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+}

NOTICE.md

@@ -0,0 +1,7 @@
+# Attribution and distribution notes
+
+The accompanying manuscript and implementation are a research-template deliverable for the Programmable Biology Group. Author ordering and the project's distribution license should be finalized by the authors before public release.
+
+External scientific model code and weights are obtained from their original repositories, subject to their own licenses. The archive does not redistribute those checkpoints. Pinned sources appear in `configs/model_registry.json`. The real-data sample is derived from ChatterjeeLab/SNOOPPI with exact source records in `examples/source_records.json`; retain the dataset's attribution and terms for redistribution.
+
+Ubuntu fonts include their Ubuntu Font Licence. Source Sans 3 fonts in the LaTeX archive include their SIL Open Font License. Model logos were supplied by the user and identify Meta/Muse, OpenAI, and Anthropic controller alternatives. These marks retain their original ownership.

README.md

@@ -1,3 +1,68 @@
 ---
-license: apache-2.0
+language:
+- en
+tags:
+- peptide-design
+- agents
+- multi-objective-optimization
+- therapeutic-peptides
 ---
+# PepPA
+
+**Peptide Planning and Prioritization Agent**
+
+PepPA coordinates multi-objective peptide generation and computational verification before final sequence commitment. The internal design loop uses PepDFM/MOG-DFM and PepMDLM/PepTune with SOAPIA, moPPIt, AlloGen, and PeptiVerse objectives. Outer checks cover AF3/docking/EvoBind structures, external pepADMET predictions, chemical identity, synthesizability, and CMC. Required human and preclinical contexts define robust generation objectives. Experimental characterization follows the frozen computational release.
+
+![PepPA workflow](assets/figures/figure1.png)
+
+This is a manuscript and reference implementation package. The CPU kernel runs deterministic dependency graphs, validates outputs, aggregates endpoint-compatible model predictions, and applies release checks. Native scientific workers and full benchmark experiments require the integrations listed in `docs/IMPLEMENTATION_STATUS.md`. Integration into the existing PeptiVerse Hugging Face Space is planned.
+
+## Install and verify
+
+```bash
+python -m venv .venv
+source .venv/bin/activate
+python -m pip install -e .
+python scripts/run_compiled_example.py
+python -m unittest discover -s tests -v
+```
+
+The example uses eight real, pinned SNOOPPI records. It preserves their interaction labels and demonstrates complete execution with an empty peptide release because required property predictions and chemical verification are absent. It runs no GPU model and invents no scores. The saved trace reproduces exactly from its cache. Thirty-four local tests cover execution, aggregation, release, scientific utilities, and the maximum-effort provider request settings. Synthetic values occur in explicitly labeled unit fixtures.
+
+## Computational formulation
+
+1. Define targets, countertargets, chemistry, human/preclinical contexts, endpoints, and budgets.
+2. Retrieve a fixed source snapshot and construct source-linked mechanistic hypotheses.
+3. Compile tool dependencies, requirements, model revisions, and conditional branches.
+4. Generate up to 192 proposals using native multi-objective frameworks.
+5. Evaluate required properties, structures, external checkers, and synthesis/CMC requirements.
+6. Apply the fixed diagnostic-to-redesign rule for at most 192 further proposals.
+7. Commit chemically defined sequences with complete context-specific reports and source records.
+
+The plan is frozen before execution. Every molecular change has a new identity and fresh scores. Experimental data belong in a separate evaluation dataset keyed to the frozen release hash.
+
+## Files
+
+- `src/peppa/compiled.py`: deterministic CPU reference kernel, lineage-aware stacking, robust reward, redesign schedule, and release checks.
+- `scripts/run_compiled_example.py`: real-source-data execution and incomplete-release example.
+- `results/compiled_trace.jsonl`, `results/compiled_release.json`: actual outputs from that example.
+- `docs/SOPHIE_RUNBOOK.md`: E0–E5 execution instructions, exact study allocations, controls, artifacts, and analysis.
+- `docs/IMPLEMENTATION_STATUS.md`: tested implementation and required native integrations.
+- `docs/WORKER_INTEGRATION.md`: scientific worker APIs and score contracts.
+- `docs/SPACE_DESIGN.md`: planned Muse-backed agent within the PeptiVerse Space.
+- `configs/study_plan.json`: synchronized computational and prospective study design.
+- `configs/model_registry.json`: pinned model and dataset revisions.
+- `schemas/PlanProposal.json`: language-model plan interface.
+- `schemas/`: existing chemical/worker record schemas.
+- `assets/figures/`: vector PDFs, editable SVGs, and PNG previews with Ubuntu labels.
+- `docs/references.bib`, `docs/PepPA.pdf`: bibliography and compiled manuscript.
+
+The original `peppa run` controller engine remains available for baseline/tool-adapter development. The primary frozen-plan formulation uses `peppa.compiled`; native worker and language-model compiler integration remains an explicit implementation stage. `results/real_data_trace.jsonl` is the source-ingestion example regenerated from the pinned source records.
+
+## Models and scientific workers
+
+Muse Glimmer-30B is the default open-weight backend. Its pinned identity is recorded in `configs/model_registry.json`. GPT-6 Astra Max and Claude Opus Max use the same PlanProposal schema. Provider credentials and exact serving revisions are supplied in the execution environment. The archive includes provider adapters but contains no live provider evaluation.
+
+moPPIt and PeptiVerse bridges follow the inspected native APIs. PepDFM/MOG-DFM, PepMDLM/PepTune, SOAPIA, AlloGen, AF3, docking, EvoBind, pepADMET, chemical standardization, and manufacturing checks require worker registration and known-input preflight. Every worker must report its input chemistry, native endpoint, units, target context, checkpoint, preprocessing, and training lineage.
+
+External code, weights, logos, and fonts retain their original rights. See `NOTICE.md`. The LaTeX project compiles with XeLaTeX, BibTeX, XeLaTeX, XeLaTeX.

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+-------------------------------
+UBUNTU FONT LICENCE Version 1.0
+-------------------------------
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+PREAMBLE
+This licence allows the licensed fonts to be used, studied, modified and
+redistributed freely. The fonts, including any derivative works, can be
+bundled, embedded, and redistributed provided the terms of this licence
+are met. The fonts and derivatives, however, cannot be released under
+any other licence. The requirement for fonts to remain under this
+licence does not require any document created using the fonts or their
+derivatives to be published under this licence, as long as the primary
+purpose of the document is not to be a vehicle for the distribution of
+the fonts.
+
+DEFINITIONS
+"Font Software" refers to the set of files released by the Copyright
+Holder(s) under this licence and clearly marked as such. This may
+include source files, build scripts and documentation.
+
+"Original Version" refers to the collection of Font Software components
+as received under this licence.
+
+"Modified Version" refers to any derivative made by adding to, deleting,
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+Original Version, by changing formats or by porting the Font Software to
+a new environment.
+
+"Copyright Holder(s)" refers to all individuals and companies who have a
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+To "Propagate" a work means to do anything with it that, without
+permission, would make you directly or secondarily liable for
+infringement under applicable copyright law, except executing it on a
+computer or modifying a private copy. Propagation includes copying,
+distribution (with or without modification and with or without charging
+a redistribution fee), making available to the public, and in some
+countries other activities as well.
+
+PERMISSION & CONDITIONS
+This licence does not grant any rights under trademark law and all such
+rights are reserved.
+
+Permission is hereby granted, free of charge, to any person obtaining a
+copy of the Font Software, to propagate the Font Software, subject to
+the below conditions:
+
+1) Each copy of the Font Software must contain the above copyright
+notice and this licence. These can be included either as stand-alone
+text files, human-readable headers or in the appropriate machine-
+readable metadata fields within text or binary files as long as those
+fields can be easily viewed by the user.
+
+2) The font name complies with the following:
+(a) The Original Version must retain its name, unmodified.
+(b) Modified Versions which are Substantially Changed must be renamed to
+avoid use of the name of the Original Version or similar names entirely.
+(c) Modified Versions which are not Substantially Changed must be
+renamed to both (i) retain the name of the Original Version and (ii) add
+additional naming elements to distinguish the Modified Version from the
+Original Version. The name of such Modified Versions must be the name of
+the Original Version, with "derivative X" where X represents the name of
+the new work, appended to that name.
+
+3) The name(s) of the Copyright Holder(s) and any contributor to the
+Font Software shall not be used to promote, endorse or advertise any
+Modified Version, except (i) as required by this licence, (ii) to
+acknowledge the contribution(s) of the Copyright Holder(s) or (iii) with
+their explicit written permission.
+
+4) The Font Software, modified or unmodified, in part or in whole, must
+be distributed entirely under this licence, and must not be distributed
+under any other licence. The requirement for fonts to remain under this
+licence does not affect any document created using the Font Software,
+except any version of the Font Software extracted from a document
+created using the Font Software may only be distributed under this
+licence.
+
+TERMINATION
+This licence becomes null and void if any of the above conditions are
+not met.
+
+DISCLAIMER
+THE FONT SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO ANY WARRANTIES OF
+MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT OF
+COPYRIGHT, PATENT, TRADEMARK, OR OTHER RIGHT. IN NO EVENT SHALL THE
+COPYRIGHT HOLDER BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY,
+INCLUDING ANY GENERAL, SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL
+DAMAGES, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
+FROM, OUT OF THE USE OR INABILITY TO USE THE FONT SOFTWARE OR FROM OTHER
+DEALINGS IN THE FONT SOFTWARE.

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configs/backend_comparisons.json

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+{
+  "muse": {
+    "label": "Muse Glimmer-30B",
+    "model_registry_key": "Muse"
+  },
+  "astra": {
+    "label": "GPT-6 Astra Max",
+    "model": "gpt-6-astra",
+    "reasoning_effort": "max",
+    "snapshot_revision": null
+  },
+  "claude": {
+    "label": "Claude Opus Max",
+    "model": "claude-opus-5",
+    "effort": "max",
+    "thinking": "adaptive",
+    "snapshot_revision": null
+  },
+  "snapshot_policy": "Resolve and record serving revisions before benchmark execution."
+}

configs/controller_astra.json

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+{
+  "type": "api",
+  "settings": {
+    "model": "gpt-6-astra",
+    "provider": "responses",
+    "api_key_env": "OPENAI_API_KEY",
+    "max_output_tokens": 64000,
+    "input_bytes": 14000,
+    "reasoning_effort": "max"
+  }
+}

configs/controller_claude.json

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+{
+  "type": "anthropic",
+  "settings": {
+    "model": "claude-opus-5",
+    "api_key_env": "ANTHROPIC_API_KEY",
+    "max_output_tokens": 64000,
+    "input_bytes": 14000,
+    "effort": "max"
+  }
+}

configs/controller_muse.json

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+{
+  "type": "api",
+  "settings": {
+    "model": "meta-models/Muse-Glimmer-30B",
+    "provider": "chat",
+    "base_url": "http://localhost:8000/v1",
+    "api_key_env": "MUSE_API_KEY",
+    "max_output_tokens": 2500,
+    "input_bytes": 14000
+  }
+}

configs/model_registry.json

@@ -0,0 +1,55 @@
+{
+  "moPPIt": {
+    "repo_id": "ChatterjeeLab/moPPIt",
+    "revision": "29de994bfb6c67890d0efca25d1cf431eea96999",
+    "repo_type": "model",
+    "source_url": "https://huggingface.co/ChatterjeeLab/moPPIt"
+  },
+  "MOG-DFM": {
+    "repo_id": "ChatterjeeLab/MOG-DFM",
+    "revision": "fe115ca03bb2dab2f5aac2f2fbaae56e65c876d6",
+    "repo_type": "model",
+    "source_url": "https://huggingface.co/ChatterjeeLab/MOG-DFM"
+  },
+  "PeptiVerse": {
+    "repo_id": "ChatterjeeLab/PeptiVerse",
+    "revision": "8cf0b21dae356278ae96b414a088e4360357d16c",
+    "repo_type": "model",
+    "source_url": "https://huggingface.co/ChatterjeeLab/PeptiVerse"
+  },
+  "PTM-Mamba": {
+    "repo_id": "ChatterjeeLab/PTM-Mamba",
+    "revision": "ad3dd0f8a770e34d9c56a0e71b264e6178dc8561",
+    "repo_type": "model",
+    "source_url": "https://huggingface.co/ChatterjeeLab/PTM-Mamba"
+  },
+  "AlloGen": {
+    "repo_id": "ChatterjeeLab/AlloGen",
+    "revision": "0720b696a23667724c4c108ff58fa9c96bec13fb",
+    "repo_type": "model",
+    "source_url": "https://huggingface.co/ChatterjeeLab/AlloGen"
+  },
+  "TD3B": {
+    "repo_id": "ChatterjeeLab/TD3B",
+    "revision": "ee96220e6df04b5dffe095b161d9f04b8cfe0db7",
+    "repo_type": "model",
+    "source_url": "https://huggingface.co/ChatterjeeLab/TD3B"
+  },
+  "PepMLM-650M": {
+    "repo_id": "ChatterjeeLab/PepMLM-650M",
+    "revision": "898fca941a9057aebdd1a6164b5ee09a1a71780e",
+    "repo_type": "model",
+    "source_url": "https://huggingface.co/ChatterjeeLab/PepMLM-650M"
+  },
+  "Muse": {
+    "repo_id": "meta-models/Muse-Glimmer-30B",
+    "revision": "a4e59da52a7bc87ae7251dd5545c0dd437c44b68",
+    "repo_type": "model",
+    "source_url": "https://huggingface.co/meta-models/Muse-Glimmer-30B"
+  },
+  "SNOOPPI": {
+    "repo_id": "ChatterjeeLab/SNOOPPI",
+    "revision": "a1c75633343e292db8bb25e3356d0b8cd64afa91",
+    "repo_type": "dataset"
+  }
+}

configs/registry_gpu.example.json

@@ -0,0 +1,69 @@
+{
+  "commands": [
+    {
+      "name": "generate_moppit",
+      "argv": [
+        "/opt/moppit/bin/python",
+        "-m",
+        "peppa.native_worker",
+        "--mode",
+        "moppit",
+        "--root",
+        "/models/moppit",
+        "--work",
+        "/results/moppit"
+      ],
+      "cwd": "/models/moppit",
+      "cost": {
+        "tool_calls": 1,
+        "gpu_minutes": 120
+      },
+      "description": "Generate a canonical linear peptide batch with pinned moPPIt.",
+      "timeout": 7200,
+      "argument_schema": {
+        "type": "object",
+        "properties": {
+          "length": {
+            "type": "integer",
+            "enum": [
+              12,
+              18,
+              24
+            ]
+          },
+          "count": {
+            "type": "integer",
+            "minimum": 1,
+            "maximum": 96
+          },
+          "objectives": {
+            "type": "array",
+            "items": {
+              "type": "string"
+            }
+          },
+          "weights": {
+            "type": "array",
+            "items": {
+              "type": "number",
+              "minimum": 0
+            }
+          },
+          "steps": {
+            "type": "integer",
+            "minimum": 1,
+            "maximum": 1000
+          },
+          "native_motif_indices": {
+            "type": "string"
+          }
+        },
+        "required": [
+          "length",
+          "count"
+        ],
+        "additionalProperties": false
+      }
+    }
+  ]
+}

