OpenDDE

License: OpenDDE is open source and free for academic and commercial use under an Apache-2.0 license. Please refer to the license for full terms.

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aurekaresearch/OpenDDE

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aurekaresearch/OpenDDE

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@misc{aureka2026opendde,
  title={Folding, Reasoning, and Scaling with Open-source Drug Discovery Engine},
  author={{Aureka AI Research}},
  year={2026},
  url={https://arxiv.org/abs/2607.03787},
  note={Preprint}
}

@misc{aureka2026opendde_software,
  title={OpenDDE: An Open-Source All-Atom Biomolecular Co-Folding Foundation Model},
  author={{Aureka AI Research}},
  year={2026},
  howpublished={\url{https://github.com/aurekaresearch/OpenDDE}}
}

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evo-design/proto-tools/proto_tools/tools/structure_prediction/opendde

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Function	Description
`run_opendde()`	All-atom biomolecular structure prediction using OpenDDE (GPU)	Docs Source

Background

OpenDDE (Aureka AI Research, 2026) predicts the joint 3D structure of a biomolecular assembly from the sequences and chemical components it contains. It is an openly licensed, all-atom co-folding model where one model folds complexes that mix proteins, DNA, RNA, and small-molecule ligands and predicts how those components are arranged relative to one another. Each protein chain can be paired with a multiple-sequence alignment (MSA) of evolutionarily related sequences, whose covariation patterns supply the evolutionary signal the model uses to place residues. Architecturally, OpenDDE follows AlphaFold3: it carries a single representation of the input tokens and a pairwise representation over token pairs, refines them through a Pairformer-style trunk, and generates all-atom coordinates with a diffusion module that starts from noise and iteratively denoises into a structure. Several structures can be sampled per complex and ranked by a confidence score. Predicted confidence includes a per-residue predicted local distance difference test (pLDDT) for local reliability, a global predicted distance error (gPDE) for the relative placement of tokens, and predicted template-modeling (pTM) and interface predicted template-modeling (ipTM) scores that summarize overall and interface accuracy, together with an overall ranking score used to select the best sample. The reference implementation is open-sourced at aurekaresearch/OpenDDE, with both the code and the model parameters released under the Apache-2.0 license for academic and commercial use. It builds on ideas and components from Protenix, OpenFold, and ColabFold. Two checkpoints are released: a general-purpose model and an antibody-antigen-tuned variant. It was developed by Aureka AI Research as an open drug-discovery engine spanning structure prediction, design, and optimization.

Learning Resources

OpenDDE Technical Report (Aureka AI Research) - the technical report describing OpenDDE’s architecture, training data, and benchmark results.

Tools

OpenDDE Structure Prediction (`opendde-prediction`)

Predicts the 3D structure of a biomolecular complex. Each input complex can combine protein, DNA, RNA, and ligand chains; the assembly is folded by OpenDDE and returned as a predicted Structure per complex with confidence metrics: average pLDDT, pTM, interface pTM for multi-chain complexes, a global predicted distance error, and a ranking score.

API Reference

Source

Input: OpenDDEInput

List[Complex]

required

List of complexes to predict structures for. Inherited from StructurePredictionInput. Each complex can contain one or more chains of proteins, DNA, RNA, or ligands.

Show Complex

List[Chain | Fragment]

required

Chains in the complex, in input order.

array

Pre-computed MSAs, one entry per complex. Each entry is a ComplexMSAs (per-chain MSAs keyed by chain index). Populated by preprocess() or supplied directly. Default: None.

Source

Config: OpenDDEConfig

string

default:"opendde_v1"

Which weights to fold with — a bundled model name ("opendde_v1" general-purpose or "opendde_abag" antibody-antigen, both auto-downloaded into PROTO_MODEL_CACHE on first inference) or a path to a custom .pt checkpoint. Default: "opendde_v1".

integer

default:"1"

Independent diffusion samples per complex (--sample); the best by ranking score is returned. Default: 1.

integer

default:"200"

Diffusion denoising steps (--step). Higher = more refined but slower. Default: 200.

integer

default:"10"

Recycling iterations (--cycle). Higher = more accurate but slower. Default: 10.

boolean

default:"False"

Enable OpenDDE’s template pipeline (--use_template). proto_tools does not generate templates, so this relies on OpenDDE’s own search, which needs the search_database/ assets setup.sh does not download. Default: False.

boolean

default:"False"

Enable OpenDDE’s RNA MSA pipeline (--use_rna_msa). Also requires the search_database/ assets. Default: False.

integer

default:"0"

Verbosity level (0=quiet, 1=info, 2=debug, 3=raw subprocess stderr). True is coerced to 1 and False to 0.

string

default:"cuda"

Device to run on ("cuda", "cpu"). Inherited. Default: "cuda".

integer

default:"1200"

Max execution time in seconds; None waits indefinitely. Default: 1200.

integer

Random seed. When set, tools run reproducibly up to small GPU float noise (see BaseToolOutput.approx_equal), and the seed participates in cache keys. When None, cacheable seed-sensitive tools skip cache until seeded.

boolean

default:"False"

Inherited. When True, OpenDDE is run with --need_atom_confidence and the per-token PAE matrix (pae) plus a derived avg_pae scalar are attached to the metrics. Off by default — the matrix is O(n_token^2) to compute and serialize. Default: False.

boolean

default:"True"

Auto-generate protein MSAs via MMseqs2 homology search; supplied MSAs always override this. Inherited. Default: True.

