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. · College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China.
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.
Docking Accuracy ↑
Ligand RMSD (Å) ↓
Model
lDDT (%)
%<2 ↑
%<5 ↑
Mean
Median
# Steps
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.
Docking Accuracy ↑
Ligand RMSD (Å) ↓
Model
lDDT (%)
%<2 ↑
%<5 ↑
Mean
Median
Search-based methods
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 ↑
Ligand RMSD (Å) ↓
Model
Apo
Bridge
AF3-prior
lDDT (%)
%<2 ↑
%<5 ↑
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 w/oalign
✓
✓
–
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 w/oalign . Higher is better for lDDT and threshold percentages, while lower is better for ligand RMSD.
Algorithm 2 DBIM Inference with a VE Bridge from Apo Structures
t∼U(σmin,σmax)
Appendix
Algorithm 3 Training with AF3-Aligned VE Bridge Samples
x^0(0)←0
Appendix
Algorithm 4 Inference with AF3-Aligned DBIM Reverse Bridge Updates
Docking Accuracy ↑
Ligand RMSD (Å) ↓
Offset δ ( τ0 )
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 τ0=σmax−δ . Higher is better for lDDT and the ligand RMSD threshold percentages; lower is better for mean and median ligand RMSD.
Backbone
Method
Success rate (%) ↑
lDDT-LP ↑
lDDT-PLI ↑
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.
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.
Wenyin Zhou, Hossein Azizpour
KTH Royal Institute of Technology · Science for Life Laboratory
All-atom generative modeling of 3D biomolecular complexes has emerged as the dominant paradigm for predicting the structure of proteins and protein-ligand systems. Generating structures at the atomic level of fidelity, however, typically requires expensive iterative diffusion rollouts, making both conventional deployment and inference-time search techniques computationally costly. In this paper, we introduce the Denoiser Cofolding All-Atom Flowmap (DeCAF) framework for distilling state-of-the-art all-atom cofolding models into all-atom flow maps that produce high-quality samples in only a few inference steps. We build DeCAF on a denoiser-based formulation of flow maps with endpoint losses that naturally support SE(3) rigid alignment, which we show is critical for training accurate models. We further derive a simple change of variables that lets DeCAF operate in the σ-space noise schedule of EDM-style architectures, enabling direct distillation from pretrained cofolding diffusion models. Equipped with DeCAF's flowmap lookahead, we introduce a purpose-built inference-time framework that improves sampling through reward-guided search. Empirically, DeCAF-Boltz statistically improves over Boltz-1x in both accuracy (RMSD) and physical validity scores of protein-ligand poses at strict NFE budgets on the challenging Runs N' Poses, while also showing a more optimal Pareto frontier across all inference compute budgets on PoseBusters. Distilling the state-of-the-art Pearl cofolding model, DeCAF-Pearl outperforms diffusion-based cofolding models and matches its teacher on success rate while using 5x fewer NFEs. We release our code at https://github.com/genesistherapeutics/decaf.
Gianluca Scarpellini, Ron Shprints, Peter Holderrieth +7
1Genesis Molecular AI · Massachusetts Institute of Technology · Carnegie Mellon University +2
Accurately modeling biomolecular interactions is a central bottleneck in biology and therapeutic discovery. Here, we introduce Open Drug Discovery Engine (OpenDDE), an open-source, all-atom biomolecular foundation model that uses co-folding as the entry point to a scalable AI-driven drug discovery engine. Rather than treating structure prediction as an isolated endpoint, OpenDDE is designed as a shared structural reasoning layer for modeling sequence-structure-function relationships across biomolecular complexes, enabling complex structure prediction today while providing a foundation for de novo design, affinity estimation, structure-conditioned optimization, and more. OpenDDE integrates advances in all-atom architecture, atomic latent reasoning, inference optimization, and large-scale data processing to achieve IsoDDE-level co-folding accuracy within a reproducible and openly accessible framework. We also identify two scaling-law directions for co-folding models, revealing practical routes for continued improvement through data, model, inference, and training scaling. By releasing training code, inference pipelines, checkpoints, and benchmarks, OpenDDE aims to democratize access to frontier biomolecular intelligence, accelerate global collaboration, and lay an open foundation for next-generation drug discovery systems that can move from predicting molecular structures toward designing, scoring, and optimizing therapeutic candidates for human health.