configs/study_plan.json

@@ -0,0 +1,45 @@
+{
+  "version": "3",
+  "central_objective": "multi-objective generation and computational robustness before final sequence commitment",
+  "computational_cycles": 2,
+  "proposals_per_cycle": 192,
+  "structural_candidates_per_cycle": 18,
+  "gpu_hours": 24,
+  "core_conditions": [
+    "fixed",
+    "react_muse",
+    "compiled_reduced_checks",
+    "peppa_muse",
+    "peppa_astra",
+    "peppa_claude",
+    "peppa_no_retrieval"
+  ],
+  "tasks": 24,
+  "seeds": [
+    2027,
+    2028,
+    2029
+  ],
+  "core_episodes": 504,
+  "component_ablation_episodes": 432,
+  "fresh_compilations": 360,
+  "fault_episodes": 840,
+  "prospective_directions": [
+    "beta_catenin_over_p53",
+    "p53_over_beta_catenin",
+    "phospho_ERK2",
+    "VHL_P3F1",
+    "VHL_SS18_SSX1",
+    "VHL_EWS_FLI1"
+  ],
+  "prospective_conditions": [
+    "fixed",
+    "react_muse",
+    "peppa_muse"
+  ],
+  "slots_per_direction_condition": 18,
+  "nomination_slots": 324,
+  "shared_control_slots": 72,
+  "release_before_prospective_assays": true,
+  "experimental_data_destination": "separate_outcome_evaluation"
+}

docs/IMPLEMENTATION_STATUS.md

@@ -0,0 +1,28 @@
+# Implementation and validation status
+
+## Implemented and exercised
+
+`peppa.compiled` implements deterministic topological scheduling, immutable input copies, unique artifact producers, dependency and budget checks, revision checks, bounded attempts, atomic output validation/commit, content-addressed caching, hash-linked semantic traces, replay, duplicate-lineage invariance, nonnegative ridge stacking, worst-context generation reward, deterministic failure-category scheduling, and complete-score release checks.
+
+The reference kernel uses a scalar reserved-cost budget and Python validator functions. Worker-specific semantic preconditions, wall-clock timeouts, actual multi-resource accounting, and parallel execution are production-adapter responsibilities. The manuscript states the full scientific protocol. The CPU kernel is a tested reference for its scheduling and mathematical operations.
+
+The tests passed. Eight real SNOOPPI records were processed in `run_compiled_example.py`; original labels and source IDs were retained. Required PAMPA scores and chemical verification were absent, so the release report correctly contains zero nominated peptides. Cached execution and replay returned identical artifacts. This validates software behavior, not design efficacy.
+
+Existing utilities implement chemical annotations, AF3 PTM input export, masked-label PTM-head fitting, connected-component partitions, conformal radii, conservative margins, diversity selection, grouped bootstrap, and mass-balanced ternary fitting. Existing controller interfaces and the moPPIt/PeptiVerse bridges are retained for integration and comparative baselines.
+
+## Integration required before scientific evaluation
+
+- Connect Muse/GPT-6 Astra Max/Claude Opus Max structured outputs to PlanProposal validation and the compiled runner. The existing baseline provider adapters have not been run against live services here.
+- Register the native generator and scientific workers, with checkpoint and environment hashes. moPPIt/PeptiVerse bridges require native known-input tests. Other scientific tools have contracts and integration instructions.
+- Implement complete graph-based chemical standardization, noncanonical monomer mapping, formula/mass calculation, supplier/route retrieval, and manufacturing annotations. The CPU release routine consumes verified identity records from these adapters.
+- Fit ensemble weights and calibration on the declared disjoint data; curate structural predicate thresholds and target/species domain rules.
+- Compile the two-cycle scientific graph and its fixed diagnostic mapping. The robust objective and category scheduler are executable; native generator conditioning is an integration task.
+- Implement timeout enforcement, multiple resource budgets, scientific validators, full report export, and held-out benchmark evaluation around the reference kernel.
+
+No new therapeutic peptide generation, GPU structural prediction, prospective assay, animal study, or clinical evaluation was executed while authoring this archive. The tables and empirical plots are pending. The Hugging Face Space is specified, not deployed.
+
+## Trace interpretation
+
+`compiled_trace.jsonl` is the current deterministic source-data example. `real_data_trace.jsonl` belongs to the earlier generic controller example. Neither contains newly measured biology. Synthetic unit tests are separate from these real-data examples. A candidate's source interaction label must never be copied into an unmeasured property endpoint.
+
+The v4 revision also checks Astra maximum reasoning and Opus maximum effort/adaptive thinking request settings using local provider mocks. Live provider calls and scientific model benchmarking remain pending.

docs/PepPA.pdf

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+size 267684

docs/REFERENCE_SOURCES.tsv

@@ -0,0 +1,75 @@
+key	type	doi	primary_source	checked
+virtualLab	article	10.1038/s41586-025-09442-9	https://doi.org/10.1038/s41586-025-09442-9	2026-09-20
+biomni	article	10.1126/science.adz4351	https://doi.org/10.1126/science.adz4351	2026-09-20
+crisprgpt	article	10.1038/s41551-025-01463-z	https://doi.org/10.1038/s41551-025-01463-z	2026-09-20
+cellvoyager	article	10.1038/s41592-026-03029-6	https://doi.org/10.1038/s41592-026-03029-6	2026-09-20
+chemcrow	article	10.1038/s42256-024-00832-8	https://doi.org/10.1038/s42256-024-00832-8	2026-09-20
+coscientist	article	10.1038/s41586-023-06792-0	https://doi.org/10.1038/s41586-023-06792-0	2026-09-20
+drugassist	article	10.1093/bib/bbae693	https://doi.org/10.1093/bib/bbae693	2026-09-20
+peptiverse	article	10.1038/s41467-026-74167-w	https://doi.org/10.1038/s41467-026-74167-w	2026-09-20
+ptmmamba	article	10.1038/s41592-025-02656-9	https://doi.org/10.1038/s41592-025-02656-9	2026-09-20
+moppit	article	10.1101/2024.07.31.606098	https://doi.org/10.1101/2024.07.31.606098	2026-09-20
+pepprclip	article	10.1126/sciadv.adr8638	https://doi.org/10.1126/sciadv.adr8638	2026-09-20
+saltpeppr	article	10.1038/s42003-023-05464-z	https://doi.org/10.1038/s42003-023-05464-z	2026-09-20
+evobind2	article	10.1038/s42004-025-01601-3	https://doi.org/10.1038/s42004-025-01601-3	2026-09-20
+evomultimer	article	10.64898/2026.08.21.746227	https://doi.org/10.64898/2026.08.21.746227	2026-09-20
+pepadmet	article	10.1021/acs.jcim.5c02518	https://doi.org/10.1021/acs.jcim.5c02518	2026-09-20
+af3	article	10.1038/s41586-024-07487-w	https://doi.org/10.1038/s41586-024-07487-w	2026-09-20
+af2	article	10.1038/s41586-021-03819-2	https://doi.org/10.1038/s41586-021-03819-2	2026-09-20
+esm2	article	10.1126/science.ade2574	https://doi.org/10.1126/science.ade2574	2026-09-20
+proteinmpnn	article	10.1126/science.add2187	https://doi.org/10.1126/science.add2187	2026-09-20
+rfdiffusion	article	10.1038/s41586-023-06415-8	https://doi.org/10.1038/s41586-023-06415-8	2026-09-20
+bindcraft	article	10.1038/s41586-025-09429-6	https://doi.org/10.1038/s41586-025-09429-6	2026-09-20
+gadd	article	10.1038/nchembio.2329	https://doi.org/10.1038/nchembio.2329	2026-09-20
+macroglue	article	10.1002/anie.202521678	https://doi.org/10.1002/anie.202521678	2026-09-20
+fkbpglue	article	10.1039/d4cb00279b	https://doi.org/10.1039/d4cb00279b	2026-09-20
+intrinsicAlpha	article	10.3390/ijms26072936	https://doi.org/10.3390/ijms26072936	2026-09-20
+phosphosite	article	10.1093/nar/gku1267	https://doi.org/10.1093/nar/gku1267	2026-09-20
+uniprot	article	10.1093/nar/gkae1010	https://doi.org/10.1093/nar/gkae1010	2026-09-20
+intact	article	10.1093/nar/gkab1006	https://doi.org/10.1093/nar/gkab1006	2026-09-20
+biogrid	article	10.1002/pro.3978	https://doi.org/10.1002/pro.3978	2026-09-20
+negatome	article	10.1093/nar/gkt1079	https://doi.org/10.1093/nar/gkt1079	2026-09-20
+peptides	article	10.1038/s41573-020-00135-8	https://doi.org/10.1038/s41573-020-00135-8	2026-09-20
+guacamol	article	10.1021/acs.jcim.8b00839	https://doi.org/10.1021/acs.jcim.8b00839	2026-09-20
+colabfold	article	10.1038/s41592-022-01488-1	https://doi.org/10.1038/s41592-022-01488-1	2026-09-20
+bh	article	10.1111/j.2517-6161.1995.tb02031.x	https://doi.org/10.1111/j.2517-6161.1995.tb02031.x	2026-09-20
+parego	article	10.1109/TEVC.2005.851274	https://doi.org/10.1109/TEVC.2005.851274	2026-09-20
+dscript	article	10.1016/j.cels.2021.08.010	https://doi.org/10.1016/j.cels.2021.08.010	2026-09-20
+gat2024discreteflowmatching	inproceedings	10.52202/079017-4239	https://proceedings.neurips.cc/paper_files/paper/2024/hash/f0d629a734b56a642701bba7bc8bb3ed-Abstract-Conference.html	2026-09-20
+cbas	inproceedings		https://proceedings.mlr.press/v97/brookes19a.html	2026-09-20
+coms	inproceedings		https://proceedings.mlr.press/v139/trabucco21a.html	2026-09-20
+designbench	inproceedings		https://proceedings.mlr.press/v162/trabucco22a.html	2026-09-20
+mmseqs	article		supplied manuscript	2026-09-20
+mogdfm	misc		https://arxiv.org/abs/2505.07086	2026-09-20
+allogen	misc		https://arxiv.org/abs/2606.05474	2026-09-20
+peptune	misc		https://arxiv.org/abs/2412.17780	2026-09-20
+react	misc		https://arxiv.org/abs/2210.03629	2026-09-20
+reflexion	misc		https://arxiv.org/abs/2303.11366	2026-09-20
+paperqa	misc		https://arxiv.org/abs/2409.13740	2026-09-20
+conformal	misc		https://arxiv.org/abs/2107.07511	2026-09-20
+biodiscovery	misc		https://arxiv.org/abs/2405.17631	2026-09-20
+soapia	misc		supplied manuscript	2026-09-20
+td3b	misc		https://huggingface.co/ChatterjeeLab/TD3B	2026-09-20
+snooppi	misc	10.57967/hf/10413	https://huggingface.co/datasets/ChatterjeeLab/SNOOPPI	2026-09-20
+muse	misc		https://huggingface.co/meta-models/Muse-Glimmer-30B	2026-09-20
+pepmlm	article	10.1038/s41587-025-02761-2	https://doi.org/10.1038/s41587-025-02761-2	2026-09-20
+lapep	misc		supplied manuscript	2026-09-20
+duab	article	10.1038/s41467-025-58872-6	https://doi.org/10.1038/s41467-025-58872-6	2026-09-20
+peptune	misc		https://arxiv.org/html/2412.17780v3	2026-09-20
+lab_instruments	misc		https://cem.com/peptide-synthesis	2026-09-20
+llmcompiler	misc		https://arxiv.org/html/2312.04511v3	2026-09-20
+llmp	misc		https://arxiv.org/html/2304.11477v3	2026-09-20
+taubench	misc		https://arxiv.org/abs/2406.12045	2026-09-20
+agentbench	misc		https://arxiv.org/abs/2308.03688	2026-09-20
+hongreview	article	10.1021/acs.biochem.6c00138	https://doi.org/10.1021/acs.biochem.6c00138	2026-09-20
+therapeutic2022	article	10.1038/s41392-022-00904-4	https://doi.org/10.1038/s41392-022-00904-4	2026-09-20
+membranemacrocycles	article	10.1016/j.cell.2022.07.019	https://doi.org/10.1016/j.cell.2022.07.019	2026-09-20
+rfpeptides	article	10.1038/s41589-025-01929-w	https://doi.org/10.1038/s41589-025-01929-w	2026-09-20
+afcycdesign	article	10.1038/s41467-025-59940-7	https://doi.org/10.1038/s41467-025-59940-7	2026-09-20
+pepinvent	article	10.1039/d4sc07642g	https://doi.org/10.1039/d4sc07642g	2026-09-20
+helmgpt	article	10.1093/bioinformatics/btae364	https://doi.org/10.1093/bioinformatics/btae364	2026-09-20
+boltzgen	article	10.1101/2025.11.20.689494	https://doi.org/10.1101/2025.11.20.689494	2026-09-20
+erkdarpins	article	10.1073/pnas.1205399109	https://doi.org/10.1073/pnas.1205399109	2026-09-20
+peptiversespace	misc		https://huggingface.co/spaces/ChatterjeeLab/PeptiVerse	2026-09-20
+astramodel	misc		https://developers.openai.com/api/docs/models/gpt-6-astra	2026-09-20
+claudeeffort	misc		https://platform.claude.com/docs/en/build-with-claude/effort	2026-09-20

