achieves results comparable to those of diffusion-based models, offering a new alternative for future research.\n2.  This work provides the insight that a \"one-shot approach demands precise interaction modeling\" and proposes learning this interaction through a pre-training paradigm on data generated by docking software.\n3.  This work significantly improves the efficiency of flexible docking prediction compared to diffusion-based models, making it more practical for real-world applications.\nWeaknesses:\n1.  The novelty of this work is limited, as the primary innovation is the \"Conditional Pre-training\" process, with no modifications to the prediction framework itself.\n2.  Section 3, which details the FIGRDock method, is poorly written. For example, it lacks formal mathematical equations and structural details for key modules such as the \"Contact Module\" and \"Regression-based Docking Module,\" providing only the loss functions.\n3.  The performance of FIGRDock is highly reliant on the SIU pre-training set, while the baseline models were trained only on PDBbind. This discrepancy in training data leads to an unfair comparison.", "was_revised": true, "final_justification": "Although the author has provided a detailed response, the core innovation of the paper still needs to be improved.", "rebuttal": "> Response to the novelty\n\nOur work focuses on the complex and challenging task, flexible molecular docking, providing a fast and accurate regression-based framework. FIGRDock distinguishes itself from previous methods with the following contributions:\n- New Perspective: precise interaction modeling matters. Mainstream generative methods that rely on repetitive sampling, scoring, and pre-trained protein embeddings, lack efficiency for real-world application. Prior regression-based approaches offer a more efficient way for molecular docking but often struggle to make accurate prediction with one-shot inference. FIGRDock takes into account both efficiency and accuracy by decoupling the learning of interaction patterns from the final docking prediction. \n- New Methodology: a simple yet effective pre-training and fine-tuning framework. We utilized SIU dataset, which contains enormous computational complex data, overcoming the limitation of the small number of crystal data. Further, our novel pre-training strategy, including pocket reconstruction and distance map prediction tasks, is effective for learning the interaction patterns. To the best of our knowledge, FIGRDock is the first regression-based approach solving all-atom level flexible docking problem in a pre-training and fine-tuning fashion.\n- New Performance. Experiments show FIGRDock outperforms all the baselines on the standard benchmark PDBbind (Section 4.1.1). Notably, it even achieves a nearly 9% relative improvement over the previous SOTA model ReDock. On PoseBusters benchmark (Section 4.1.2), FIGRDock performs significantly better than FlexDock as well as other deep-learning-based rigid docking models, and achieves better performance than search-based methods GOLD and Vina with holo input. Moreover, ablation study (Section 4.2.3) highlights the scalability potential of our method.\n\n\nAnd we notice that Reviewer qhP6 agrees that our work is novel, as it mentions, \"the precise understanding of protein-ligand interactions is achieved through a novel pre-training stage\" and \"pre-training a pocket-encoder for docking with a denoising task is novel\".\n\n> More details of the Method\n\nThank you for the suggestions. We illustrate more details about the network architecture here and will revisit the methodology described in Section 3 of the paper in the future.\nWe adopt a Transformer-based architecture in our framework, with the basic layer structure following Uni-Mol due to its simplicity. The input to each layer consists of node-level embeddings $ h_s $ and pair-wise embeddings $ q $.\n\nEach Transformer layer performs the following operations:\n\n (1) compute the query, key, and value vectors $Q$, $K$, $V$ for each head based on the atomic representations $h_i^l$;\n\n (2) compute attention weights with the pairwise bias $ q_{ij}^l $;\n \n(3) update the atomic representations using:\n$h_i^{l+1} = \\text{FFN}(\\text{MultiHeadAttention}(h_i^l, q_{ij}^{l}))$\nand\n (4) update the pairwise representations using:\n$ q_{ij}^{l+1} = q_{ij}^l +   \\{ \\frac{Q_i^{l,h} \\cdot (K_j^{l,h})^\\top}{\\sqrt{d}} \\mid h \\in [1, H] \\} $\n\nwhere $l$ denotes the layer index and $h$ denotes the head index among $H $ total attention heads.\nFor both the pocket and ligand encoders, the node-level embeddings are initialized based on atom types, while the pairwise-level embeddings are initialized using the Euclidean distances between atom pairs.\n\nFor the input to the contact module, the node-level embedding is the concatenated output of the ligand and pocket encoders. \n\nAs for the pairwise-level embeddings, denoted as $q^0_{\\text{PL}}$, we initialize them with zeros and then populate them with the values from $q^0_{\\text{PP}}$ and $q^0_{\\text{LL}}$ at the corresponding positions as follows:\n\n$\nq^0_{\\text{PL}}[:L_p, :L_p] = q^0_{\\text{PP}}, \\quad \nq^0_{\\text{PL}}[-L_m:, -L_m:] = q^0_{\\text{LL}}\n$\n\nHere, $L_p$ and $L_m$ denote the number of atoms in the pocket and the ligand, respectively. The updated node- and pairwise-level embeddings produced by the contact module are then used as input to the docking regression module.