Add OpenDDE tool card

references/software-registry.yaml

@@ -322,6 +322,48 @@ software:
     citation_notes: "Record repo commit, model weights, and inputs."
     status: planned
     notes: "Verify current repo/license before packaging."
+  opendde:
+    role: biomolecular_complex_prediction
+    image_family: ai-design
+    install_method: source_or_public_docker
+    upstream_repo: "https://github.com/aurekaresearch/OpenDDE"
+    upstream_commit_sha_observed_2026_07_05: "a72e9f655231660f8af0072dbdb8b2a54f3fbd3c"
+    docker_image: "aurekaresearch/opendde:v1"
+    docker_tag_digest_observed_2026_07_05: "sha256:a9404f76df1cd965a80447ee30c9c440af48b199581d9bd44ee157c18109a9d6"
+    upstream_model_repo: "https://huggingface.co/aurekaresearch/OpenDDE"
+    upstream_model_repo_commit_observed_2026_07_05: "eddd563ce96571f784012edd8f045181c8f8627d"
+    released_checkpoints: "opendde.pt, opendde_abag.pt"
+    checkpoint_sizes_observed_2026_07_05: "opendde.pt ~2.5GB; opendde_abag.pt ~2.5GB"
+    common_runtime_size_observed_2026_07_05: "~0.65GB before search databases"
+    license_class: open_source_apache_2_reported_by_repo
+    license_gate: verify_current_terms_and_weight_posture
+    runpod_fit: strong_after_no_search_canary
+    gpu_class: "RTX 4090/L40S for canaries; 4x GPU for Fold-CP distributed route"
+    smoke_command: "opendde doctor && opendde --help"
+    runtime_artifacts: "opendde_predictions/, opendde_manifest.json, summary_confidence.json, full_data.json when --need_atom_confidence true"
+    citation_notes: "Record repo commit, Docker digest or source install route, Hugging Face model commit, checkpoint hashes, input JSON, MSA/template posture, and confidence sidecars."
+    weights_cache_default: "$OPENDDE_ROOT_DIR/checkpoint"
+    weights_cache_env_var: "OPENDDE_ROOT_DIR"
+    weights_source: "Hugging Face aurekaresearch/OpenDDE; opendde.pt default, opendde_abag.pt selected with --load_checkpoint_path."
+    status: planned
+    notes: |
+      AlphaFold-3-style all-atom prediction/cofold lane for proteins, DNA,
+      RNA, ligands, ions, explicit covalent bonds, and antibody-antigen
+      canaries. Use as an orthogonal validator next to Boltz, Chai-1,
+      AF2-Multimer, and ESMFold2, not as a generator.
+
+      Start with a no-search public protein canary:
+      --use_msa false --use_template false --use_rna_msa false. Promote to
+      MSA/template/RNA-MSA only after database, HMMER/Kalign, and public/private
+      sequence posture are declared. Do not send private sequences to the public
+      ColabFold-compatible MMseqs2 service.
+
+      The reviewed source path loads PyTorch and pickle-based runtime artifacts,
+      including the model checkpoint and RDKit CCD cache. Execution-ready
+      provider packets must pin URL, remote ETag/Xet hash where available, local
+      SHA-256, and byte size before loading. For ipSAE-style rescoring, run with
+      --need_atom_confidence true and preserve the full_data JSON; summary-only
+      output is scalar confidence, not a complete interface sidecar bundle.
   alphafold3:
     role: biomolecular_prediction
     image_family: ai-design