docs/SOPHIE_RUNBOOK.md

@@ -0,0 +1,97 @@
+# Sophie’s PepPA execution runbook
+
+The scientific objective is to balance therapeutic properties during generation, verify robustness computationally across the intended development contexts, and commit to final sequences before experimental characterization. The two computational proposal cycles are complete before any prospective assay data are collected.
+
+## E0 — Scientific worker preflight
+
+Install the CPU package and run:
+
+```bash
+python -m pip install -e .
+python scripts/run_compiled_example.py
+python -m unittest discover -s tests -v
+```
+
+Use `docs/WORKER_INTEGRATION.md` and `configs/model_registry.json` to install each native worker separately. Freeze code, checkpoint, tokenizer, preprocessing, input chemistry, software environment, and hardware. Run one known input and one deliberately invalid input through every worker. Save native output, normalized output, timing, model lineage, and validation diagnostics under `runs/preflight/TOOL/`. Verify moPPIt motif-index conventions, AlloGen state labels, PeptiVerse units/uncertainty, pepADMET domains, and explicit PTM/bond handling for AF3. A sequence-changing EvoBind run must create a new candidate.
+
+Complete chemical standardization and monomer mapping before release. For each allowed residue/closure, verify stereochemistry, graph identity, formula, mass, protecting groups, monomer source, and route annotation. Have the synthesis lead review the permitted chemistry registry once before the comparative runs.
+
+Artifact: `registry.lock.json` with known-input reports and scientific validators. Resolve every required worker before the corresponding task is admitted.
+
+## E1 — Data, task, and development profile freeze
+
+Curate eight affinity/selectivity, eight motif, and eight conformation tasks. A ranking pool must have the measurements needed for its declared evaluation; retain endpoint masks. Record any task-category shortfall before running comparisons. Add human and preclinical target contexts, countertargets, isoforms/states, route, and formulation requirements. For xenografts, separate the human tumor target from host-species exposure and off-target contexts.
+
+Cluster proteins at 30% identity with 80% bidirectional coverage and peptides at 80% global identity. Join records sharing target cluster, peptide cluster, or primary publication. Assign connected components to 70% model fit, 10% ensemble fit, 10% calibration, and 10% test. Record actual sizes. Freeze labels and test-outcome passages away from the agent's inputs. Use a time cutoff for retrieved literature and record pretrained-model exposure separately.
+
+Two authors annotate constructs, residue maps, mechanisms, required tools, endpoint definitions, source support/opposition, and counterexamples. Adjudicate disagreements. Up to three hypotheses per task are allowed. Native structural thresholds are selected on mechanistically annotated validation positives/decoys at a prespecified 5% false-positive operating point, with sensitivity and sample size retained. Endpoint property thresholds and species applicability are written explicitly in each task. A small or missing validation set receives an unresolved status and does not support a calibrated claim.
+
+Use six query templates: target + peptide binding; target + binding motif; target + selectivity/countertargets; target + state/modification; target + mechanism/partner; target + species/developability. Retain at most ten records per template, DOI/accession deduplication, exact passages, and source dates.
+
+Ensemble fitting: deduplicate identical checkpoint/input/preprocessing evaluations. Fit compatible endpoints separately, choose ridge strength from {0.001, 0.01, 0.1, 1} by grouped cross-validation, then calibrate on the untouched calibration partition. Use total marginal error allocation 0.1 across required calibrated endpoints. Save prediction matrices and model membership. Evaluate selected-distribution coverage separately.
+
+Artifacts: `tasks.jsonl`, `sources.jsonl`, `partitions.json`, `context_profiles.json`, `ensemble_fit/`, `calibration/`, `thresholds.json`, `registry.lock.json`.
+
+## E2 — Computational comparisons
+
+Seven core conditions: fixed workflow; ReAct/Muse; compiled/reduced checks; full PepPA/Muse; full PepPA/GPT-6 Astra Max; full PepPA/Claude Opus Max; full PepPA/Muse without retrieval. With 24 tasks and seeds {2027,2028,2029}, run 504 episodes. The same task/seed pair shares tools, raw proposal allowance, structural cap, and GPU budget.
+
+Per episode: two proposal cycles of 192 raw outputs, 384 total; at most 18 structurally evaluated candidates per cycle, 36 total; 24 GPU-hours on the declared hardware. Canonical primary comparisons use 18 residues. Run 12- and 24-residue transfer strata separately. Each candidate/context structure uses three model seeds and five diffusion samples per seed. Native model/search hyperparameters must be pinned before the comparison.
+
+Divide proposal slots equally among compatible generators, then accepted hypotheses, using stable round-robin allocation. Four objective vectors are balanced and affinity-, specificity-, and developability-emphasized. For K objective groups, balanced weights are 1/K; each emphasized vector assigns 0.6 to its selected group and 0.4/(K-1) to each other group. Normalize subobjective weights within each group. For K=1, use weight one. Preserve the weights and ordering in the plan.
+
+The outer checker evaluates structures/docking, external ADMET, chemistry, synthesis, and CMC. Classify first-cycle failures as binding, specificity, exposure/developability, structural, or chemical-manufacturing. Assign second-cycle quotas proportional to (failure count + 1). Round by largest remainder, using category IDs for ties. Visit categories in decreasing failure-count order and emit slots until their quotas are exhausted. Each category maps to a frozen objective vector and permitted chemistry transformations. Every modified identity is rescored. Store both cycle reports and the final release.
+
+Additional experiments: six component ablations × 24 tasks × three seeds = 432 episodes. Five fresh compilations × 24 tasks × three backends = 360 planning runs. Twelve fault classes × ten fixtures × seven conditions = 840 fault episodes. Reuse cached scientific outputs for the fault suite. Compare single-context with multi-context guidance using paired seeds and all other settings fixed.
+
+Metrics: exact requirement recovery; executable graph rate; complete-report rate; precision@12 and recovery@12 on labeled pools; nominal and selected-set coverage; worst-context feasibility; canonical/noncanonical validity; duplicate-lineage rank change; diversity; false release; success on all five repeated compilations; wall time/tokens/GPU use. For fresh runs report rank Jaccard, score deviation, and release agreement; cached replay requires identical hashes.
+
+Artifacts: one directory per task/condition/seed with frozen plan, source snapshot, proposals, scores, structures, failures, resource ledger, trace, and release. Figures 2 and the aggregation figure use these artifacts. Keep every empirical cell blank until its source analysis is available.
+
+## E3 — Final prospective commitment
+
+Six directions: β-catenin over p53; p53 over β-catenin; phospho-ERK2 over unmodified ERK2; VHL recruitment to PAX3::FOXO1, SS18::SSX1, and EWS::FLI1. Three conditions: fixed workflow, ReAct/Muse, full PepPA/Muse. Each direction/condition has 18 slots, giving 324 nominations. Twelve shared control slots per direction give 72 controls. Include at least 12 canonical slots and up to six noncanonical analogues where the corresponding workers support the chemistry. Each analogue occupies its own slot.
+
+Freeze the complete peptide identities, ranking, predicted scores, hypothesis/context matrix, and assay allocations. Hash the bundle. Keep empty slots and failed candidates visible; do not silently replace them after screening. Phage characterization precedes free-peptide assays for ERK2 and ternary tasks, but the full frozen synthesis panel is already committed.
+
+Each synthesis record contains monomer order, stereochemistry, termini, closure atoms, formula/mass, proposed route, requested amount, analytical method, and buffer/handling requirements. Use Liberty Blue synthesis, RAZR/Razor cleavage, and Prodigy purification. Require ≥95% analytical HPLC purity and expected LC–MS identity for assay-ready material. Record failed synthesis and recovery against the original nomination denominator.
+
+## E4 — Experimental characterization
+
+### Reciprocal binding and regulation
+
+Freeze full-length β-catenin, its N-terminal IDR boundaries, isolated IDR, IDR-deletion construct, full-length p53, tags, and sequence maps. BLI measures each β-catenin-directed peptide against all four proteins and the reciprocal p53 guides against p53 and β-catenin. Use 12 concentrations over 0.1 nM–10 µM, reference sensors, blanks, matched loading, and a second orientation or solution competition. Fit kinetic or registered equilibrium models with residual inspection. Criteria: target KD ≤100 nM, ≥10-fold countertarget discrimination; IDR binding also requires ≥10-fold affinity loss after deletion and retained isolated-IDR binding.
+
+Insert canonical β-catenin guides into peptide–GSGSG–CHIPΔTPR in the published pcDNA3 uAb backbone. Sequence-verify the guide, linker, and effector. Insert reciprocal p53 guides into peptide–GSGSG–OTUB1 catalytic-domain duAbs. Use DLD1 for β-catenin and HeLa for p53. Confirm target and countertarget alleles and include the expressed sequences in the computational context panel before generation. Freeze construct boundaries, promoter, DNA dose, cell density, and delivery conditions after a reference-construct pilot using the supplied SaLT&PepPr, PepPrCLIP, and duAb protocols. Include non-targeting, effector-only, catalytically inactive CHIP, OTUB1 C91S, OTUB1 D88A/C91S/H265A, and PR-619 controls. Collect both protein bands at 24, 48, and 72 h, effector expression, viability, turnover/ubiquitination, and transcript readouts. Quantify equal-protein whole-cell lysates with BCA, SDS–PAGE, membrane transfer, and unsaturated target/countertarget/effector images. Normalize background-subtracted bands to total-lane protein and the matched non-targeting control. Keep compartment-specific measurements separate. Fix the primary molecular time point in the reference-construct pilot and record later TOPFlash/FOPFlash, p53 transcription, and PARP-cleavage responses as functional outcomes. Primary uAb criterion: ≥50% β-catenin loss with p53 within 20% of control. Primary duAb criterion: ≥2-fold p53 increase with β-catenin within 20% of control; viability ≥80%.
+
+### Peptide properties
+
+PAMPA: 10 µM donor, pH 7.4, 25°C, 4 h; freeze membrane lipid, area, volumes, and cosolvent. Quantify both compartments by peptide-calibrated LC–MS. Retain membrane retention and mass recovery. Use the manuscript equation, require 80–120% recovery for quantitative interpretation, and preserve detection bounds. Proposed success threshold: Pe ≥10^-6 cm/s.
+
+Solubility: supernatant concentration after 24 h in the registered buffer; threshold ≥10 µM. Stability: intact peptide at 0, 15, 30, 60, 120, and 240 min at 37°C in 50% human serum; threshold half-life ≥60 min. Add matched preclinical-species serum and formulation conditions for the declared transfer analysis. Fit exponential loss with diagnostics. Keep PAMPA, cellular uptake, intracellular exposure, and systemic exposure as separate endpoints.
+
+### Phospho-ERK2
+
+Use occupancy-verified pThr185/pTyr187 and matched unmodified WT-sequence ERK2. Common low-valency pIII display and three independent phage selections, each with three panning rounds; retain input/display/amplification controls, tag-only controls, and phosphatase-treated target. Sequence all pools. Analyze differential enrichment with pseudocount one and library-size normalization as in Methods. Test the complete frozen peptide panel by paired BLI. Criteria: phospho-KD ≤100 nM and ≥10-fold preference. Verify occupancy before and after binding assays. Cellular pull-downs quantify phospho- and total ERK2 normalized to input with pathway activation/inhibition and scrambled controls.
+
+### VHL ternary assembly and degradation
+
+Freeze P3F1, SS18::SSX1, and EWS::FLI1 breakpoint sequences and parental countertargets. Use VHL–Elongin B–Elongin C. Phage co-capture uses both partners, each alone, each omitted, tag-only, non-targeting, and interface-competition controls in reciprocal capture orientations. Record valency, protein concentrations, input and output counts.
+
+Measure free-peptide binary KD by BLI and ternary concentration matrices by a calibrated proximity assay, such as TR-FRET between labeled target and VHL complex. Include donor-only, acceptor-only, tag-matched, and partner-omission controls and confirm assembly by reciprocal co-capture. Fit the mass-balanced equilibrium model only with identifiable binary constants and calibrated observation model. Retain hook-effect data and interval/identifiability diagnostics. Use RH4 for P3F1, HS-SY-II for SS18::SSX1, and A673 for EWS::FLI1, with confirmed breakpoints and VHL expression. Dose eight concentrations spanning 1 nM–10 µM at 2, 6, and 24 h. Quantify fusion, parents, loading, intracellular peptide exposure, target engagement, and viability. Criterion: ≥50% degradation at ≤1 µM with parental proteins within 20% of control and viability ≥80%. Test proteasome/neddylation inhibition, VHL competition or depletion, target ubiquitination, and target transcripts.
+
+Use three independent assay days, phage selections, or cellular preparations. Average technical triplicates within the independent unit. Randomize by assay-day block and blind condition identities. Retain full unsaturated western blots and raw curves.
+
+## E5 — Outcome analysis
+
+Link experimental records to the frozen release hash in a separate evaluation directory. Report nomination-slot success as primary, interpretable-measurement success as secondary. Separate binding, PTM discrimination, ternary assembly, permeability, and cellular regulation. Use equal task weights, average seeds within task, and 2,000 task-bootstrap resamples for 95% intervals. Report all losses, unfilled slots, synthesis failures, censored values, and missing endpoints.
+
+The six prospective directions are exploratory at this allocation. Later animal pharmacology, toxicology, formulation/scale-up, and clinical studies require their own designs and evidence. Preserve the same committed molecular identity when assessing transfer; document any subsequently necessary redesign as a new development candidate.
+
+## Minimum candidate bundle
+
+`candidates.jsonl`, `scores.csv`, `contexts.json`, `synthesis_manifest.csv`, `structures/`, `evidence.jsonl`, `plan.lock.json`, `model_registry.lock.json`, `calibration_manifest.json`, `trace.jsonl`, `failures.jsonl`, `resource_use.json`, and `checksums.json`. Every score includes endpoint/unit/context, native value, transformed value, interval or structural range, support type, applicability, model lineage, and source artifact. Every required metric must be populated before release.
+
+## Controller settings
+
+Compare Muse Glimmer-30B with GPT-6 Astra Max and Claude Opus Max. The latter two use `gpt-6-astra` and `claude-opus-5` with maximum reasoning effort. Freeze the exact serving snapshot, output-token cap, input context, and total token budget before evaluation. Save returned model IDs and provider request settings. The adapter tests verify request settings with local mocks; live model comparisons remain part of E2.

docs/SPACE_DESIGN.md

@@ -0,0 +1,11 @@
+# PepPA integration into the PeptiVerse Space
+
+The existing [PeptiVerse Space](https://huggingface.co/spaces/ChatterjeeLab/PeptiVerse) is the planned host for the PepPA workflow. The agent interface accepts a therapeutic specification with exact targets, countertargets, chemistry, human/preclinical contexts, desired properties, and budgets. Muse Glimmer-30B produces a PlanProposal against the pinned scientific registry and frozen evidence snapshot. Schema and scientific validation precede execution.
+
+The interface displays the compiled computational graph, generation progress, external checker statuses, remaining budget, and complete candidate reports. Users can download sequences, monomer/SMILES annotations, synthesis metadata, context-specific scores, structures, sources, failure records, and a replayable trace. The internal generator and outer computational checker loop are separate views. The completed output is an immutable candidate bundle.
+
+GPU scientific workers run in pinned native environments behind typed adapters. Secrets remain server-side. Cached runs are identified by the full input and revision hash. Scientific release requires configured endpoint calibration and chemical-domain support. A development context with incomplete required inputs returns diagnostics.
+
+The experimental evaluation dataset is linked to a completed release by its bundle hash. The Space design is prospective; no Space has been deployed in this package.
+
+Muse Glimmer-30B is the default backend. Comparative runs use GPT-6 Astra Max and Claude Opus Max through the same plan schema. These labels correspond to GPT-6 Astra and Claude Opus 5 with maximum reasoning effort. Exact provider IDs, returned revisions, token caps, and prompt versions are saved per run. The existing PeptiVerse interface remains the starting point for the planned agent integration.