\n\n>  Response to redundancy of Figure 1\n\nThanks for your feedback! Instead of redundancy, we believe that Figure 1 and Figure 2 play different roles in presenting our method. Figure 1 is designed to enable readers to have a quick understanding of the flexible docking problem tackled by FIGRDock. As shown in Figure 1, FIGRDock mainly comprises two modules: a conditional encoder and a regression-based docking module. The conditional encoder is pre-trained on computational complex data to provide a interaction guidance. The regression-based docking module is fine-tuned with accurate experimental complex data to perform fast interaction-guided regression for flexible docking. When it comes to Figure 2, we dive into the implementation details of these two modules and present complete workflow of FIGRDock, starting from the pre-training strategy to the final output of different heads. Therefore, we believe that these two figures do not conflict with each other and there is no redundancy. \n\n> Response to framework diagram\n\nThank you for pointing it out. As suggested, we will add the mathematical notations for each module and ensure their consistency with the text.\n\n> Response to the pre-training of other baselines\n\nWe would like to clarify that the goal of the method proposed in our paper is to improve both the efficiency and accuracy of flexible docking. The pre-training stage learns interaction representations that, when combined with a regression-based approach, enable fast and accurate structure prediction, outperforming previous generative baselines. While it is possible to adapt generative approaches by incorporating the pre-trained representations as conditional guidance to further improve generation accuracy, this direction falls beyond the scope of the current study."}, {"review_id": "RC0HUfAjRF", "reviewer": "Reviewer_qhP6", "summary": "This paper introduces a regression-based approach, FIGRDock, for pocket-based flexible docking. Key to the success of regression-based approaches is a precise understanding of protein-ligand interactions and associated distances, which are then used by the structure module to then predict the final structure. The precise understanding of protein-ligand interactions is achieved through a novel pre-training stage, utilizing pocket-reconstruction and distance-map prediction tasks. Empirical results showcase the improved performance of FIGRDock over previous baselines.", "questions": "Questions are marked with a **Questions** identifier in the Weaknesses section.", "limitations": "Limitations are not discussed, and should be included. For example, generative models align well with the experimental uncertainty associated in the docking task, and have an implicit ability to correct their predictions. This is however missing in regression based models.", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 2, "originality": 3, "strengths_and_weaknesses": "### **Strengths**:\n\n* Accurately modelling protein flexibility in docking is an important yet understudied problem. The paper takes a step in the right direction in that regard, with a very computationally efficient method.\n\n* The submission is clearly written, well-organized, and the technical details and contributions of the paper are well-explained to the reader.\n\n* The technical contributions / choices adopted by the paper are sound, especially in the context of flexible docking. To my best knowledge, pre-training a pocket-encoder for docking with a denoising task is novel, even though similar strategies have been used for small molecule representation learning. Empirical investigations confirm the effectiveness of these choices (some caveats remain, see Weaknesses).\n\n\n### **Weaknesses / Clarifications:**\n\n**Technical Contributions:**\n\n1. The main technical contribution of the paper lies in the conditional pre-training stage, where both the pocket encoder, and pair-representation between the ligand and protein is learnt.\n\n**Question**: For the pocket encoder denoising task, do you only use a single noise scale, or is it a combination of noise scales as in diffusion models? \n\n2. For the distance-map prediction task, it appears as though a regression paradigm is used by directly predicting distances. A commonly used strategy across co-folding approaches is the distogram prediction which insteads utilizes a classification perspective.\n\n**Questions:**\n\n* Did the authors experiment with both regression and classification settings, and if yes, have any insights on when one paradigm would be preferred over the other?\n* How do the authors ensure training is stable across different distance scales in the regression setting?\n\n**Empirical Results:**\n\n1. From the ablation results, it is quite clear that the greatest improvement in performance comes from the conditional pre-training strategy (~9% improvement). This improvement emerges from one of the following reasons:\n\n* The pre-training data from ProSFA and SIU datasets contain a lot of diversity in pocket structures and binding modes, and allow the model to learn flexible interaction patterns, improving generalization.