Mmseqs2HomologySearchConfig

MMseqs2 search config; only used when use_msa=True. Inherited. Default: None.

boolean

default:"True"

Taxonomy-pair heterocomplex protein MSAs. Inherited. Default: True.

Source

Output: OpenDDEOutput

List[Structure]

required

Predicted structures, one per input complex, each carrying an :class:OpenDDEMetrics instance on .metrics.

Show Structure

string

required

Raw structure content in PDB or CIF format.

string

Format of the content string (auto-detected if omitted).

BFactorType

What the B-factor column represents.

string

Optional source identifier (filepath or tool name).

Metrics

Associated metrics (e.g., pLDDT, pTM scores, per-chain lists, pairwise matrices). None values are stripped at construction.

Metrics (one set per structures item)

Metric	Type	Range	Availability
`avg_plddt`	float	0.0 to 100.0	always
`ptm`	float	0.0 to 1.0	always
`iptm`	float	0.0 to 1.0	always
`gpde`	float	≥ 0.0	always
`ranking_score`	float	unbounded	always
`has_clash`	bool	unbounded	always
`avg_pae`	float	0.0 to 32.0	when include_pae_matrix=True
`pae`	list[list[float]]	0.0 to 32.0	when include_pae_matrix=True

Applications

This tool predicts the structure of multi-component assemblies such as protein-DNA and protein-RNA complexes or protein-ligand binding poses, and the opendde_abag checkpoint targets antibody-antigen complexes specifically. Running it on a multi-chain complex also estimates how confidently the components are placed relative to each other through interface pTM and the global predicted distance error, which is informative for ranking predicted interfaces before trusting them downstream.

Usage Tips

model_checkpoint selects the weights. Pass a bundled model name — opendde_v1 (default, general-purpose) or opendde_abag (antibody-antigen tuned) — both auto-downloaded into PROTO_MODEL_CACHE on first inference; or pass a path to a custom .pt checkpoint to fold with your own weights.
use_msa defaults to True. An MMseqs2 homology search generates an MSA for each protein chain; set it False to fold single-sequence, or attach precomputed MSAs to the input, which always take precedence. OpenDDE’s own internal MSA search runs only when use_msa=True and no MSAs are supplied.
num_samples draws independent diffusion samples. OpenDDE draws num_samples (default 1) structures per complex and keeps the best by ranking score, so raising it explores more candidate conformations at proportional cost. Predictions are stochastic; set seed for reproducibility.
num_steps and num_cycles trade accuracy for time. num_steps (default 200) sets the number of diffusion denoising steps and num_cycles (default 10) sets the recycling iterations; higher values refine the prediction but increase runtime.
use_template and use_rna_msa enable OpenDDE’s extra pipelines. Both default to False; enable use_template for OpenDDE’s template search and use_rna_msa for its RNA MSA pipeline when folding RNA-containing complexes.
Confidence is reported as pLDDT, pTM, ipTM, gPDE, and a ranking score. avg_plddt (0 to 100) is the primary per-structure quality metric; iptm is 0.0 for single-chain inputs, and gpde is in angstroms. OpenDDE writes only scalar summary confidences, so no per-token PAE matrix is available and the inherited include_pae_matrix is ignored.

Toolkit Notes

These apply to every OpenDDE tool in this toolkit (opendde-prediction).

Requires a GPU. OpenDDE runs through a PyTorch backend and needs an NVIDIA GPU; CPU execution is not practical.
Open AlphaFold3-style co-folding model. OpenDDE releases both code and weights under Apache-2.0 for academic and commercial use, and follows the AlphaFold3 diffusion architecture like Boltz-2 and Protenix. Its opendde_abag checkpoint additionally specializes in antibody-antigen complexes.
Predictions are stochastic. Structures come from a diffusion process, so repeated runs vary unless sampling is seeded.
Early preview upstream. Checkpoints and interfaces may change, so pin the version you validate against.

Example notebook: See the full working example for a copy-paste-ready walkthrough.

Infrastructure Guides

The following guides cover how to run tools efficiently and at scale.

Tool Persistence

Keep a tool’s model warm across calls instead of reloading it every invocation.

Device Management

How GPUs are allocated to tools and how to target specific devices.

Parallel Execution

Fan a batch of inputs out across multiple GPUs.

Tools

Guides

Tool Catalog

Concepts

Implementing a Tool

Background

Learning Resources

Tools

OpenDDE Structure Prediction (`opendde-prediction`)

API Reference

Applications

Usage Tips

Toolkit Notes

Infrastructure Guides

Tool Persistence

Device Management

Parallel Execution

​Background

​Learning Resources

​Tools

​OpenDDE Structure Prediction (opendde-prediction)

​API Reference

​Applications

​Usage Tips

​Toolkit Notes

​Infrastructure Guides

Tool Persistence

Device Management

Parallel Execution

Background

Learning Resources

Tools

OpenDDE Structure Prediction (`opendde-prediction`)

API Reference

Applications

Usage Tips

Toolkit Notes

Infrastructure Guides