docs/WORKER_INTEGRATION.md

@@ -0,0 +1,70 @@
+# Native scientific worker integration
+
+Run workers in pinned native environments. Install PepPA without replacing their scientific dependencies. Registered commands receive request and result paths. Save persistent artifacts outside the temporary request directory. Return `schemas/ToolResult.json`, with a sidecar recording command, revision, seed, hashes, runtime, GPU allocation, and score conventions.
+
+## moPPIt / PepDFM
+
+The inspected revision uses `moppit.py`; older examples mention `moo.py`. The supplied bridge uses the actual entrypoint, sets Python/NumPy/Torch seeds, uses `n_samples=1`, and obtains batches through `n_batches`. Objective names and weights follow native scorer initialization order. The entrypoint fixes step size to 1/100, so the bridge requires 100 steps. It exports canonical linear proposals. Modified and cyclic candidates need a compatible worker. Save the conversion from one-based canonical motif positions to native indices. Native CSV scores require endpoint calibration before conservative ranking.
+
+## PeptiVerse
+
+The inspected API is `PeptiVersePredictor(manifest_path, classifier_weight_root, device=...)`. Calls use `predict_property(prop_key, col='wt', input_str=...)` and `predict_binding_affinity(col='wt', target_seq=..., binder_str=...)`. The bridge retains native JSON outputs. SMILES endpoints require a chemistry-compatible route. Native uncertainty may be entropy, ensemble variation, or an interval. Verify the endpoint-specific definition.
+
+## PTM-Mamba
+
+Use the native package at https://github.com/programmablebio/ptm-mamba and its declared checkpoint. The inspected example imports `PTMMamba` from `protein_lm.modeling.scripts.infer` and uses explicit tokens such as `M<N-acetylalanine>K`. Verify the exact vocabulary for each PTM. Save `hidden_states` with a fixed pooling rule, chemical identity, token map, checkpoint hash, and partition. Align target features to frozen ESM-2 peptide features before fitting the supplied head. PTM-specific interaction labels supervise binding prediction.
+
+## SOAPIA / SiameseCat
+
+Supply the lab's compatible checkpoint and native two-track preprocessing. Return target and countertarget distances, beta, margin, and specificity contrast. Preserve unknown labels separately from negatives. The supplied PTM bilinear head is a separate supervised adapter, with paired chemical-state labels.
+
+## AlloGen
+
+At the pinned revision the documented API is:
+
+```python
+from models.differentiable_features import DifferentiableQTheta
+scorer = DifferentiableQTheta(checkpoint=checkpoint_path, device='cuda:0')
+scorer.load_receptor(holo_path='holo.pdb', rec_chain='A',
+                     apo_path='apo.pdb', apo_chain='A')
+q_holo = scorer.score('design.pdb', binder_chain='B', state='holo')
+q_apo = scorer.score('design.pdb', binder_chain='B', state='apo')
+```
+
+Run with the native `code` directory on the Python path. Retain both state scores and their difference. Check input-feature support for the requested PTM chemistry.
+
+## TD3B
+
+Load the trained Direction Oracle with native target representation and tokenizer. The inspected implementation returns probability of agonism in [0,1]. Its gate is `sigmoid(d * (p_agonist - 0.5) / temperature)`, with +1 for agonism and -1 for antagonism. Require successful trained-checkpoint loading. The source includes a neutral placeholder mode, which must be rejected during evaluation.
+
+## AF3
+
+`peppa.structure.af3_input` exports explicit CCD modifications and atom-level bonds using one-based construct positions. Run the native predictor separately and retain the input hash, chain-pair confidence, and all structures. The planned protocol uses three seeds and five samples per seed. Preserve the candidate identity across matched PTM inputs and ternary inputs. Report both peptide interfaces.
+
+## EvoBind2 / EvoBind-Multimer
+
+Use native sequence-optimizing protocols. Molecular-glue sources are https://doi.org/10.64898/2026.08.21.746227 and https://github.com/patrickbryant1/EvoBind-multimer . Any changed sequence becomes a new candidate with a parent ID and fresh evaluations. Fixed-sequence AF3 validation is a distinct operation. Pin code, model parameters, templates, and candidate counts. Preserve upstream licensing.
+
+
+## PepDFM, PepMDLM, and PepTune
+
+Register a generator worker under the shared CommandTool interface. PepDFM and MOG-DFM use the native sequence sampler and checkpoint from the supplied MOG-DFM study. PepMDLM uses the peptide-SMILES masked-diffusion sampler described in https://arxiv.org/abs/2412.17780. PepTune uses its native multi-objective search procedure. Freeze the exact repository, checkpoint, tokenizer, chemistry parser, decoder settings, and objective implementations before evaluation. These generator workers require native integration and preflight; they are not bundled model implementations.
+
+Each generator returns exact candidate chemistry, seed, parent ID when applicable, attempted and valid candidate counts, objective weights, native scores, elapsed GPU minutes, and source artifacts. A SMILES worker must serialize stereochemistry, monomers, terminal groups, and closure bonds into the Candidate representation. Use the same eligible generator portfolio in each controller comparison. Freeze chemistry and proposal allocation; compare generator routes in matched strata.
+
+## Ranking and experimental acceptance
+
+Keep two explicit lists in the frozen task manifest: `score_requirements` for calibrated computational component scores and `experimental_requirements` for measured success. Pass the first list to `candidate_score`; pass the second to `joint_success`. This permits optimization of binding, properties, and interface proxies before a PTM-selectivity or ternary-cooperativity measurement exists. Preserve the lists and their hashes in artifacts and run metadata. A native property probability must retain its actual endpoint definition.
+
+
+## Frozen computational workflow
+
+Connect native workers to `peppa.compiled` through registry callables with revision, run, and scientific validate functions. Use baseline `CommandTool` wrappers as process adapters. Unroll the two proposal cycles and all conditional computation before execution. Mandatory outputs include context-specific scores, lineage and applicability metadata, chemistry verification, and release diagnostics. Production adapters must enforce timeout and GPU/CPU/memory budgets.
+
+## pepADMET, docking, and CMC
+
+Pin native pepADMET code/checkpoint and define endpoint-specific representation/domain checks. Preserve predictions independently of PeptiVerse, then compare or aggregate only matched endpoints. Record shared data ancestry. Docking contracts include preparation, protonation, search region, seeds, scoring function, and full pose outputs. CMC contracts include structure-derived formula/mass, monomer availability with dated source, route/closure/protection annotations, coupling liabilities, aggregation/chemical stability flags, and analytical specifications. These adapters require implementation and known-input preflight.
+
+## Prospective outcome analysis
+
+Experimental assays are imported into a separate evaluation dataset keyed to the immutable candidate-release hash. Aggregate replicates with units, censoring, assay context, and raw artifact identifiers and sources. Preserve expressed uAb/duAb constructs separately from free-peptide dosing. Compute experimental success against the preregistered assay requirements after computational commitment.

docs/figure_manifest.json

@@ -0,0 +1,20 @@
+{
+  "figure1": "computational workflow with internal generation, outer checks and final commitment",
+  "figure2": "empty computational benchmark panels",
+  "figure3": "empty reciprocal binding/property/cell panels",
+  "figure4": "empty aggregation and ablation panels",
+  "figure5": "empty ERK2 panels",
+  "figure6": "empty ternary/cell panels",
+  "empirical_results": "pending",
+  "font": "Ubuntu",
+  "pink": "#FBE8EF",
+  "navy": "#011F5B",
+  "paper_number_to_asset": {
+    "1": "figure1",
+    "2": "figure2",
+    "3": "figure4",
+    "4": "figure3",
+    "5": "figure5",
+    "6": "figure6"
+  }
+}