\n\n* The interaction patterns in the pre-training data largely mimic patterns seen in the test set (this is a known issue with the time-split of PDBBind), and the increased performance is from additional memorization. \n\nIn my experience, such large improvements are often from sort of memorization rather than true generalization. Given the mounting evidence of similar issues also for co-folding models such AlphaFold3 and Boltz-1 [1], my suspicion for the improved performance is from the latter reason. \n\nThis is a critical issue as the main utility of docking models is to provide fast and accurate predictions across the proteome. If the conditional pre-training stage only offers a better route to memorization, the technical contributions of the paper are immediately thin.\n\n**Question:** Is it possible to construct an overlap score similar to [1], between the 5 million poses in SIU dataset and the PDBBind split test set? The distogram of the similarity to the closest training set point should give a clear indication of whether memorization was happening.\n\n2. For flexible docking, one is also often interested in predicting accurate protein structures, given the downstream utility in binding affinity prediction.\n\n**Question:** Can the authors also compare the pocket-aligned All-Atom RMSD < 1 of their method against other baselines (wherever available)? This should help understand if the pocket-encoder pre-training endows the model with some understanding of the protein conformational space.\n\n3. The learnt interactions are only evaluated from a geometric perspective.\n\n**Question:** Can the authors also evaluate the protein-ligand interaction recovery metrics from [2] to see if key physico-chemical patterns are preserved?\n\n4. This is minor, but most docking papers claim that fast approaches are needed to support the demands of virtual screening. However, there are a few papers that showcase the effectiveness of active-learning style approaches in finding reasonable hits for virtual screening, which makes computational efficiency (in comparison to accuracy) a less of a bottleneck [3, 4]\n\nI have currently recommended the paper as Borderline Reject. If there is very little evidence for memorization from the pre-training stage, I am happy to raise my score. On the other hand, if the evidence is overwhelmingly in favor of memorization, I will keep my rating.\n\n**References:**\n\n[1]: Have protein-ligand co-folding methods moved beyond memorization?\\\n[2]: Assessing Interaction Recovery of Predicted Protein-Ligand Poses\\\n[3]: Thompson Sampling─An Efficient Method for Searching Ultralarge Synthesis on Demand Databases\\\n[4]: New Trends in Virtual Screening", "was_revised": true, "final_justification": "The authors addressed most of my concerns during the rebuttal and I'm happy to raise my score.", "rebuttal": "> For the pocket encoder denoising task, do you only use a single noise scale, or is it a combination of noise scales as in diffusion models?\n\nSorry about the confusion. We only use a single noise scale in the pocket encoder denoising task. This approach is much simpler than using a combination of noise scales as in diffusion models. Neither extra modules for time embedding nor complicated loss function are needed. Exploring the use of multiple noise scales could be an interesting direction for future work.\n\n>  Did the authors experiment with both regression and classification settings\n\nWe did not explore the classification setting and instead adopted a regression setting.  The main reason we directly adopt the regression setting is that we believe it provides more fine-grained supervision signals compared to classification.\n\n\n> How do the authors ensure training is stable across different distance scales in the regression setting\n\nWe indeed employ a strategy for stabilizing the training process. A predicted distance is ignored for gradient calculation when it goes beyond a threshold of 8 Å. Following the practice of Uni-Mol, we apply a mask to zero out the loss for atom pairs whose ground-truth distance is greater than 8Å. These pairs do not contribute to the gradient during training.\n\n>  Response to memorization or true generalization\n\nThanks for your suggestions. The docking structures used in pre-training only provide coarse-grained structural information. Initially, we did not consider their similarity to the test set in our experiments. To further investigate whether the model demonstrates memorization or generalization, we analyzed the similarity between the SIU test set and the PDBbind test set.\n\nSpecifically, we examined subsets of the pre-training data used in Section 4.2.3 (i.e., the 100K and 1M subsets and the 5M full set) and measured their overlap with the test set in terms of PDB ID, protein sequence homology (with similarity metrics such as sequence identity), and calculated the proportion of SIU samples that fall into these overlapping categories. The analysis results are presented in the table below.