tools/README.md

@@ -46,6 +46,7 @@ The wider tool knowledge base lives in [`references/software-registry.yaml`](../
 - [Cofold scoring stack](cofold-scoring-stack.md). Multi-validator slate plus ipSAE rescore. Reads from the individual cards below.
 - [Boltz](boltz.md). Open biomolecular cofold with confidence and full PAE outputs.
 - [Chai-1](chai.md). Open biomolecular cofold with MSA-driven prediction.
+- [OpenDDE](opendde.md). Open all-atom biomolecular cofold/prediction lane with ABAG checkpoint and optional PAE/PDE sidecars.
 - [ESMFold2](esmfold2.md). Biohub structure prediction and foldability review lane, with Hugging Face weights as the first cloud canary route and Biohub API as optional/deferred.
 - [ESMFold2 binder control notes](esmfold2-binder-controls.md). Sequence, structure, interface, logit, and optimization controls for constraining ESMFold2/Biohub binder-design runs against a deposited target site.
 - [BioEmu](bioemu.md). Sequence-conditioned monomer ensemble sampling for flexibility-risk review.
@@ -82,7 +83,7 @@ A short routing guide. Pick the designer arm by target and binder length, then a
 | A protein that changes shape on ligand binding (allostery, AND gate, biosensor) | [SwitchCraft](switchcraft.md) | [Cofold scoring stack](cofold-scoring-stack.md) for the orthogonal switch check |
 | Add a function or effector to a validated binder (fusion construct) | [DOMINO](domino.md) | [Cofold scoring stack](cofold-scoring-stack.md) to prove both modules fold |
 | Triage or mature antibody variant libraries (sequence-only) | [CoSiNE](cosine.md) | [Cofold scoring stack](cofold-scoring-stack.md) for an orthogonal structural check |
-| Cofold one designed candidate against a target | [Boltz](boltz.md) or [Chai-1](chai.md) alone | [Cofold scoring stack](cofold-scoring-stack.md) for multi-validator gating |
+| Cofold one designed candidate against a target | [Boltz](boltz.md), [Chai-1](chai.md), or [OpenDDE](opendde.md) alone | [Cofold scoring stack](cofold-scoring-stack.md) for multi-validator gating |
 | Check foldability or uncertainty for a public sequence/candidate | [ESMFold2](esmfold2.md) | [Cofold scoring stack](cofold-scoring-stack.md) when interface or binder claims are requested |
 | Check monomer conformational spread | [BioEmu](bioemu.md) | [MolViewSpec](molviewspec.md) for report scenes, [Cofold scoring stack](cofold-scoring-stack.md) when interface claims are requested |
 | Prioritize a large ligand library | [MolPAL](molpal.md) | Score provider lane, [PoseBusters](posebusters.md), screening consensus ranking |
@@ -99,14 +100,15 @@ target prep
   -> designer (RFdiffusion3 | RFpeptides | HelixDiff | PepGLAD | EvoBind | Genie3)
   -> sequence design (ProteinMPNN or SolubleMPNN)
   -> foldability/uncertainty review (ESMFold2 where useful)
-  -> cofold slate (Boltz + Chai-1 + AF2-Multimer)
+  -> cofold slate (Boltz + Chai-1 + AF2-Multimer + optional OpenDDE)
   -> ipSAE rescore on PAE matrices
   -> consensus gate
   -> refinement
   -> rendered report
 ```
 
-The cofold scoring stack card explains how to compose Boltz, Chai-1, and AF2-Multimer into the validator slate.
+The cofold scoring stack card explains how to compose Boltz, Chai-1,
+AF2-Multimer, and optional OpenDDE into the validator slate.
 
 ## Important Context
 

tools/cofold-scoring-stack.md

@@ -11,7 +11,7 @@ Public scaffold: yes. Runtime execution: review required for current package ter
 ## Hand A Mission To An Agent
 
 ```text
-Use the BioSymphony Structure Factory skill with the Cofold Scoring Stack tool card. For target <PDB:ID> and candidate set <path>, prepare a multi-validator cofold lane that runs Boltz, Chai-1, and AF2-Multimer in parallel, then rescores with ipSAE for a min-ipSAE consensus gate. Specify recycle and sample counts per validator and the closeout artifact list.
+Use the BioSymphony Structure Factory skill with the Cofold Scoring Stack tool card. For target <PDB:ID> and candidate set <path>, prepare a multi-validator cofold lane that runs Boltz, Chai-1, AF2-Multimer, and optional OpenDDE in parallel, then rescores with ipSAE for a min-ipSAE consensus gate. Specify recycle and sample counts per validator and the closeout artifact list.
 ```
 
 ## Headline Finding (2026): Multi-Validator min-ipSAE Consensus
@@ -26,13 +26,15 @@ This card is the meta-lane. Individual cofold validators have their own cards:
 
 - [Boltz](boltz.md) for fast cofold with full PAE output.
 - [Chai-1](chai.md) for MSA-driven cofold with parquet inputs.
+- [OpenDDE](opendde.md) for all-atom biomolecular cofolds, ABAG canaries, and optional PAE/PDE sidecars.
 - AF2-Multimer (via LocalColabFold) as the long-established baseline validator.
 
 A common slate:
 
 1. **AF2-Multimer v3** via LocalColabFold (`alphafold2_multimer_v3` with `--templates` on for target classes with dense PDB coverage).
 2. **Boltz-2** with optional template injection (`templates: [{cif:<ref>.cif, force:true}]` in YAML).
 3. **Chai-1** with proper MSA + template (`use_esm_embeddings=False`, `msa_directory=<target_aligned.pqt>`).
+4. **OpenDDE** for selected canaries or ranked candidates, especially antibody-antigen complexes where the ABAG checkpoint is relevant.
 
 Each validator emits a PAE matrix → ipSAE post-hoc rescore.
 
@@ -129,6 +131,27 @@ colabfold_batch \
 
 5-model mean+stddev is your free orthogonal confidence proxy.
 
+### OpenDDE
+
+```bash
+LAYERNORM_TYPE=torch opendde pred \
+    -i inputs/design_001.opendde.json \
+    -o opendde_out/design_001 \
+    -n opendde_v1 \
+    --use_msa false \
+    --use_template false \
+    --use_rna_msa false \
+    --sample 3 \
+    --step 200 \
+    --cycle 10 \
+    --need_atom_confidence true
+```
+
+Use `--load_checkpoint_path "$OPENDDE_ROOT_DIR/checkpoint/opendde_abag.pt"` for
+the ABAG checkpoint. Preserve both summary confidence JSON and `full_data` JSON
+when the output will feed an ipSAE-style or PAE/PDE sidecar gate. Summary-only
+output is useful for triage but incomplete for interface-error rescoring.
+
 ### ABCFold (one-call multi-validator)
 