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+
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+  title = {{Design of linear and cyclic peptide binders from protein sequence information}},
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+}
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+  title = {{De novo
+                  Design of Macrocyclic Molecular Glues}},
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+  year = {2026},
+  journal = {Preprint},
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+}
+
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+}
+
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+}
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+}
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+
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+}
+
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+  title = {{PhosphoSitePlus, 2014: mutations, PTMs and recalibrations}},
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+}
+
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+  title = {{UniProt: the Universal Protein Knowledgebase in 2025}},
+  author = {{The UniProt Consortium}},
+  year = {2024},
+  journal = {Nucleic Acids Research},
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+}
+
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+  journal = {Nucleic Acids Research},
+  volume = {50},
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+}
+
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+  title = {{The
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+}
+
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+  title = {{Negatome 2.0: a database of non-interacting proteins derived by literature mining, manual annotation and protein structure analysis}},
+  author = {Blohm, Philipp and Frishman, Goar and Smialowski, Pawel and Goebels, Florian and Wachinger, Benedikt and Ruepp, Andreas and Frishman, Dmitrij},
+  year = {2013},
+  journal = {Nucleic Acids Research},
+  volume = {42},
+  number = {D1},
+  pages = {D396-D400},
+  doi = {10.1093/nar/gkt1079},
+  url = {https://doi.org/10.1093/nar/gkt1079}
+}
+
+@article{peptides,
+  title = {{Trends in peptide drug discovery}},
+  author = {Muttenthaler, Markus and King, Glenn F. and Adams, David J. and Alewood, Paul F.},
+  year = {2021},
+  journal = {Nature Reviews Drug Discovery},
+  volume = {20},
+  number = {4},
+  pages = {309-325},
+  doi = {10.1038/s41573-020-00135-8},
+  url = {https://doi.org/10.1038/s41573-020-00135-8}
+}
+
+@article{guacamol,
+  title = {{GuacaMol: Benchmarking Models for de Novo Molecular Design}},
+  author = {Brown, Nathan and Fiscato, Marco and Segler, Marwin H.S. and Vaucher, Alain C.},
+  year = {2019},
+  journal = {Journal of Chemical Information and Modeling},
+  volume = {59},
+  number = {3},
+  pages = {1096-1108},
+  doi = {10.1021/acs.jcim.8b00839},
+  url = {https://doi.org/10.1021/acs.jcim.8b00839}
+}
+
+@article{colabfold,
+  title = {{ColabFold: making protein folding accessible to all}},
+  author = {Mirdita, Milot and Schütze, Konstantin and Moriwaki, Yoshitaka and Heo, Lim and Ovchinnikov, Sergey and Steinegger, Martin},
+  year = {2022},
+  journal = {Nature Methods},
+  volume = {19},
+  number = {6},
+  pages = {679-682},
+  doi = {10.1038/s41592-022-01488-1},
+  url = {https://doi.org/10.1038/s41592-022-01488-1}
+}
+
+@article{bh,
+  title = {{Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing}},
+  author = {Benjamini, Yoav and Hochberg, Yosef},
+  year = {1995},
+  journal = {Journal of the Royal Statistical Society Series B: Statistical Methodology},
+  volume = {57},
+  number = {1},
+  pages = {289-300},
+  doi = {10.1111/j.2517-6161.1995.tb02031.x},
+  url = {https://doi.org/10.1111/j.2517-6161.1995.tb02031.x}
+}
+
+@article{parego,
+  title = {{ParEGO: a hybrid algorithm with on-line landscape approximation for expensive multiobjective optimization problems}},
+  author = {Knowles, J.},
+  year = {2006},
+  journal = {IEEE Transactions on Evolutionary Computation},
+  volume = {10},
+  number = {1},
+  pages = {50-66},
+  doi = {10.1109/TEVC.2005.851274},
+  url = {https://doi.org/10.1109/TEVC.2005.851274}
+}
+
+@article{dscript,
+  title = {{D-SCRIPT translates genome to phenome with sequence-based, structure-aware, genome-scale predictions of protein-protein interactions}},
+  author = {Sledzieski, Samuel and Singh, Rohit and Cowen, Lenore and Berger, Bonnie},
+  year = {2021},
+  journal = {Cell Systems},
+  volume = {12},
+  number = {10},
+  pages = {969-982.e6},
+  doi = {10.1016/j.cels.2021.08.010},
+  url = {https://doi.org/10.1016/j.cels.2021.08.010}
+}
+
+
+@inproceedings{gat2024discreteflowmatching,
+  title = {{Discrete Flow Matching}},
+  author = {Gat, Itai and Remez, Tal and Shaul, Neta and Kreuk, Felix and Chen, Ricky T. Q. and Synnaeve, Gabriel and Adi, Yossi and Lipman, Yaron},
+  year = {2024},
+  booktitle = {Advances in Neural Information Processing Systems},
+  volume = {37},
+  doi = {10.52202/079017-4239},
+  url = {https://proceedings.neurips.cc/paper_files/paper/2024/hash/f0d629a734b56a642701bba7bc8bb3ed-Abstract-Conference.html},
+  pages = {133345--133385}
+}
+
+@inproceedings{cbas,
+  title = {{Conditioning by adaptive sampling for robust design}},
+  author = {Brookes, David and Park, Hahnbeom and Listgarten, Jennifer},
+  booktitle = {Proceedings of the 36th International Conference on Machine Learning},
+  pages = {773--782},
+  year = {2019},
+  volume = {97},
+  series = {Proceedings of Machine Learning Research},
+  publisher = {PMLR},
+  url = {https://proceedings.mlr.press/v97/brookes19a.html}
+}
+
+@inproceedings{coms,
+  title = {{Conservative Objective Models for Effective Offline Model-Based Optimization}},
+  author = {Trabucco, Brandon and Kumar, Aviral and Geng, Xinyang and Levine, Sergey},
+  booktitle = {Proceedings of the 38th International Conference on Machine Learning},
+  pages = {10358--10368},
+  year = {2021},
+  volume = {139},
+  series = {Proceedings of Machine Learning Research},
+  publisher = {PMLR},
+  url = {https://proceedings.mlr.press/v139/trabucco21a.html}
+}
+
+@inproceedings{designbench,
+  title = {{Design-Bench: Benchmarks for Data-Driven Offline Model-Based Optimization}},
+  author = {Trabucco, Brandon and Geng, Xinyang and Kumar, Aviral and Levine, Sergey},
+  booktitle = {Proceedings of the 39th International Conference on Machine Learning},
+  pages = {21658--21676},
+  year = {2022},
+  volume = {162},
+  series = {Proceedings of Machine Learning Research},
+  publisher = {PMLR},
+  url = {https://proceedings.mlr.press/v162/trabucco22a.html}
+}
+
+@article{mmseqs,
+  title={MMseqs2 enables sensitive protein sequence searching for the analysis of massive data sets},
+  author={Steinegger, Martin and S{\"o}ding, Johannes},
+  journal={Nature biotechnology},
+  volume={35},
+  number={11},
+  pages={1026--1028},
+  year={2017},
+  publisher={Nature Publishing Group US New York}
+}
+
+@misc{mogdfm,
+ title = {{Multi-Objective-Guided Discrete Flow Matching for Controllable Biological Sequence Design}},
+ author = {Chen, Tong and Zhang, Yinuo and Tang, Sophia and Chatterjee, Pranam},
+ year = {2025},
+ eprint = {2505.07086},
+ archivePrefix = {arXiv},
+ url = {https://arxiv.org/abs/2505.07086}
+}
+
+@misc{allogen,
+ title = {{AlloGen: Conformation-Selective Binder Generation with Differential State Scoring}},
+ author = {Cao, Hanqun and Quinn, Zachary and Pal, Aastha and Kimura, Sumi and Zhang, Jingjie and Heng, Pheng Ann and Chatterjee, Pranam},
+ year = {2026},
+ eprint = {2606.05474},
+ archivePrefix = {arXiv},
+ url = {https://arxiv.org/abs/2606.05474}
+}
+
+@misc{peptune,
+ title = {{PepTune: De Novo Generation of Therapeutic Peptides with Multi-Objective-Guided Discrete Diffusion}},
+ author = {Tang, Sophia and Zhang, Yinuo and Chatterjee, Pranam},
+ year = {2024},
+ eprint = {2412.17780},
+ archivePrefix = {arXiv},
+ url = {https://arxiv.org/abs/2412.17780}
+}
+
+@misc{react,
+ title = {{ReAct: Synergizing Reasoning and Acting in Language Models}},
+ author = {Yao, Shunyu and Zhao, Jeffrey and Yu, Dian and Du, Nan and Shafran, Izhak and Narasimhan, Karthik and Cao, Yuan},
+ year = {2022},
+ eprint = {2210.03629},
+ archivePrefix = {arXiv},
+ url = {https://arxiv.org/abs/2210.03629}
+}
+
+@misc{reflexion,
+ title = {{Reflexion: Language Agents with Verbal Reinforcement Learning}},
+ author = {Shinn, Noah and Cassano, Federico and Berman, Edward and Gopinath, Ashwin and Narasimhan, Karthik and Yao, Shunyu},
+ year = {2023},
+ eprint = {2303.11366},
+ archivePrefix = {arXiv},
+ url = {https://arxiv.org/abs/2303.11366}
+}
+
+@misc{paperqa,
+ title = {{Language agents achieve superhuman synthesis of scientific knowledge}},
+ author = {Skarlinski, Michael D. and Cox, Sam and Laurent, Jon M. and Braza, James D. and Hinks, Michaela and Hammerling, Michael J. and Ponnapati, Manvitha and Rodriques, Samuel G. and White, Andrew D.},
+ year = {2024},
+ eprint = {2409.13740},
+ archivePrefix = {arXiv},
+ url = {https://arxiv.org/abs/2409.13740}
+}
+
+@misc{conformal,
+ title = {{A Gentle Introduction to Conformal Prediction and Distribution-Free Uncertainty Quantification}},
+ author = {Angelopoulos, Anastasios N. and Bates, Stephen},
+ year = {2021},
+ eprint = {2107.07511},
+ archivePrefix = {arXiv},
+ url = {https://arxiv.org/abs/2107.07511}
+}
+
+@misc{biodiscovery,
+ title = {{BioDiscoveryAgent: An AI Agent for Designing Genetic Perturbation Experiments}},
+ author = {Roohani, Yusuf and Lee, Andrew and Huang, Qian and Vora, Jian and Steinhart, Zachary and Huang, Kexin and Marson, Alexander and Liang, Percy and Leskovec, Jure},
+ year = {2024},
+ eprint = {2405.17631},
+ archivePrefix = {arXiv},
+ url = {https://arxiv.org/abs/2405.17631}
+}
+
+@misc{soapia,
+ title = {{SOAPIA: Specificity-Guided Generation of Off-Target-Avoiding Protein Interactions with High Target Affinity}},
+ author = {Vincoff, Sophia and Liu, Howard and Linardic, Corinne M. and Chatterjee, Pranam},
+ year = {2026},
+ note = {Supplied ICML workshop manuscript, Generative and Agentic AI for Biology}
+}
+@misc{td3b,
+ title = {{TD3B: Transition-Directed Discrete Diffusion for Allosteric Binder Generation}},
+ author = {Cao, Hanqun and Pal, Aastha and Tang, Sophia and Zhang, Yinuo and Zhang, Jingjie and Heng, Pheng Ann and Chatterjee, Pranam},
+ year = {2026},
+ note = {Supplied ICML manuscript},
+ url = {https://huggingface.co/ChatterjeeLab/TD3B}
+}
+@misc{snooppi,
+ title = {{SNOOPPI: Sequence-Normalized Database of On- and Off-Target Protein-Protein Interactions}},
+ author = {{Chatterjee Lab}},
+ year = {2026},
+ doi = {10.57967/hf/10413},
+ note = {Dataset revision a1c75633343e292db8bb25e3356d0b8cd64afa91},
+ url = {https://huggingface.co/datasets/ChatterjeeLab/SNOOPPI}
+}
+@misc{muse,
+ title = {{Muse Glimmer-30B Model Card}},
+ author = {{Meta Superintelligence Lab}},
+ year = {2026},
+ note = {Revision a4e59da52a7bc87ae7251dd5545c0dd437c44b68. Accessed 20 September 2026},
+ url = {https://huggingface.co/meta-models/Muse-Glimmer-30B}
+}
+
+@article{pepmlm,
+ title = {{Target sequence-conditioned design of peptide binders using masked language modeling}},
+ author = {Chen, Leo Tianlai and Quinn, Zachary and Dumas, Madeleine and Peng, Christina and Hong, Lauren and Lopez-Gonzalez, Moises and Mestre, Alexander and Watson, Rio and Vincoff, Sophia and Zhao, Lin and Wu, Jianli and Stavrand, Audrey and Schaepers-Cheu, Mayumi and Wang, Tian Zi and Srijay, Divya and Monticello, Connor and Vure, Pranay and Pulugurta, Rishab and Pertsemlidis, Sarah and Kholina, Kseniia and Goel, Shrey and DeLisa, Matthew P. and Chi, Jen-Tsan Ashley and Truant, Ray and Aguilar, Hector C. and Chatterjee, Pranam},
+ year = {2025},
+ journal = {Nature Biotechnology},
+ doi = {10.1038/s41587-025-02761-2},
+ url = {https://doi.org/10.1038/s41587-025-02761-2}
+}
+
+@misc{lapep,
+ title = {{LaPep: Can Language Contribute to Property-Guided Peptide Design?}},
+ author = {{Anonymous Authors}},
+ year = {2026},
+ note = {Supplied NeurIPS manuscript}
+}
+
+@article{duab,
+ title = {Programmable protein stabilization with language model-derived peptide guides},
+ author = {Hong, Lauren and Ye, Tianzheng and Wang, Tian Z. and Srijay, Divya and Liu, Howard and Zhao, Lin and Watson, Rio and Vincoff, Sophia and Chen, Tianlai and Kholina, Kseniia and Goel, Shrey and DeLisa, Matthew P. and Chatterjee, Pranam},
+ journal = {Nature Communications},
+ year = {2025},
+ volume = {16},
+ pages = {3555},
+ doi = {10.1038/s41467-025-58872-6}
+}
+
+@misc{llmcompiler,
+ title = {{An LLM Compiler for Parallel Function Calling}},
+ author = {Kim, Sehoon and Moon, Suhong and Tabrizi, Ryan and Lee, Nicholas and Mahoney, Michael W. and Keutzer, Kurt and Gholami, Amir},
+ year = {2024}, eprint = {2312.04511}, archivePrefix = {arXiv},
+ url = {https://arxiv.org/abs/2312.04511}, note = {ICML 2024}
+}
+@misc{llmp,
+ title = {{LLM+P: Empowering Large Language Models with Optimal Planning Proficiency}},
+ author = {Liu, Bo and Jiang, Yuqian and Zhang, Xiaohan and Liu, Qiang and Zhang, Shiqi and Biswas, Joydeep and Stone, Peter},
+ year = {2023}, eprint = {2304.11477}, archivePrefix = {arXiv},
+ url = {https://arxiv.org/abs/2304.11477}
+}
+@misc{taubench,
+ title = {{$\tau$-bench: A Benchmark for Tool-Agent-User Interaction in Real-World Domains}},
+ author = {Yao, Shunyu and Shinn, Noah and Razavi, Pedram and Narasimhan, Karthik},
+ year = {2024}, eprint = {2406.12045}, archivePrefix = {arXiv},
+ url = {https://arxiv.org/abs/2406.12045}
+}
+@misc{agentbench,
+ title = {{AgentBench: Evaluating LLMs as Agents}},
+ author = {Liu, Xiao and Yu, Hao and Zhang, Hanchen and Xu, Yifan and Lei, Xuanyu and Lai, Hanyu and Gu, Yu and Ding, Hangliang and Men, Kaiwen and Yang, Kejuan and Zhang, Shudan and Deng, Xiang and Zeng, Aohan and Du, Zhengxiao and Zhang, Chenhui and Shen, Sheng and Zhang, Tianjun and Su, Yu and Sun, Huan and Huang, Minlie and Dong, Yuxiao and Tang, Jie},
+ year = {2024}, eprint = {2308.03688}, archivePrefix = {arXiv},
+ url = {https://arxiv.org/abs/2308.03688}, note = {ICLR 2024}
+}
+
+@article{hongreview,
+  title = {{AI-Designed Peptides as Tools for Biochemistry}},
+  author = {Hong, Lauren and Vincoff, Sophia and Chatterjee, Pranam},
+  year = {2026},
+  journal = {Biochemistry},
+  volume = {65},
+  number = {8},
+  pages = {1071--1083},
+  doi = {10.1021/acs.biochem.6c00138},
+  url = {https://doi.org/10.1021/acs.biochem.6c00138}
+}
+
+@article{therapeutic2022,
+  title = {{Therapeutic peptides: current applications and future directions}},
+  author = {Wang, Lei and Wang, Nanxi and Zhang, Wenping and Cheng, Xurui and Yan, Zhibin and Shao, Gang and Wang, Xi and Wang, Rui and Fu, Caiyun},
+  year = {2022},
+  journal = {Signal Transduction and Targeted Therapy},
+  volume = {7},
+  number = {1},
+  pages = {48},
+  doi = {10.1038/s41392-022-00904-4},
+  url = {https://doi.org/10.1038/s41392-022-00904-4}
+}
+
+@article{membranemacrocycles,
+  title = {{Accurate de novo design of membrane-traversing macrocycles}},
+  author = {Bhardwaj, Gaurav and O’Connor, Jacob and Rettie, Stephen and Huang, Yen-Hua and Ramelot, Theresa A. and Mulligan, Vikram Khipple and Alpkilic, Gizem Gokce and Palmer, Jonathan and Bera, Asim K. and Bick, Matthew J. and Di Piazza, Maddalena and Li, Xinting and Hosseinzadeh, Parisa and Craven, Timothy W. and Tejero, Roberto and Lauko, Anna and Choi, Ryan and Glynn, Calina and Dong, Linlin and Griffin, Robert and van Voorhis, Wesley C. and Rodriguez, Jose and Stewart, Lance and Montelione, Gaetano T. and Craik, David and Baker, David},
+  year = {2022},
+  journal = {Cell},
+  volume = {185},
+  number = {19},
+  pages = {3520--3532.e26},
+  doi = {10.1016/j.cell.2022.07.019},
+  url = {https://doi.org/10.1016/j.cell.2022.07.019}
+}
+
+@article{rfpeptides,
+  title = {{Accurate de novo design of high-affinity protein-binding macrocycles using deep learning}},
+  author = {Rettie, Stephen A. and Juergens, David and Adebomi, Victor and Bueso, Yensi Flores and Zhao, Qinqin and Leveille, Alexandria N. and Liu, Andi and Bera, Asim K. and Wilms, Joana A. and Üffing, Alina and Kang, Alex and Brackenbrough, Evans and Lamb, Mila and Gerben, Stacey R. and Murray, Analisa and Levine, Paul M. and Schneider, Maika and Vasireddy, Vibha and Ovchinnikov, Sergey and Weiergräber, Oliver H. and Willbold, Dieter and Kritzer, Joshua A. and Mougous, Joseph D. and Baker, David and DiMaio, Frank and Bhardwaj, Gaurav},
+  year = {2025},
+  journal = {Nature Chemical Biology},
+  volume = {21},
+  number = {12},
+  pages = {1948--1956},
+  doi = {10.1038/s41589-025-01929-w},
+  url = {https://doi.org/10.1038/s41589-025-01929-w}
+}
+
+@article{afcycdesign,
+  title = {{Cyclic peptide structure prediction and design using AlphaFold2}},
+  author = {Rettie, Stephen A. and Campbell, Katelyn V. and Bera, Asim K. and Kang, Alex and Kozlov, Simon and Bueso, Yensi Flores and De La Cruz, Joshmyn and Ahlrichs, Maggie and Cheng, Suna and Gerben, Stacey R. and Lamb, Mila and Murray, Analisa and Adebomi, Victor and Zhou, Guangfeng and DiMaio, Frank and Ovchinnikov, Sergey and Bhardwaj, Gaurav},
+  year = {2025},
+  journal = {Nature Communications},
+  volume = {16},
+  number = {1},
+  pages = {4730},
+  doi = {10.1038/s41467-025-59940-7},
+  url = {https://doi.org/10.1038/s41467-025-59940-7}
+}
+
+@article{pepinvent,
+  title = {{PepINVENT: generative peptide design beyond natural amino acids}},
+  author = {Geylan, Gökçe and Janet, Jon Paul and Tibo, Alessandro and He, Jiazhen and Patronov, Atanas and Kabeshov, Mikhail and Czechtizky, Werngard and David, Florian and Engkvist, Ola and De Maria, Leonardo},
+  year = {2025},
+  journal = {Chemical Science},
+  volume = {16},
+  number = {20},
+  pages = {8682--8696},
+  doi = {10.1039/d4sc07642g},
+  url = {https://doi.org/10.1039/d4sc07642g}
+}
+
+@article{helmgpt,
+  title = {{HELM-GPT: de novo macrocyclic peptide design using generative pre-trained transformer}},
+  author = {Xu, Xiaopeng and Xu, Chencheng and He, Wenjia and Wei, Lesong and Li, Haoyang and Zhou, Juexiao and Zhang, Ruochi and Wang, Yu and Xiong, Yuanpeng and Gao, Xin},
+  year = {2024},
+  journal = {Bioinformatics},
+  volume = {40},
+  number = {6},
+  pages = {btae364},
+  doi = {10.1093/bioinformatics/btae364},
+  url = {https://doi.org/10.1093/bioinformatics/btae364}
+}
+
+@article{boltzgen,
+  title = {{BoltzGen: Toward Universal Binder Design}},
+  author = {Stark, Hannes and Faltings, Felix and Choi, MinGyu and Xie, Yuxin and Hur, Eunsu and O’Donnell, Timothy and Bushuiev, Anton and Uçar, Talip and Passaro, Saro and Mao, Weian and Reveiz, Mateo and Bushuiev, Roman and Portnoi, Tally and Pluskal, Tomáš and Sivic, Josef and Kreis, Karsten and Vahdat, Arash and Ray, Shamayeeta and Goldstein, Jonathan T. and Savinov, Andrew and Hambalek, Jacob A. and Gupta, Anshika and Taquiri-Diaz, Diego A. and Zhang, Yaotian and Snyder, Samuel J. and Hatstat, A. Katherine and Arada, Angelika and Kim, Nam Hyeong and Fan, Haoyu and Tackie-Yarboi, Ethel and Boselli, Dylan and Schnaider, Lee and Liu, Chang C. and Li, Gene-Wei and Hnisz, Denes and Sabatini, David M. and DeGrado, William F. and Wohlwend, Jeremy and Corso, Gabriele and Barzilay, Regina and Jaakkola, Tommi},
+  year = {2025},
+  journal = {bioRxiv},
+  doi = {10.1101/2025.11.20.689494},
+  url = {https://doi.org/10.1101/2025.11.20.689494},
+  note = {Preprint}
+}
+
+@article{erkdarpins,
+  title = {{Structural and functional analysis of phosphorylation-specific binders of the kinase ERK from designed ankyrin repeat protein libraries}},
+  author = {Kummer, Lutz and Parizek, Petra and Rube, Peter and Millgramm, Bastian and Prinz, Anke and Mittl, Peer R. E. and Kaufholz, Melanie and Zimmermann, Bastian and Herberg, Friedrich W. and Plückthun, Andreas},
+  year = {2012},
+  journal = {Proceedings of the National Academy of Sciences},
+  volume = {109},
+  number = {34},
+  doi = {10.1073/pnas.1205399109},
+  url = {https://doi.org/10.1073/pnas.1205399109}
+}
+
+@misc{peptiversespace,
+  author = {{ChatterjeeLab}},
+  title = {{PeptiVerse Hugging Face Space}},
+  year = {2026},
+  url = {https://huggingface.co/spaces/ChatterjeeLab/PeptiVerse},
+  note = {Accessed 20 September 2026}
+}
+
+@misc{astramodel,
+  author = {{OpenAI}},
+  title = {{GPT-6 Astra model documentation}},
+  year = {2026},
+  url = {https://developers.openai.com/api/docs/models/gpt-6-astra},
+  note = {Accessed 20 September 2026}
+}
+
+@misc{claudeeffort,
+  author = {{Anthropic}},
+  title = {{Effort controls for Claude models}},
+  year = {2026},
+  url = {https://platform.claude.com/docs/en/build-with-claude/effort},
+  note = {Accessed 20 September 2026}
+}