\n\n\n| Pre-training Scale | PDB ID Overlap | Seq. Sim. >90% | Seq. Sim. >50% |\n|--------------------|----------------|----------------|----------------|\n| 100K               | 0              | 0              | 0              |\n| 1M                 | 0.60%          | 4.10%          | 5.70%          |\n| 5M (full)          | 0.80%          | 5.30%          | 8.60%          |\n\nWe found that under the 100K pre-training setting, there was no significant overlap or high similarity between the pre-training and test sets. Yet, the model still demonstrated generalization capability — for example, the percentage of structures with RMSD < 2 on the apo set increased from 37.8% to 40.4%.\n\nHowever, for the 1M and 5M pre-training settings, we did observe some degree of overlap and similarity with the test set. To further examine whether the improvements were due to memorization or generalization, we conducted an ablation study under the 1M pre-training setting (Due to time constraints during the rebuttal period, we were only able to afford the experimental cost of this subset setting). We applied two filtering strategies:\n- Removing all exact PDB ID overlaps.\n- Further removing all proteins with sequence similarity > 50%.\n\nAfter obtaining the filtered dataset, we re-conducted the pretraining and fine-tuning processes, yielding the following results: \n\n\n| Pre-training settings\\ PDBBind (APO)                    | RMSDs < 2Å ↑ | Median RMSD ↓ |\n|----------------------------------|--------------|----------------|\n| Without condition                | 37.8         | 2.8            |\n| 100k                              | 40.4         | 2.5            |\n| 1M                             | 44.1         | 2.4            |\n| 1M (erase overlap)             | 43.5         | 2.5            |\n| 1M (erase > 50% seq sim)       | 42.6         | 2.5            |\n\nFrom the above table, we can conclude that:\n- Removing overlapping PDB IDs and similar proteins (sequence similarity > 50%) did lead to a drop in performance.\n- However, even after removing all proteins with >50% sequence similarity, the model still outperformed both the no-pretraining baseline and the 100K pre-training setting.\n\nThese findings suggest that while similar proteins in the pre-training set do contribute to performance gains, they are not the sole reason for the improvements. Our model has indeed learned generalizable representations, rather than simply memorizing the training data.\n\nAs for the similarity score between the pre-training data and test data mentioned in paper [1], we attempted to compute it using the runs-n-poses benchmark approach for our own pre-training and test datasets. However, we found that a necessary step in the data preparation process—run_structure_qc—would require approximately one week to complete, which exceeds the time constraints of the rebuttal period. Therefore, we conducted the alternative experiment described above to provide supporting evidence for our claim.\n\n\nIn the future, we plan to perform the same filtering experiments on the full pre-training dataset and update the corresponding results in the paper. We sincerely appreciate your thoughtful and constructive feedback.\n\n[1]:  Have protein-ligand co-folding methods moved beyond memorization?\n\n\n>  Can the authors also compare the pocket-aligned All-Atom RMSD < 1 of their method against other baselines\n\n\nWe evaluated FIGRDock's performance on PDBBind benchmark with pocket-aligned All-Atom RMSD < 1 and we compare with baselines mentioned in our paper. And the results are as follows:\n\n| | Pocket-aligned AA RMSD %<1↑   |\n|-------------------------|---------------------|\n|DiffDock-Pocket | 32.4 |\n|FlexDock | 39.8 |\n|FlexDock | 41.7 |\n|FIGRDock(ours)| 41.5 |\n\nFIGRDock achieved comparable result with baseline models in terms of pocket RMSD metric. This result indicates that the pocket-encoder pre-training facilitates the model's acquisition of a certain level of comprehension regarding the conformational space of proteins.\n\n\n> Can the authors also evaluate the protein-ligand interaction recovery metrics from [2] to see if key physico-chemical patterns are preserved?\n\nThank you for the suggestion. We carefully studied the methodology described in the paper and applied it to evaluate the protein-ligand interaction recovery rates of our PoseBusters test results. Our evaluation results are shown in the figure below. The results for all methods except FIGRDock are taken directly from Figure 2 of the original paper.