BridgeDock: Taming Generative Co-Folding Prior for Molecular Docking with Diffusion Bridge

Shikun Feng; Zipeng Yang; Qiuyi Li; Mingze Yin; Xiaoyuan Zhang; Tingjun Hou; Yiheng Zhu

cs.AIOct 31, 2026

BridgeDock: Taming Generative Co-Folding Prior for Molecular Docking with Diffusion Bridge

Authors: Shikun Feng, Zipeng Yang, Qiuyi Li, Mingze Yin, Xiaoyuan Zhang, Tingjun Hou, Yiheng Zhu

Organizations: Zhongguancun Academy, Beijing, China. · Zhongguancun Institute of Artificial Intelligence, Beijing, China. · School of Physical Science and Technology, Beijing University of Posts and Telecommunications, Beijing, China. · College of Computer Science and Technology, Zhejiang University, Hangzhou, China. ·

Abstract

Molecular docking aims to predict the three-dimensional structure of a protein-ligand complex; in the conventional structure-based setting, it is conditioned on an input protein structure and ligand, often starting from an apo receptor in flexible docking or a holo receptor in rigid docking. Recent co-folding models exemplified by AlphaFold 3 achieve strong accuracy in protein-ligand complex prediction, but they typically generate complexes from sequence-derived inputs and therefore do not directly use apo structures as structural priors in docking, leading to a larger conformational search space and weaker robustness in difficult or few-step settings. We present BridgeDock, a framework that adapts pretrained co-folding backbones to flexible docking by modeling the apo-to-holo transition as a diffusion bridge, allowing generation from the initial protein-ligand state rather than from Gaussian noise. To make this bridge formulation compatible with pretrained denoisers, we introduce an alignment mechanism that maps bridge states to the original denoising schedule of the pretrained model. Experiments on standard flexible docking benchmarks show that BridgeDock consistently outperforms strong co-folding baselines. Moreover, BridgeDock is computationally efficient at inference time and remains effective even with very few denoising steps.

Figures & tables

Figure 1: Comparison of three paradigms for protein-ligand docking. Left: Traditional deep learning-based methods directly predict holo complexes from protein and ligand inputs. Middle: Recent co-folding methods formulate docking as a generation process starting from noise. Right: BridgeDock leverages apo structures as explicit structural priors and formulates docking as an apo-to-holo diffusion bridge.

Figure 2: Overview of BridgeDock. Unlike prior co-folding diffusion methods that start from Gaussian noise, and Apo SFT which only conditions on apo structures, BridgeDock formulates protein-ligand docking as an apo-to-holo diffusion bridge, directly transforming apo conformations into target holo complexes.

Model	lDDT (%)	%<2 $\uparrow$	%<5 $\uparrow$	Mean	Median	# Steps
	Docking Accuracy $\uparrow$			Ligand RMSD (Å) $\downarrow$
AF3 baseline (ODE)	83.91	69.76	86.51	2.33	1.19	1
BridgeDock	90.77	69.86	88.04	2.19	1.08	1
AF3 baseline (ODE)	84.26	68.23	84.40	2.72	1.14	5
BridgeDock	91.23	71.77	88.52	2.33	1.04	5
AF3 baseline	84.79	68.61	84.69	2.64	1.09	20

Table 1: Docking performance on PoseBusters under different denoising budgets. The 20-step setting is the default used in the main comparison. Higher values indicate better performance for lDDT and for the percentages of predictions with ligand RMSD below 2 Å or 5 Å, whereas lower values indicate better performance for mean and median ligand RMSD.

Search-based methods
	Docking Accuracy $\uparrow$			Ligand RMSD (Å) $\downarrow$
Model	lDDT (%)	%<2 $\uparrow$	%<5 $\uparrow$	Mean	Median
SMINA	–	3.60	20.50	–	7.30
GNINA	–	6.60	28.00	–	7.20
Deep learning-based methods
DiffDock-Pocket	–	41.70	74.90	–	2.60

Table 2: Comparison with prior methods on the PDBBind test set. Methods are grouped into search-based methods, deep learning-based methods, and co-folding methods. The values for SMINA, GNINA, DiffDock-Pocket, Re-Dock, and FlexDock are literature-reported benchmark results, whereas the Protenix baseline and BridgeDock results are generated by us. The three flexible docking models use ligand-derived pocket information as detailed in Appendix E . For these literature-reported methods, only the percentages below the 2 Å and 5 Å ligand RMSD thresholds and median RMSD are available; unreported metrics are marked as –. Higher is better for lDDT and the threshold percentages, while lower is better for ligand RMSD.

	Settings			Docking Accuracy $\uparrow$			Ligand RMSD (Å) $\downarrow$
Model	Apo	Bridge	AF3-prior	lDDT (%)	%<2 $\uparrow$	%<5 $\uparrow$	Mean	Median
AF3 baseline	–	–	✓	84.79	68.61	84.69	2.64	1.09
AF3-SFT-Apo	✓	–	✓	87.53	70.14	86.12	2.48	1.01
BridgeDock ${}_{\mathrm{w/o\ align}}$	✓	✓	–	85.23	68.42	86.60	2.77	1.22
BridgeDock	✓	✓	✓	90.41	73.21	89.47	2.26	1.09

Table 3: Ablation results on PoseBusters. The table includes both the apo-conditioned supervised fine-tuning baseline, denoted as AF3-SFT-Apo, and a bridge variant without AF3 alignment, denoted as BridgeDock

{}_{\mathrm{w/o\ align}}

. Higher is better for lDDT and threshold percentages, while lower is better for ligand RMSD.