examples/compiled_plan.json

@@ -0,0 +1,40 @@
+{
+  "task_hash": "source_data_example",
+  "evidence_snapshot_hash": "examples/source_records.json",
+  "hypotheses": [],
+  "obligations": [],
+  "nodes": [
+    {
+      "id": "01",
+      "tool": "normalize",
+      "revision": "1",
+      "inputs": [
+        "raw"
+      ],
+      "outputs": [
+        "normalized"
+      ],
+      "cost": 1,
+      "attempts": 1
+    },
+    {
+      "id": "02",
+      "tool": "report",
+      "revision": "1",
+      "inputs": [
+        "normalized"
+      ],
+      "outputs": [
+        "release_report"
+      ],
+      "cost": 1,
+      "attempts": 1
+    }
+  ],
+  "required": [
+    "release_report"
+  ],
+  "budget": 2,
+  "seed": 2027,
+  "diagnostics": []
+}

examples/observed_result.json

@@ -0,0 +1,35 @@
+{
+  "candidates": [
+    {
+      "molecule": {
+        "sequence": "ACYCRIPACIAGERRYGTCIYQGRLWAFCC",
+        "modifications": [],
+        "bonds": [],
+        "n_terminus": "free",
+        "c_terminus": "free",
+        "smiles": null
+      },
+      "parent_ids": [],
+      "generator": "SNOOPPI observed interaction",
+      "revision": "a1c75633343e292db8bb25e3356d0b8cd64afa91"
+    }
+  ],
+  "measurements": [
+    {
+      "candidate_id": "7c9f37d06bfb4db71cf9",
+      "endpoint": "observed_interaction",
+      "value": 1.0,
+      "unit": "binary_label",
+      "source_id": "SNOOPPI_positive_row_52",
+      "kind": "experiment",
+      "lower": null,
+      "upper": null,
+      "assay": "SNOOPPI_curated",
+      "replicate_id": null,
+      "censor": "none"
+    }
+  ],
+  "evidence": [],
+  "artifacts": {},
+  "message": "Imported curated positive label from the pinned source. No new affinity or peptide experiment was performed."
+}

examples/ptm_decision_blueprint.json

@@ -0,0 +1,15 @@
+{
+  "status": "illustrative blueprint; no execution or experimental result",
+  "decision": {
+    "tool": "paired_binding_assay",
+    "arguments": {
+      "candidate_id": "REPLACE_WITH_REAL_CHEMICAL_ID"
+    },
+    "hypothesis": "The peptide preferentially binds ERK2 pThr185/pTyr187 over matched unmodified wild-type ERK2.",
+    "evidence_ids": [],
+    "decision_summary": "Acquire the matched measurement required by the fixed task.",
+    "expected_observation": "Paired BLI Kd estimates for phospho-ERK2 and unmodified ERK2, with a paired log10 selectivity interval.",
+    "stop": false
+  },
+  "worker_requirement": "Register a validated assay-export worker and use actual molecular inputs."
+}

examples/registry.json

@@ -0,0 +1 @@
+{"commands": []}

examples/scripted_controller.json

@@ -0,0 +1,16 @@
+{
+  "type": "scripted",
+  "decisions": [
+    {
+      "tool": "ingest_results",
+      "arguments": {
+        "path": "examples/observed_result.json"
+      },
+      "hypothesis": "The archived interaction label can be reconstructed with unchanged source records.",
+      "evidence_ids": [],
+      "decision_summary": "Import the observed source label for the real-data replay example.",
+      "expected_observation": "One source-linked interaction label.",
+      "stop": false
+    }
+  ]
+}