\n\n| Category                 | Method                | RMSD < 2 | RMSD < 2 && PBValid | RMSD < 2 && PBValid && PLIF-valid @ 50% | RMSD < 2 && PBValid && PLIF-valid @ 100% |\n|--------------------------|-----------------------|----------|----------------------|------------------------------------------|-------------------------------------------|\n| Classical Docking        | GOLD                  | 75.7     | 70.9                 | 63.3                                     | 28.4                                      |\n| Classical Docking        | HYBRID                | 74.3     | 45.6                 | 37.0                                     | 16.8                                      |\n| Classical Docking        | FRED                  | 53.1     | 34.6                 | 30.1                                     | 14.9                                      |\n| Machine Learning Method  | FIGRDock              | 70.5     | 68.3                 | 42.7                                     | 12.8                                      |\n| Machine Learning Method  | DIFFDock-L            | 47.6     | 24.1                 | 21.4                                     | 7.0                                       |\n| Machine Learning Method  | RosettaFold-AllAtom   | 19.1     | 7.3                  | 2.9                                      | 1.5                                       |\n| Machine Learning Method  | Umol                  | 24.5     | 2.9                  | 0.7                                      | 0.3                                       |\n\n\nAs we can see, FIGRDock outperforms other machine learning methods across all metrics, including RMSD, PBValid, and interaction recovery rate. However, there is still a performance gap between FIGRDock and the classical docking method GOLD in terms of RMSD and PLIF-related metrics. This finding is consistent with the conclusions reported in the original study.\n\n\n\n> About limitation\n\nWe agreed with you that generative models can account for uncertainty in the docking task and it has the ability to correct its prediction through iterative sampling. We will further discuss these limitations in our final version."}, {"review_id": "wdS82ngLT1", "reviewer": "Reviewer_q2Sc", "summary": "This paper introduces FIGRDock, a regression framework for one-shot flexible protein-ligand docking. The core idea is a two-stage training strategy. First, the model is pre-trained on large datasets  to learn conditional pair representations. Second, this pre-trained model is fine-tuned on a smaller set of high-quality experimental crystal structures (PDBbind).  Tested on the PDBbind and PoseBusters, FIGRDock outperforms baselines such as FlexDock and ReDock.", "questions": "**Suggestions/Questions:**\n1. Providing a similarity analysis between pre-training dataset and testing set.\n2. Providing a comparison between more powerful baselines, such as DiffBind-FR and HelixDock.\n3. Besides the task, what are the main difference between FIGRDock and previous pre-training docking methods, such as CarsiDock?\n\nI'm willing to raise my score if these **suggestions/questions** are solved.", "limitations": "Yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 3, "strengths_and_weaknesses": "**Strengths**：\n1. The paper is generally well-written and easy to follow.\n2.  FIGRDock outputs baselines (such as FlexDock, ReDock, and Smina) on the PDBbind and PoseBusters test set\n\n\n**Weaknesses**:\n1. Lack of Technical Originality. The strategy of pre-training on large datasets of computationally docked structures has been explored in prior work (HelixDock, CarsiDock). The pre-training strategy of ligand encoder and pocket encoder are also previous Uni-Mol and ProFSA.\n2. The paper omits several highly relevant and competitive baselines in the flexible docking space. For example, DiffBind-FR.\n3. There is a major concern regarding the unfair comparison of the reported results. The model is pre-trained on the SIU dataset, which is derived from structures in the PDB. The PDBbind test set is a time-split subset of the PDB. It is highly probable that the molecular systems (or highly similar ones) in the PDBbind test set are present in the massive SIU pre-training dataset. While the authors use a time-split for fine-tuning, they do not mention any filtering of the pre-training data based on the test set. This data contamination would mean the model has essentially \"seen\" the test examples during pre-training, making the strong performance unsurprising and casting doubt on the model's true generalization ability to novel targets.", "was_revised": true, "final_justification": "My concerns have been solved.", "rebuttal": "> Providing a similarity analysis between pre-training dataset and testing set\n\nThank you for your valuable suggestions. In our initial experiments, we did not filter out overlapping PDB IDs between the pre-training dataset and the PDBbind test set, as the structures in the pre-training data were generated by docking software and are generally considered noisy and approximate.\n\nTo further investigate this concern, we analyzed the overlap between the pre-training datasets (at different scales: 100K, 1M, and 5M/full dataset) and the PDBbind test set in terms of both exact PDB ID matches and sequence-level homology. Specifically, we calculated the number of shared PDB IDs as well as the proportion of proteins with sequence similarity greater than 90% and 50%. The results are summarized in the following table:\n\n| Pre-training Scale | PDB ID Overlap | Seq. Sim. >90% | Seq. Sim. >50% |\n|--------------------|----------------|----------------|----------------|\n| 100K               | 0              | 0              | 0              |\n| 1M                 | 0.60%          | 4.10%          | 5.70%          |\n| 5M (full)          | 0.80%          | 5.30%          | 8.60%          |\n\nWe observe that in the 100K setting, there is no PDB ID overlap and essentially no sequence similarity to the test set. Yet, even under this setting, FIGRDock yields noticeable performance improvements (e.g., RMSD < 2: 37.8% → 40.4% on the apo set), indicating that the gains are not purely due to memorization.