 ```bash
@@ -174,6 +197,9 @@ python /workspace/ipsae/ipsae.py \
 | AF2M | `--num-recycle` | 3 | 3 | Bump to 5 for borderline calls |
 | AF2M | `--rank` | plddt | **`multimer`** | iPTM-weighted ranking |
 | AF2M | `--templates` | off | **on** when target has PDB coverage | Templates pin register where cofolders hallucinate |
+| OpenDDE | `--need_atom_confidence` | false | **true** for ranked candidates | Saves PAE/PDE/contact sidecars. |
+| OpenDDE | `--use_msa`, `--use_template`, `--use_rna_msa` | true/false by flag | **false for first canary** | Avoids public-service and large-database dependencies. |
+| OpenDDE | `--load_checkpoint_path` | default `opendde.pt` | use `opendde_abag.pt` for ABAG canaries | Selects antibody-antigen checkpoint. |
 | ipSAE | `pae_cutoff` (Å) | 10 | 10 | Dunbrack default |
 | ipSAE | `dist_cutoff` (Å) | 5 | 5 | Dunbrack default |
 | ABCFold | `-abc` | — | **-abc** | Three cofolders, one call |
@@ -193,6 +219,8 @@ python /workspace/ipsae/ipsae.py \
 | Chai-1 | NPZ | `per_chain_pair_iptm[binder, target]` | interface iPTM (not global `iptm`) |
 | AF2M | `<job>_summary_confidences.json` | `iptm`, `ranking_confidence`, `ptm` | top-rank model |
 | AF2M | `<job>_scores_rank_<k>.json` | `ptm`, `iptm`, `predicted_aligned_error` | per-model, for the 5-model spread |
+| OpenDDE | `<job>_summary_confidence_sample_<k>.json` | `chain_pair_iptm`, `iptm`, `ptm`, `gpde`, `ranking_score` | scalar and chain-pair confidence diagnostics |
+| OpenDDE | `<job>_full_data_sample_<k>.json` | `token_pair_pae`, `token_pair_pde`, `contact_probs` | sidecars for downstream interface-error scoring |
 | ipSAE | stdout / CSV | `ipSAE_d0chn`, `ipSAE_max`, mean interface PAE | post-hoc |
 
 ## Consensus Gate
@@ -243,6 +271,9 @@ A candidate that passes the first row but fails site or state checks should clos
 - **Boltz `--num_workers > 1` crashes on shared community hosts** with a CUDA `_cuda_init` driver-too-old race even when single-worker mode works. Force `--num_workers 1`.
 - **LocalColabFold's pixi installer is the current supported install path** (2026-01 pivot); the older `install_colabbatch_linux.sh` still works on Ubuntu 22.04.
 - **AF2-Multimer fetches its own MSAs** (per-job, ColabFold MMseqs2). Even if you have cached a Boltz / Chai `.a3m`, AF2M does not reuse it — rate-limit risk reappears on >20 jobs.
+- **OpenDDE package install currently needs source or Docker** as of the 2026-07-05 review; the advertised PyPI package route was not visible. Pin the source commit or Docker digest in provider packets.
+- **OpenDDE summary-only output is not enough for ipSAE-style gates.** Use `--need_atom_confidence true` and preserve `full_data`, then budget for larger artifacts.
+- **OpenDDE uses public MMseqs2 by default for protein MSA search.** Use precomputed A3M or a self-hosted `MMSEQS_SERVICE_HOST_URL` for private sequences or batch fanout.
 - **For peptide ligands with disordered or partly-cleaved N-termini** (common in GPCR-peptide complexes), expect interface ipSAE to be lower due to peptide flexibility. Not a bad model — a real biological feature. Calibrate the threshold against a known positive-control complex.
 
 ## Citations And References
@@ -257,6 +288,7 @@ A candidate that passes the first row but fails site or state checks should clos
 - Overath / Rygaard binder-presence floor: bioRxiv 2025.08.14.670059v2
 - DigBioLab reference pipeline (defines the consensus gate): https://github.com/DigBioLab/de_novo_binder_scoring
 - BindEnergyCraft / pTMEnergy (free upgrade on pAE logits): arXiv 2505.21241
+- OpenDDE: https://github.com/aurekaresearch/OpenDDE
 
 ## Currency Check (Run Before Reusing This Card)