examples/snooppi_raw.jsonl

@@ -0,0 +1,8 @@
+{"SNOOPPI_final_label": "positive", "MI0915_final_label": "positive", "SNOOPPI_Partner_A_ID": "SNOOPPI_partner_00000098", "SNOOPPI_Partner_B_ID": "SNOOPPI_partner_00077234", "SNOOPPI_Pair_ID": "SNOOPPI_pair_00000141", "SNOOPPI_Labeled_Pair_ID": "POSITIVE_SNOOPPI_pair_00000141", "seq_sort": "AAF_MARSLLLPLQILLLSLALETAGEEAQGDKIIDGAPCARGSHPWQVALLSGNQLHCGGVLVNERWVLTAAHCKMNEYTVHLGSDTLGDRRAQRIKASKSFRHPGYSTQTHVNDLMLVKLNSQARLSSMVKKVRLPSRCEPPGTTCTVSGWGTTTSPDVTFPSDLMCVDVKLISPQDCTKVYKDLLENSMLCAGIPDSKKNACNGDSGGPLVCRGTLQGLVSWGTFPCGQPNDPGVYTQVCKFTKWINDTMKKHR", "unique_interaction_mi_terms": "MI:0407 (direct interaction)", "partner_A_sequence": "AAF", "partner_B_sequence": "MARSLLLPLQILLLSLALETAGEEAQGDKIIDGAPCARGSHPWQVALLSGNQLHCGGVLVNERWVLTAAHCKMNEYTVHLGSDTLGDRRAQRIKASKSFRHPGYSTQTHVNDLMLVKLNSQARLSSMVKKVRLPSRCEPPGTTCTVSGWGTTTSPDVTFPSDLMCVDVKLISPQDCTKVYKDLLENSMLCAGIPDSKKNACNGDSGGPLVCRGTLQGLVSWGTFPCGQPNDPGVYTQVCKFTKWINDTMKKHR", "partner_A_length": "3", "partner_B_length": "253", "partner_A_mol_type": "peptide", "partner_B_mol_type": "protein", "intact_interaction_ids": "EBI-15662552", "n_intact_interaction_ids": "1", "partner_A_intact_interactor_ids": "intact:EBI-15662549", "partner_B_intact_interactor_ids": "intact:EBI-10635674", "partner_A_n_intact_interactor_ids": "1", "partner_B_n_intact_interactor_ids": "1", "partner_A_uniprot_ids": null, "partner_B_uniprot_ids": "P49862-0", "partner_A_gene_names": null, "partner_B_gene_names": "KLK7_HUMAN", "partner_A_species": "9606 (human)", "partner_B_species": "-2 (chemical synthesis)", "mi_scores_all": "0.44", "mi_score_mean": "0.44", "mi_score_min": "0.44", "mi_score_max": "0.44", "assays_unique": "MI:0114 (x-ray crystallography)", "n_assays_unique": "1", "n_papers": "1", "pubmed_ids": "17909180", "publication_years_all": "2007", "publication_year_min": "2007", "publication_year_max": "2007", "feature_relationship_types": null, "feature_relationship_summary": null, "has_mutation_relationship": "False", "has_ptm_relationship": "False", "has_binding_site_relationship": "False", "partner_A_wildtype_sequence_if_feature_modified": null, "partner_B_wildtype_sequence_if_feature_modified": null, "wildtype_seq_sort_if_feature_modified": null, "wildtype_SNOOPPI_final_label": null, "wildtype_MI0915_final_label": null, "exact_sequence_pair_intact_label_summary": "positive", "snooppi_label_transition_from_intact": "confirmed", "seq_sort_origin_categories": "exact_seq_sort_seen_in_original_intact", "unique_expansions": null, "partner_A_go_terms": null, "partner_B_go_terms": null, "partner_A_dip": null, "partner_B_dip": null, "partner_A_ensg": null, "partner_B_ensg": null, "partner_A_ense": null, "partner_B_ense": null, "partner_A_enst": null, "partner_B_enst": null, "partner_A_interpro": null, "partner_B_interpro": null, "partner_A_rcsb_pdb": null, "partner_B_rcsb_pdb": null, "partner_A_reactome": null, "partner_B_reactome": null, "partner_A_host_context": "labels_full = In vitro", "partner_B_host_context": "labels_full = In vitro", "partner_A_sequence_snooppi_degree_by_label": "positive=1; negative=0; unknown=0", "partner_B_sequence_snooppi_degree_by_label": "positive=3; negative=0; unknown=8", "partner_A_sequence_modified_role_counts": null, "partner_B_sequence_modified_role_counts": null, "partner_A_sequence_partner_modified_role_counts": null, "partner_B_sequence_partner_modified_role_counts": null, "partner_A_gene_snooppi_degree_by_label": null, "partner_B_gene_snooppi_degree_by_label": "positive=3; negative=0; unknown=11", "partner_A_gene_modified_role_counts": null, "partner_B_gene_modified_role_counts": "binding_site=3", "partner_A_gene_partner_modified_role_counts": null, "partner_B_gene_partner_modified_role_counts": null}
+{"SNOOPPI_final_label": "positive", "MI0915_final_label": "positive", "SNOOPPI_Partner_A_ID": "SNOOPPI_partner_00000205", "SNOOPPI_Partner_B_ID": "SNOOPPI_partner_00077234", "SNOOPPI_Pair_ID": "SNOOPPI_pair_00000247", "SNOOPPI_Labeled_Pair_ID": "POSITIVE_SNOOPPI_pair_00000247", "seq_sort": "AAPF_MARSLLLPLQILLLSLALETAGEEAQGDKIIDGAPCARGSHPWQVALLSGNQLHCGGVLVNERWVLTAAHCKMNEYTVHLGSDTLGDRRAQRIKASKSFRHPGYSTQTHVNDLMLVKLNSQARLSSMVKKVRLPSRCEPPGTTCTVSGWGTTTSPDVTFPSDLMCVDVKLISPQDCTKVYKDLLENSMLCAGIPDSKKNACNGDSGGPLVCRGTLQGLVSWGTFPCGQPNDPGVYTQVCKFTKWINDTMKKHR", "unique_interaction_mi_terms": "MI:0407 (direct interaction)", "partner_A_sequence": "AAPF", "partner_B_sequence": "MARSLLLPLQILLLSLALETAGEEAQGDKIIDGAPCARGSHPWQVALLSGNQLHCGGVLVNERWVLTAAHCKMNEYTVHLGSDTLGDRRAQRIKASKSFRHPGYSTQTHVNDLMLVKLNSQARLSSMVKKVRLPSRCEPPGTTCTVSGWGTTTSPDVTFPSDLMCVDVKLISPQDCTKVYKDLLENSMLCAGIPDSKKNACNGDSGGPLVCRGTLQGLVSWGTFPCGQPNDPGVYTQVCKFTKWINDTMKKHR", "partner_A_length": "4", "partner_B_length": "253", "partner_A_mol_type": "peptide", "partner_B_mol_type": "protein", "intact_interaction_ids": "EBI-15662575", "n_intact_interaction_ids": "1", "partner_A_intact_interactor_ids": "intact:EBI-15662572", "partner_B_intact_interactor_ids": "intact:EBI-10635674", "partner_A_n_intact_interactor_ids": "1", "partner_B_n_intact_interactor_ids": "1", "partner_A_uniprot_ids": null, "partner_B_uniprot_ids": "P49862-0", "partner_A_gene_names": null, "partner_B_gene_names": "KLK7_HUMAN", "partner_A_species": "9606 (human)", "partner_B_species": "-2 (chemical synthesis)", "mi_scores_all": "0.44", "mi_score_mean": "0.44", "mi_score_min": "0.44", "mi_score_max": "0.44", "assays_unique": "MI:0114 (x-ray crystallography)", "n_assays_unique": "1", "n_papers": "1", "pubmed_ids": "17909180", "publication_years_all": "2007", "publication_year_min": "2007", "publication_year_max": "2007", "feature_relationship_types": null, "feature_relationship_summary": null, "has_mutation_relationship": "False", "has_ptm_relationship": "False", "has_binding_site_relationship": "False", "partner_A_wildtype_sequence_if_feature_modified": null, "partner_B_wildtype_sequence_if_feature_modified": null, "wildtype_seq_sort_if_feature_modified": null, "wildtype_SNOOPPI_final_label": null, "wildtype_MI0915_final_label": null, "exact_sequence_pair_intact_label_summary": "positive", "snooppi_label_transition_from_intact": "confirmed", "seq_sort_origin_categories": "exact_seq_sort_seen_in_original_intact", "unique_expansions": null, "partner_A_go_terms": null, "partner_B_go_terms": null, "partner_A_dip": null, "partner_B_dip": null, "partner_A_ensg": null, "partner_B_ensg": null, "partner_A_ense": null, "partner_B_ense": null, "partner_A_enst": null, "partner_B_enst": null, "partner_A_interpro": null, "partner_B_interpro": null, "partner_A_rcsb_pdb": null, "partner_B_rcsb_pdb": null, "partner_A_reactome": null, "partner_B_reactome": null, "partner_A_host_context": "labels_full = In vitro", "partner_B_host_context": "labels_full = In vitro", "partner_A_sequence_snooppi_degree_by_label": "positive=1; negative=0; unknown=0", "partner_B_sequence_snooppi_degree_by_label": "positive=3; negative=0; unknown=8", "partner_A_sequence_modified_role_counts": null, "partner_B_sequence_modified_role_counts": null, "partner_A_sequence_partner_modified_role_counts": null, "partner_B_sequence_partner_modified_role_counts": null, "partner_A_gene_snooppi_degree_by_label": null, "partner_B_gene_snooppi_degree_by_label": "positive=3; negative=0; unknown=11", "partner_A_gene_modified_role_counts": null, "partner_B_gene_modified_role_counts": "binding_site=3", "partner_A_gene_partner_modified_role_counts": null, "partner_B_gene_partner_modified_role_counts": null}
+{"SNOOPPI_final_label": "positive", "MI0915_final_label": "positive", "SNOOPPI_Partner_A_ID": "SNOOPPI_partner_00000361", "SNOOPPI_Partner_B_ID": "SNOOPPI_partner_00202528", "SNOOPPI_Pair_ID": "SNOOPPI_pair_00000484", "SNOOPPI_Labeled_Pair_ID": "POSITIVE_SNOOPPI_pair_00000484", "seq_sort": "ACYCRIPACIAGERRYGTCIYQGRLWAFCC_MKWVTFISLLLLFSSAYSRGVFRRDTHKSEIAHRFKDLGEEHFKGLVLIAFSQYLQQCPFDEHVKLVNELTEFAKTCVADESHAGCEKSLHTLFGDELCKVASLRETYGDMADCCEKQEPERNECFLSHKDDSPDLPKLKPDPNTLCDEFKADEKKFWGKYLYEIARRHPYFYAPELLYYANKYNGVFQECCQAEDKGACLLPKIETMREKVLASSARQRLRCASIQKFGERALKAWSVARLSQKFPKAEFVEVTKLVTDLTKVHKECCHGDLLECADDRADLAKYICDNQDTISSKLKECCDKPLLEKSHCIAEVEKDAIPENLPPLTADFAEDKDVCKNYQEAKDAFLGSFLYEYSRRHPEYAVSVLLRLAKEYEATLEECCAKDDPHACYSTVFDKLKHLVDEPQNLIKQNCDQFEKLGEYGFQNALIVRYTRKVPQVSTPTLVEVSRSLGKVGTRCCTKPESERMPCTEDYLSLILNRLCVLHEKTPVSEKVTKCCTESLVNRRPCFSALTPDETYVPKAFDEKLFTFHADICTLPDTEKQIKKQTALVELLKHKPKATEEQLKTVMENFVAFVDKCCAADDKEACFAVEGPKLVVSTQTALA", "unique_interaction_mi_terms": "MI:0407 (direct interaction)", "partner_A_sequence": "ACYCRIPACIAGERRYGTCIYQGRLWAFCC", "partner_B_sequence": "MKWVTFISLLLLFSSAYSRGVFRRDTHKSEIAHRFKDLGEEHFKGLVLIAFSQYLQQCPFDEHVKLVNELTEFAKTCVADESHAGCEKSLHTLFGDELCKVASLRETYGDMADCCEKQEPERNECFLSHKDDSPDLPKLKPDPNTLCDEFKADEKKFWGKYLYEIARRHPYFYAPELLYYANKYNGVFQECCQAEDKGACLLPKIETMREKVLASSARQRLRCASIQKFGERALKAWSVARLSQKFPKAEFVEVTKLVTDLTKVHKECCHGDLLECADDRADLAKYICDNQDTISSKLKECCDKPLLEKSHCIAEVEKDAIPENLPPLTADFAEDKDVCKNYQEAKDAFLGSFLYEYSRRHPEYAVSVLLRLAKEYEATLEECCAKDDPHACYSTVFDKLKHLVDEPQNLIKQNCDQFEKLGEYGFQNALIVRYTRKVPQVSTPTLVEVSRSLGKVGTRCCTKPESERMPCTEDYLSLILNRLCVLHEKTPVSEKVTKCCTESLVNRRPCFSALTPDETYVPKAFDEKLFTFHADICTLPDTEKQIKKQTALVELLKHKPKATEEQLKTVMENFVAFVDKCCAADDKEACFAVEGPKLVVSTQTALA", "partner_A_length": "30", "partner_B_length": "607", "partner_A_mol_type": "protein", "partner_B_mol_type": "protein", "intact_interaction_ids": "EBI-27090321", "n_intact_interaction_ids": "1", "partner_A_intact_interactor_ids": "intact:EBI-27090284", "partner_B_intact_interactor_ids": "intact:EBI-2296927", "partner_A_n_intact_interactor_ids": "1", "partner_B_n_intact_interactor_ids": "1", "partner_A_uniprot_ids": "P59665-PRO_0000006773", "partner_B_uniprot_ids": "P02769-0", "partner_A_gene_names": "DEF1_HUMAN", "partner_B_gene_names": "ALBU_BOVIN", "partner_A_species": "9606 (human)", "partner_B_species": "9913 (bovin)", "mi_scores_all": "0.44", "mi_score_mean": "0.44", "mi_score_min": "0.44", "mi_score_max": "0.44", "assays_unique": "MI:0053 (fluorescence polarization spectroscopy)", "n_assays_unique": "1", "n_papers": "1", "pubmed_ids": "34487793", "publication_years_all": "2021", "publication_year_min": "2021", "publication_year_max": "2021", "feature_relationship_types": null, "feature_relationship_summary": null, "has_mutation_relationship": "False", "has_ptm_relationship": "False", "has_binding_site_relationship": "False", "partner_A_wildtype_sequence_if_feature_modified": null, "partner_B_wildtype_sequence_if_feature_modified": null, "wildtype_seq_sort_if_feature_modified": null, "wildtype_SNOOPPI_final_label": null, "wildtype_MI0915_final_label": null, "exact_sequence_pair_intact_label_summary": "positive", "snooppi_label_transition_from_intact": "confirmed", "seq_sort_origin_categories": "exact_seq_sort_seen_in_original_intact", "unique_expansions": null, "partner_A_go_terms": null, "partner_B_go_terms": null, "partner_A_dip": null, "partner_B_dip": null, "partner_A_ensg": null, "partner_B_ensg": null, "partner_A_ense": null, "partner_B_ense": null, "partner_A_enst": null, "partner_B_enst": null, "partner_A_interpro": null, "partner_B_interpro": null, "partner_A_rcsb_pdb": null, "partner_B_rcsb_pdb": null, "partner_A_reactome": null, "partner_B_reactome": null, "partner_A_host_context": "labels_full = In vitro", "partner_B_host_context": "labels_full = In vitro", "partner_A_sequence_snooppi_degree_by_label": "positive=1; negative=0; unknown=6", "partner_B_sequence_snooppi_degree_by_label": "positive=15; negative=6; unknown=13", "partner_A_sequence_modified_role_counts": null, "partner_B_sequence_modified_role_counts": null, "partner_A_sequence_partner_modified_role_counts": "mutation=6; binding_site=9", "partner_B_sequence_partner_modified_role_counts": "mutation=7; binding_site=19", "partner_A_gene_snooppi_degree_by_label": "positive=1; negative=0; unknown=10", "partner_B_gene_snooppi_degree_by_label": "positive=15; negative=6; unknown=13", "partner_A_gene_modified_role_counts": null, "partner_B_gene_modified_role_counts": null, "partner_A_gene_partner_modified_role_counts": "mutation=6; binding_site=9", "partner_B_gene_partner_modified_role_counts": "mutation=7; binding_site=19"}
+{"SNOOPPI_final_label": "positive", "MI0915_final_label": "positive", "SNOOPPI_Partner_A_ID": "SNOOPPI_partner_00000461", "SNOOPPI_Partner_B_ID": "SNOOPPI_partner_00202320", "SNOOPPI_Pair_ID": "SNOOPPI_pair_00000607", "SNOOPPI_Labeled_Pair_ID": "POSITIVE_SNOOPPI_pair_00000607", "seq_sort": "ADSTITIRGYVRDNR_MKWCKRGYVLAAILALASATIQAADVTITVNGKVVAKPCTVSTTNATVDLGDLYSFSLMSAGAASAWHDVALELTNCPVGTSRVTASFSGAADSTGYYKNQGTAQNIQLELQDDSGNTLNTGATKTVQVDDSSQSAHFPLQVRALTVNGGATQGTIQAVISITYTYS", "unique_interaction_mi_terms": "MI:0407 (direct interaction)", "partner_A_sequence": "ADSTITIRGYVRDNR", "partner_B_sequence": "MKWCKRGYVLAAILALASATIQAADVTITVNGKVVAKPCTVSTTNATVDLGDLYSFSLMSAGAASAWHDVALELTNCPVGTSRVTASFSGAADSTGYYKNQGTAQNIQLELQDDSGNTLNTGATKTVQVDDSSQSAHFPLQVRALTVNGGATQGTIQAVISITYTYS", "partner_A_length": "15", "partner_B_length": "167", "partner_A_mol_type": "peptide", "partner_B_mol_type": "protein", "intact_interaction_ids": "EBI-15697509 | EBI-15697566 | EBI-15697585 | EBI-15697620", "n_intact_interaction_ids": "4", "partner_A_intact_interactor_ids": "intact:EBI-15697506", "partner_B_intact_interactor_ids": "intact:EBI-1785843", "partner_A_n_intact_interactor_ids": "1", "partner_B_n_intact_interactor_ids": "1", "partner_A_uniprot_ids": null, "partner_B_uniprot_ids": "P08190-0", "partner_A_gene_names": null, "partner_B_gene_names": "FIMG_ECOLI", "partner_A_species": "83333 (ecoli)", "partner_B_species": "-2 (chemical synthesis)", "mi_scores_all": "0.65", "mi_score_mean": "0.65", "mi_score_min": "0.65", "mi_score_max": "0.65", "assays_unique": "MI:0016 (circular dichroism) | MI:0017 (classical fluorescence spectroscopy) | MI:0071 (molecular sieving) | MI:0114 (x-ray crystallography)", "n_assays_unique": "4", "n_papers": "1", "pubmed_ids": "18400183", "publication_years_all": "2008", "publication_year_min": "2008", "publication_year_max": "2008", "feature_relationship_types": null, "feature_relationship_summary": null, "has_mutation_relationship": "False", "has_ptm_relationship": "False", "has_binding_site_relationship": "False", "partner_A_wildtype_sequence_if_feature_modified": null, "partner_B_wildtype_sequence_if_feature_modified": null, "wildtype_seq_sort_if_feature_modified": null, "wildtype_SNOOPPI_final_label": null, "wildtype_MI0915_final_label": null, "exact_sequence_pair_intact_label_summary": "positive", "snooppi_label_transition_from_intact": "confirmed", "seq_sort_origin_categories": "exact_seq_sort_seen_in_original_intact", "unique_expansions": null, "partner_A_go_terms": null, "partner_B_go_terms": null, "partner_A_dip": null, "partner_B_dip": null, "partner_A_ensg": null, "partner_B_ensg": null, "partner_A_ense": null, "partner_B_ense": null, "partner_A_enst": null, "partner_B_enst": null, "partner_A_interpro": null, "partner_B_interpro": null, "partner_A_rcsb_pdb": null, "partner_B_rcsb_pdb": null, "partner_A_reactome": null, "partner_B_reactome": null, "partner_A_host_context": "labels_full = In vitro", "partner_B_host_context": "labels_full = In vitro", "partner_A_sequence_snooppi_degree_by_label": "positive=1; negative=0; unknown=6", "partner_B_sequence_snooppi_degree_by_label": "positive=2; negative=0; unknown=0", "partner_A_sequence_modified_role_counts": null, "partner_B_sequence_modified_role_counts": null, "partner_A_sequence_partner_modified_role_counts": "binding_site=12", "partner_B_sequence_partner_modified_role_counts": null, "partner_A_gene_snooppi_degree_by_label": null, "partner_B_gene_snooppi_degree_by_label": "positive=2; negative=0; unknown=6", "partner_A_gene_modified_role_counts": null, "partner_B_gene_modified_role_counts": "binding_site=12", "partner_A_gene_partner_modified_role_counts": null, "partner_B_gene_partner_modified_role_counts": null}