\n\nTo further examine whether the improvements stem from memorization or generalization, we conducted an ablation study under the 1M pre-training setting due to time constraints during the rebuttal period. We performed two filtering strategies: (1) removing all exact PDB ID overlaps, and (2) further removing all proteins with sequence similarity greater than 50%. The results are as follows:\n\n| PDBBind (APO)                    | RMSDs < 2Å ↑ | Median RMSD ↓ |\n|----------------------------------|--------------|----------------|\n| Without condition                | 37.8         | 2.8            |\n| 100k                              | 40.4         | 2.5            |\n| 1M                             | 44.1         | 2.4            |\n| 1M (erase overlap)             | 43.5         | 2.5            |\n| 1M (erase > 50% seq sim)       | 42.6         | 2.5            |\n\n- We found that removing overlapping PDB IDs and similar proteins (seq. sim > 50%) led to a drop in performance.\n- However, even after removing all proteins with >50% sequence similarity, the model still outperformed both the no-pretraining baseline and the 100K pre-training setting.\n\nThese results indicate that while similar proteins in the pre-training set do contribute to performance improvements, they are not the sole reason for the gains. Our model has indeed learned generalizable representations rather than simply memorizing training data.\n\nIn the future, we plan to conduct the same filtering experiments on the full pre-training dataset and update the corresponding results in the paper. We sincerely appreciate your thoughtful suggestion.\n\n\n> Providing a comparison between more powerful baselines\n\nWe present additional results comparing our method with DiffBind-FR and HelixDock on the PDBbind and PoseBusters V1 benchmarks, as shown in the table below. Our approach demonstrates a clear performance advantage over DiffBind-FR:\n\n| Method           | Task | PDBbind (RMSD < 2Å) | PoseBusters V1 (RMSD < 2Å) | PoseBusters V1 (RMSD < 2Å & PB-valid) |\n|------------------|----------------------|----------------------|---------------------------|-------------------------------------|\n| DiffBind-FR      | flexible docking | 51.2%                | 48.1%                     | 44.4%                               |\n| FIGRDock (ours)  | flexible docking | 57.2%                | 63.3%                     | 60.7%                               |\n| HelixDock        | rigid docking | 90.1%                | 85.6%                     | 85.2%                               |\n\n\nSince both DiffDock-FR and our method fall under the category of flexible docking, they are not directly comparable to HelixDock, which is based on rigid docking. \n\n> Besides the task, what are the main difference between FIGRDock and previous pre-training docking methods, such as CarsiDock?\n\nAlthough both our method and CarsiDock incorporate a pre-training stage, our approach targets a different and more challenging task scenario—flexible docking—in which both the protein and the small-molecule ligand undergo conformational changes. And CarsiDock is conducted on rigid docking, only considering molecule flexibility. Our pre-training strategy is carefully designed to align with our final application. Specifically, we introduce a pocket encoder that captures side-chain information to better model protein flexibility. Furthermore, during the noise-adding process, we apply perturbations to protein side chains to simulate the conformational transition from the apo to holo state.\n\n> The difference with Uni-Mol and ProFSA\n\n\nOur pre-training strategy differs from UniMol primarily in the design of the noise mechanism. Specifically, we apply a hybrid noise to the pocket, combining side-chain perturbations with Gaussian noise, whereas UniMol uses noise sampled from a uniform distribution. We consider the side-chain perturbation noise to be closer to physical protein dynamics. Additionally, we extend the atom type vocabulary by treating atoms of the same chemical type but located at different positions within protein side chains as distinct types. Although we utilize the ProFSA dataset, our training objectives and strategy are  different from the contrastive learning approach used in ProFSA."}]

Author rebuttal:

Meta-review: Overall, this is a borderline paper. Reviewers raised several concerns regarding the paper, including:  
- Technical novelty and contribution  
- Evaluation fairness and generalization  
- Methodological details  

The rebuttal phase was constructive, as the authors provided detailed clarifications and conducted additional experiments, which led to an increase in reviewer scores.

After careful consideration of the reviews and rebuttal, I recommend acceptance. While the novelty is incremental in some respects, the paper makes a meaningful contribution to the flexible docking domain by combining efficiency, accuracy, and scalability.
Decision: Accept (poster)