Appendix figures & tables7 assets

Supplementary material from the paper’s appendix.

Appendix

\bar{\mathbf{x}}^{\mathrm{apo}}\leftarrow\mathbf{x}^{\mathrm{apo}}-\operatorname{mean}_{\mathrm{atom}}(\mathbf{x}^{\mathrm{apo}}),\qquad\hat{\mathbf{x}}_{0}^{(0)}\leftarrow D_{\theta}(\bar{\mathbf{x}}^{\mathrm{apo}},\sigma_{\max},\mathcal{C})

Appendix

Algorithm 2 DBIM Inference with a VE Bridge from Apo Structures

t\sim\mathcal{U}(\sigma_{\min},\sigma_{\max})

Appendix

Algorithm 3 Training with AF3-Aligned VE Bridge Samples

\hat{\mathbf{x}}_{0}^{(0)}\leftarrow\mathbf{0}

Appendix

Algorithm 4 Inference with AF3-Aligned DBIM Reverse Bridge Updates

	Docking Accuracy $\uparrow$			Ligand RMSD (Å) $\downarrow$
Offset $\delta$ ( $\tau_{0}$ )	lDDT (%)	% $<2$ Å	% $<5$ Å	Mean	Median
1 (39.0)	90.01	71.77	89.47	2.26	1.07
0.1 (39.9)	90.41	73.21	89.47	2.26	1.09
0.01 (39.99)	90.46	72.25	89.95	2.18	1.06
0.001 (39.999)	90.43	72.73	90.43	2.16	1.07

Appendix

Table 4: Sensitivity to the diffusion-time offset on PoseBusters using 20 sampling steps. The start time is

\tau_{0}=\sigma_{\max}-\delta

. Higher is better for lDDT and the ligand RMSD threshold percentages; lower is better for mean and median ligand RMSD.

Backbone	Method	Success rate (%) $\uparrow$	lDDT-LP $\uparrow$	lDDT-PLI $\uparrow$
Protenix-Mini	Baseline	41.65	0.8153	0.6145
Protenix-Mini	BridgeDock	42.63	0.8829	0.6377
Protenix-Base v1.0.0	Baseline	56.97	0.9107	0.7251
Protenix-Base v1.0.0	BridgeDock-v1	59.33	0.9112	0.7504

Appendix

Table 5: FoldBench protein-ligand results on 509 targets with a single random seed and five predictions per target. We report confidence-ranked top-1 performance. Higher is better for all metrics; bold indicates the better result within each backbone setting.

Figure 3: Qualitative structural overlays for PoseBusters targets 7bmi (top), 7nf3 (middle), and 7ozc (bottom). Each row compares BridgeDock (left) and the AF3-style Protenix baseline (right) against the same ground-truth holo complex. Ground-truth proteins and ligands are shown in cyan and yellow, respectively; BridgeDock predictions are shown in blue and red; and Protenix predictions are shown in magenta and green. Closer overlap of the protein cartoons indicates better agreement in receptor conformation, while closer overlap of the ligand sticks indicates better agreement in ligand pose. In these examples, BridgeDock more closely matches both the ground-truth receptor and ligand, whereas the Protenix baseline shows larger deviations in receptor conformation and/or ligand placement.

Figure 4: Additional qualitative structural overlays for PoseBusters targets 7lev (top), 7xqz (middle), and 8cnh (bottom). Each row compares BridgeDock (left) and the AF3-style Protenix baseline (right) against the same ground-truth holo complex. Ground-truth proteins and ligands are shown in cyan and yellow, respectively; BridgeDock predictions are shown in blue and red; and Protenix predictions are shown in magenta and green. The protein and ligand overlays visualize agreement in receptor conformation and ligand pose, respectively. Across the shown examples, BridgeDock exhibits closer joint agreement with the ground-truth protein-ligand complexes, particularly around the protein-ligand interface.

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Flexible docking is a highly practical task, aiming to predict the joint configuration of a protein-ligand complex from a protein apo structure and a ligand SMILES string. Diffusion and flow-based generative models have made this task tractable by modeling the posterior distribution over the ligand pose and the protein conformation conditioned on the apo input. The central challenge, however, remains the long-tailed apo-to-holo structure shift, where a small fraction of protein undergo large conformational changes. In this work, motivated by the practical observation that downweighting large shift examples improves generalization, and by the mixed nature of the long tail consisting of genuinely difficult examples and noisy ones, we propose to use curriculum learning for this task, gradually incorporating large conformational change examples during training. With a linear pacing function, we show that an existing method can be strongly enhanced. On the PDBBind ESMFold apo benchmark, with relaxation and confidence-based pose selection, the resulting model GCLDock achieves a 46.52% top-1 success rate (ligand RMSD less than 2A) and 40.26% when additionally requiring PoseBusters validity, outperforming existing baselines.

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