+{"SNOOPPI_final_label": "positive", "MI0915_final_label": "positive", "SNOOPPI_Partner_A_ID": "SNOOPPI_partner_00000689", "SNOOPPI_Partner_B_ID": "SNOOPPI_partner_00036686", "SNOOPPI_Pair_ID": "SNOOPPI_pair_00001116", "SNOOPPI_Labeled_Pair_ID": "POSITIVE_SNOOPPI_pair_00001116", "seq_sort": "AERPNIMQVKLV_MAALGVQSINWQTAFNRQAHHTDKFSSQELILRRGQNFQVLMIMNKGLGSNERLEFIVSTGPYPSESAMTKAVFPLSNGSSGGWSAVLQASNGNTLTISISSPASAPIGRYTMALQIFSQGGISSVKLGTFILLFNPWLNVDSVFMGNHAEREEYVQEDAGIIFVGSTNRIGMIGWNFGQFEEDILSICLSILDRSLNFRRDAATDVASRNDPKYVGRVLSAMINSNDDNGVLAGNWSGTYTGGRDPRSWNGSVEILKNWKKSGFSPVRYGQCWVFAGTLNTALRSLGIPSRVITNFNSAHDTDRNLSVDVYYDPMGNPLDKGSDSVWNFHVWNEGWFVRSDLGPSYGGWQVLDATPQERSQGVFQCGPASVIGVREGDVQLNFDMPFIFAEVNADRITWLYDNTTGKQWKNSVNSHTIGRYISTKAVGSNARMDVTDKYKYPEGSDQERQVFQKALGKLKPNTPFAATSSMGLETEEQEPSIIGKLKVAGMLAVGKEVNLVLLLKNLSRDTKTVTVNMTAWTIIYNGTLVHEVWKDSATMSLDPEEEAEHPIKISYAQYEKYLKSDNMIRITAVCKVPDESEVVVERDIILDNPTLTLEVLNEARVRKPVNVQMLFSNPLDEPVRDCVLMVEGSGLLLGNLKIDVPTLGPKEGSRVRFDILPSRSGTKQLLADFSCNKFPAIKAMLSIDVAE", "unique_interaction_mi_terms": "MI:0414 (enzymatic reaction)", "partner_A_sequence": "AERPNIMQVKLV", "partner_B_sequence": "MAALGVQSINWQTAFNRQAHHTDKFSSQELILRRGQNFQVLMIMNKGLGSNERLEFIVSTGPYPSESAMTKAVFPLSNGSSGGWSAVLQASNGNTLTISISSPASAPIGRYTMALQIFSQGGISSVKLGTFILLFNPWLNVDSVFMGNHAEREEYVQEDAGIIFVGSTNRIGMIGWNFGQFEEDILSICLSILDRSLNFRRDAATDVASRNDPKYVGRVLSAMINSNDDNGVLAGNWSGTYTGGRDPRSWNGSVEILKNWKKSGFSPVRYGQCWVFAGTLNTALRSLGIPSRVITNFNSAHDTDRNLSVDVYYDPMGNPLDKGSDSVWNFHVWNEGWFVRSDLGPSYGGWQVLDATPQERSQGVFQCGPASVIGVREGDVQLNFDMPFIFAEVNADRITWLYDNTTGKQWKNSVNSHTIGRYISTKAVGSNARMDVTDKYKYPEGSDQERQVFQKALGKLKPNTPFAATSSMGLETEEQEPSIIGKLKVAGMLAVGKEVNLVLLLKNLSRDTKTVTVNMTAWTIIYNGTLVHEVWKDSATMSLDPEEEAEHPIKISYAQYEKYLKSDNMIRITAVCKVPDESEVVVERDIILDNPTLTLEVLNEARVRKPVNVQMLFSNPLDEPVRDCVLMVEGSGLLLGNLKIDVPTLGPKEGSRVRFDILPSRSGTKQLLADFSCNKFPAIKAMLSIDVAE", "partner_A_length": "12", "partner_B_length": "693", "partner_A_mol_type": "peptide", "partner_B_mol_type": "protein", "intact_interaction_ids": "EBI-8171915", "n_intact_interaction_ids": "1", "partner_A_intact_interactor_ids": "intact:EBI-8171919", "partner_B_intact_interactor_ids": "intact:EBI-1050019", "partner_A_n_intact_interactor_ids": "1", "partner_B_n_intact_interactor_ids": "1", "partner_A_uniprot_ids": null, "partner_B_uniprot_ids": "Q08188-0", "partner_A_gene_names": null, "partner_B_gene_names": "TGM3_HUMAN", "partner_A_species": "-2 (chemical synthesis)", "partner_B_species": "9606 (human)", "mi_scores_all": "0.44", "mi_score_mean": "0.44", "mi_score_min": "0.44", "mi_score_max": "0.44", "assays_unique": "MI:0415 (enzymatic study)", "n_assays_unique": "1", "n_papers": "1", "pubmed_ids": "20716179", "publication_years_all": "2010", "publication_year_min": "2010", "publication_year_max": "2010", "feature_relationship_types": null, "feature_relationship_summary": null, "has_mutation_relationship": "False", "has_ptm_relationship": "False", "has_binding_site_relationship": "False", "partner_A_wildtype_sequence_if_feature_modified": null, "partner_B_wildtype_sequence_if_feature_modified": null, "wildtype_seq_sort_if_feature_modified": null, "wildtype_SNOOPPI_final_label": null, "wildtype_MI0915_final_label": null, "exact_sequence_pair_intact_label_summary": null, "snooppi_label_transition_from_intact": "added", "seq_sort_origin_categories": "not_seen_in_original_intact_origin_unclear", "unique_expansions": null, "partner_A_go_terms": null, "partner_B_go_terms": null, "partner_A_dip": null, "partner_B_dip": null, "partner_A_ensg": null, "partner_B_ensg": null, "partner_A_ense": null, "partner_B_ense": null, "partner_A_enst": null, "partner_B_enst": null, "partner_A_interpro": null, "partner_B_interpro": null, "partner_A_rcsb_pdb": null, "partner_B_rcsb_pdb": null, "partner_A_reactome": null, "partner_B_reactome": null, "partner_A_host_context": "labels_full = In vitro", "partner_B_host_context": "labels_full = In vitro", "partner_A_sequence_snooppi_degree_by_label": "positive=1; negative=0; unknown=0", "partner_B_sequence_snooppi_degree_by_label": "positive=16; negative=0; unknown=4", "partner_A_sequence_modified_role_counts": null, "partner_B_sequence_modified_role_counts": null, "partner_A_sequence_partner_modified_role_counts": null, "partner_B_sequence_partner_modified_role_counts": "mutation=1", "partner_A_gene_snooppi_degree_by_label": null, "partner_B_gene_snooppi_degree_by_label": "positive=16; negative=0; unknown=7", "partner_A_gene_modified_role_counts": null, "partner_B_gene_modified_role_counts": "binding_site=3", "partner_A_gene_partner_modified_role_counts": null, "partner_B_gene_partner_modified_role_counts": "mutation=1"}
+{"SNOOPPI_final_label": "positive", "MI0915_final_label": "positive", "SNOOPPI_Partner_A_ID": "SNOOPPI_partner_00000839", "SNOOPPI_Partner_B_ID": "SNOOPPI_partner_00000839", "SNOOPPI_Pair_ID": "SNOOPPI_pair_00001286", "SNOOPPI_Labeled_Pair_ID": "POSITIVE_SNOOPPI_pair_00001286", "seq_sort": "AGCKNFFWKTFTSC_AGCKNFFWKTFTSC", "unique_interaction_mi_terms": "MI:0407 (direct interaction)", "partner_A_sequence": "AGCKNFFWKTFTSC", "partner_B_sequence": "AGCKNFFWKTFTSC", "partner_A_length": "14", "partner_B_length": "14", "partner_A_mol_type": "protein", "partner_B_mol_type": "protein", "intact_interaction_ids": "EBI-20824248 | EBI-26453408", "n_intact_interaction_ids": "2", "partner_A_intact_interactor_ids": "intact:EBI-20824010", "partner_B_intact_interactor_ids": "intact:EBI-20824010", "partner_A_n_intact_interactor_ids": "1", "partner_B_n_intact_interactor_ids": "1", "partner_A_uniprot_ids": "P61278-PRO_0000033088", "partner_B_uniprot_ids": "P61278-PRO_0000033088", "partner_A_gene_names": "SMS_HUMAN", "partner_B_gene_names": "SMS_HUMAN", "partner_A_species": "9606 (human)", "partner_B_species": "9606 (human)", "mi_scores_all": "0.72", "mi_score_mean": "0.72", "mi_score_min": "0.72", "mi_score_max": "0.72", "assays_unique": "MI:0055 (fluorescent resonance energy transfer) | MI:0071 (molecular sieving) | MI:1232 (aggregation assay)", "n_assays_unique": "3", "n_papers": "2", "pubmed_ids": "28650319 | 32915532", "publication_years_all": "2017 | 2020", "publication_year_min": "2017", "publication_year_max": "2020", "feature_relationship_types": null, "feature_relationship_summary": null, "has_mutation_relationship": "False", "has_ptm_relationship": "False", "has_binding_site_relationship": "False", "partner_A_wildtype_sequence_if_feature_modified": null, "partner_B_wildtype_sequence_if_feature_modified": null, "wildtype_seq_sort_if_feature_modified": null, "wildtype_SNOOPPI_final_label": null, "wildtype_MI0915_final_label": null, "exact_sequence_pair_intact_label_summary": "positive", "snooppi_label_transition_from_intact": "confirmed", "seq_sort_origin_categories": "exact_seq_sort_seen_in_original_intact", "unique_expansions": null, "partner_A_go_terms": null, "partner_B_go_terms": null, "partner_A_dip": null, "partner_B_dip": null, "partner_A_ensg": null, "partner_B_ensg": null, "partner_A_ense": null, "partner_B_ense": null, "partner_A_enst": null, "partner_B_enst": null, "partner_A_interpro": null, "partner_B_interpro": null, "partner_A_rcsb_pdb": null, "partner_B_rcsb_pdb": null, "partner_A_reactome": null, "partner_B_reactome": null, "partner_A_host_context": "labels_full = In vitro", "partner_B_host_context": "labels_full = In vitro", "partner_A_sequence_snooppi_degree_by_label": "positive=3; negative=0; unknown=0", "partner_B_sequence_snooppi_degree_by_label": "positive=3; negative=0; unknown=0", "partner_A_sequence_modified_role_counts": null, "partner_B_sequence_modified_role_counts": null, "partner_A_sequence_partner_modified_role_counts": null, "partner_B_sequence_partner_modified_role_counts": null, "partner_A_gene_snooppi_degree_by_label": "positive=3; negative=0; unknown=3", "partner_B_gene_snooppi_degree_by_label": "positive=3; negative=0; unknown=3", "partner_A_gene_modified_role_counts": null, "partner_B_gene_modified_role_counts": null, "partner_A_gene_partner_modified_role_counts": "mutation=3", "partner_B_gene_partner_modified_role_counts": "mutation=3"}
+{"SNOOPPI_final_label": "positive", "MI0915_final_label": "positive", "SNOOPPI_Partner_A_ID": "SNOOPPI_partner_00000839", "SNOOPPI_Partner_B_ID": "SNOOPPI_partner_00004454", "SNOOPPI_Pair_ID": "SNOOPPI_pair_00001287", "SNOOPPI_Labeled_Pair_ID": "POSITIVE_SNOOPPI_pair_00001287", "seq_sort": "AGCKNFFWKTFTSC_DAEFRHDSGYEVHHQKLVFFAEDVGSNKGAIIGLMVGGVVIA", "unique_interaction_mi_terms": "MI:0407 (direct interaction)", "partner_A_sequence": "AGCKNFFWKTFTSC", "partner_B_sequence": "DAEFRHDSGYEVHHQKLVFFAEDVGSNKGAIIGLMVGGVVIA", "partner_A_length": "14", "partner_B_length": "42", "partner_A_mol_type": "protein", "partner_B_mol_type": "protein", "intact_interaction_ids": "EBI-20824302 | EBI-20824324 | EBI-26453382 | EBI-26453443 | EBI-26453454 | EBI-26453608", "n_intact_interaction_ids": "6", "partner_A_intact_interactor_ids": "intact:EBI-20824010", "partner_B_intact_interactor_ids": "intact:EBI-821758", "partner_A_n_intact_interactor_ids": "1", "partner_B_n_intact_interactor_ids": "1", "partner_A_uniprot_ids": "P61278-PRO_0000033088", "partner_B_uniprot_ids": "P05067-PRO_0000000092", "partner_A_gene_names": "SMS_HUMAN", "partner_B_gene_names": "A4_HUMAN", "partner_A_species": "9606 (human)", "partner_B_species": "9606 (human)", "mi_scores_all": "0.77", "mi_score_mean": "0.77", "mi_score_min": "0.77", "mi_score_max": "0.77", "assays_unique": "MI:0065 (isothermal titration calorimetry) | MI:0069 (mass spectrometry studies of complexes) | MI:0071 (molecular sieving) | MI:0404 (comigration in non denaturing gel electrophoresis) | MI:0808 (comigration in sds page) | MI:1103 (solution state nmr) | MI:1232 (aggregation assay)", "n_assays_unique": "7", "n_papers": "2", "pubmed_ids": "28650319 | 32915532", "publication_years_all": "2017 | 2020", "publication_year_min": "2017", "publication_year_max": "2020", "feature_relationship_types": null, "feature_relationship_summary": null, "has_mutation_relationship": "False", "has_ptm_relationship": "False", "has_binding_site_relationship": "False", "partner_A_wildtype_sequence_if_feature_modified": null, "partner_B_wildtype_sequence_if_feature_modified": null, "wildtype_seq_sort_if_feature_modified": null, "wildtype_SNOOPPI_final_label": null, "wildtype_MI0915_final_label": null, "exact_sequence_pair_intact_label_summary": "positive", "snooppi_label_transition_from_intact": "confirmed", "seq_sort_origin_categories": "exact_seq_sort_seen_in_original_intact", "unique_expansions": null, "partner_A_go_terms": null, "partner_B_go_terms": null, "partner_A_dip": null, "partner_B_dip": null, "partner_A_ensg": null, "partner_B_ensg": null, "partner_A_ense": null, "partner_B_ense": null, "partner_A_enst": null, "partner_B_enst": null, "partner_A_interpro": null, "partner_B_interpro": null, "partner_A_rcsb_pdb": null, "partner_B_rcsb_pdb": null, "partner_A_reactome": null, "partner_B_reactome": null, "partner_A_host_context": "labels_full = In vitro", "partner_B_host_context": "labels_full = In vitro", "partner_A_sequence_snooppi_degree_by_label": "positive=3; negative=0; unknown=0", "partner_B_sequence_snooppi_degree_by_label": "positive=79; negative=0; unknown=152", "partner_A_sequence_modified_role_counts": null, "partner_B_sequence_modified_role_counts": "ptm=2", "partner_A_sequence_partner_modified_role_counts": null, "partner_B_sequence_partner_modified_role_counts": "mutation=10; binding_site=176", "partner_A_gene_snooppi_degree_by_label": "positive=3; negative=0; unknown=3", "partner_B_gene_snooppi_degree_by_label": "positive=155; negative=28; unknown=2363", "partner_A_gene_modified_role_counts": null, "partner_B_gene_modified_role_counts": "mutation=1542; ptm=8; binding_site=1142", "partner_A_gene_partner_modified_role_counts": "mutation=3", "partner_B_gene_partner_modified_role_counts": "mutation=71; binding_site=1835"}
+{"SNOOPPI_final_label": "positive", "MI0915_final_label": "positive", "SNOOPPI_Partner_A_ID": "SNOOPPI_partner_00001273", "SNOOPPI_Partner_B_ID": "SNOOPPI_partner_00155352", "SNOOPPI_Pair_ID": "SNOOPPI_pair_00002127", "SNOOPPI_Labeled_Pair_ID": "POSITIVE_SNOOPPI_pair_00002127", "seq_sort": "AKERCGSGK_MGAPTLPPAWQPFLKDHRISTFKNWPFLEGCACTPERMAEAGFIHCPTENEPDLAQCFFCFKELEGWEPDDDPIEEHKKHSSGCAFLSVKKQFEELTLGEFLKLDRERAKNKIAKETNNKKKEFEETAKKVRRAIEQLAAMD", "unique_interaction_mi_terms": "MI:0407 (direct interaction)", "partner_A_sequence": "AKERCGSGK", "partner_B_sequence": "MGAPTLPPAWQPFLKDHRISTFKNWPFLEGCACTPERMAEAGFIHCPTENEPDLAQCFFCFKELEGWEPDDDPIEEHKKHSSGCAFLSVKKQFEELTLGEFLKLDRERAKNKIAKETNNKKKEFEETAKKVRRAIEQLAAMD", "partner_A_length": "9", "partner_B_length": "142", "partner_A_mol_type": "peptide", "partner_B_mol_type": "protein", "intact_interaction_ids": "EBI-15949929 | EBI-15949965 | EBI-15949982", "n_intact_interaction_ids": "3", "partner_A_intact_interactor_ids": "intact:EBI-15949926", "partner_B_intact_interactor_ids": "intact:EBI-518823", "partner_A_n_intact_interactor_ids": "1", "partner_B_n_intact_interactor_ids": "1", "partner_A_uniprot_ids": null, "partner_B_uniprot_ids": "O15392-0", "partner_A_gene_names": null, "partner_B_gene_names": "BIRC5_HUMAN", "partner_A_species": "9606 (human)", "partner_B_species": "-2 (chemical synthesis)", "mi_scores_all": "0.62", "mi_score_mean": "0.62", "mi_score_min": "0.62", "mi_score_max": "0.62", "assays_unique": "MI:0069 (mass spectrometry studies of complexes) | MI:0071 (molecular sieving) | MI:0114 (x-ray crystallography)", "n_assays_unique": "3", "n_papers": "1", "pubmed_ids": "22032967", "publication_years_all": "2011", "publication_year_min": "2011", "publication_year_max": "2011", "feature_relationship_types": null, "feature_relationship_summary": null, "has_mutation_relationship": "False", "has_ptm_relationship": "False", "has_binding_site_relationship": "False", "partner_A_wildtype_sequence_if_feature_modified": null, "partner_B_wildtype_sequence_if_feature_modified": null, "wildtype_seq_sort_if_feature_modified": null, "wildtype_SNOOPPI_final_label": null, "wildtype_MI0915_final_label": null, "exact_sequence_pair_intact_label_summary": "positive", "snooppi_label_transition_from_intact": "confirmed", "seq_sort_origin_categories": "exact_seq_sort_seen_in_original_intact", "unique_expansions": null, "partner_A_go_terms": null, "partner_B_go_terms": null, "partner_A_dip": null, "partner_B_dip": null, "partner_A_ensg": null, "partner_B_ensg": null, "partner_A_ense": null, "partner_B_ense": null, "partner_A_enst": null, "partner_B_enst": null, "partner_A_interpro": null, "partner_B_interpro": null, "partner_A_rcsb_pdb": null, "partner_B_rcsb_pdb": null, "partner_A_reactome": null, "partner_B_reactome": null, "partner_A_host_context": "labels_full = In vitro", "partner_B_host_context": "labels_full = In vitro", "partner_A_sequence_snooppi_degree_by_label": "positive=1; negative=0; unknown=0", "partner_B_sequence_snooppi_degree_by_label": "positive=11; negative=0; unknown=102", "partner_A_sequence_modified_role_counts": null, "partner_B_sequence_modified_role_counts": null, "partner_A_sequence_partner_modified_role_counts": null, "partner_B_sequence_partner_modified_role_counts": "ptm=20; binding_site=120", "partner_A_gene_snooppi_degree_by_label": null, "partner_B_gene_snooppi_degree_by_label": "positive=18; negative=5; unknown=105", "partner_A_gene_modified_role_counts": null, "partner_B_gene_modified_role_counts": "mutation=14; binding_site=3", "partner_A_gene_partner_modified_role_counts": null, "partner_B_gene_partner_modified_role_counts": "ptm=25; binding_site=120"}

examples/source_records.json

@@ -0,0 +1,20 @@
+{
+  "dataset": "ChatterjeeLab/SNOOPPI",
+  "revision": "a1c75633343e292db8bb25e3356d0b8cd64afa91",
+  "file": "data/positive-00000-of-00001.parquet",
+  "source_rows_zero_based": [
+    8,
+    10,
+    52,
+    56,
+    110,
+    112,
+    113,
+    137
+  ],
+  "source_file_sha256": "5dbea543d4e425d25df99fa69650f1ba50c918f84a9a9d1b8bd7ce18cd7bbf7d",
+  "demo_row": 52,
+  "pubmed_ids": "34487793",
+  "retrieved": "2026-09-20",
+  "meaning": "Existing curated interaction labels, preserved without invented affinities."
+}