Initial commit: model-agnostic inference-time chemical steering

foldsteer improves the chemical validity of ligand geometry predicted by
AF3-style diffusion models, without retraining and without modifying host
model weights. It implements the two mechanisms used by Boltz-1x and
Protenix-v2:

  - physical guidance: gradient descent on a flat-bottom chemical energy
    applied to the denoiser's x0 prediction each diffusion step
  - Feynman-Kac steering: particle-population resampling toward low-energy
    trajectories, using stepwise energy improvement as the FK potential

The engine is host-agnostic: it consumes a ChemicalContext of atom-indexed
constraint tensors and exposes two pure functions (guide, resample_indices).
It never sees the denoiser, noise schedule, or model, so adding a host means
writing one adapter. Nothing from OpenFold3 is imported at module scope.

Nine potentials, all flat-bottom, so valid geometry yields exactly zero energy
and zero gradient and steering is inert on good predictions. Beyond the Boltz
term set this adds sp2 planarity, sp3 non-planarity, and amide planarity,
which respond to the revised validity criteria in the Protenix-v2 report: a
direct port of Boltz's double-bond-keyed planarity term covers neither
aromatic rings (RDKit types them AROMATIC) nor amides (typed SINGLE).

Validation: every analytic gradient checked against autograd (max error
~1e-15); the three documented failure modes detected; end-to-end against a
mock sampler reproducing OpenFold3's rollout contract.

See DESIGN.md for the source analysis and OpenFold3 integration points, and
NOTICE for attribution to Boltz (MIT) and Protenix (Apache-2.0).

Author: foldsteer

.github/workflows/tests.yml

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+name: tests
+
+on:
+  push:
+    branches: [main]
+  pull_request:
+  workflow_dispatch:
+
+jobs:
+  test:
+    runs-on: ubuntu-latest
+    strategy:
+      fail-fast: false
+      matrix:
+        python-version: ["3.10", "3.12"]
+    steps:
+      - uses: actions/checkout@v4
+      - uses: actions/setup-python@v5
+        with:
+          python-version: ${{ matrix.python-version }}
+      - name: Install
+        run: |
+          python -m pip install --upgrade pip
+          # CPU-only torch keeps the runner fast; foldsteer needs no GPU.
+          pip install torch --index-url https://download.pytorch.org/whl/cpu
+          pip install -e '.[test]'
+      - name: Run tests
+        run: pytest tests/ -q

.gitignore

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+__pycache__/
+*.py[cod]
+*.egg-info/
+build/
+dist/
+.pytest_cache/
+.ruff_cache/
+.venv/
+venv/
+.DS_Store

DESIGN.md

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LICENSE

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+foldsteer
+Copyright 2026 the foldsteer authors
+
+Licensed under the Apache License, Version 2.0. See LICENSE.
+
+------------------------------------------------------------------------------
+Attribution
+------------------------------------------------------------------------------
+
+This project is an independent, model-agnostic reimplementation of
+inference-time chemical steering for AF3-style structure prediction. Its design
+and default parameters derive from two prior open-source implementations, and
+neither project is affiliated with or endorses this one.
+
+1. Boltz / Boltz-1x
+   https://github.com/jwohlwend/boltz
+   Copyright (c) 2024 Jeremy Wohlwend, Gabriele Corso, Saro Passaro
+   MIT License.
+
+   The flat-bottom potential formulation, the Feynman-Kac steering scheme
+   (including the use of stepwise energy improvement as the FK potential and
+   the guidance likelihood correction), and the default term weights in
+   `foldsteer.engine.default_terms` follow the released Boltz implementation
+   (src/boltz/model/potentials/potentials.py and
+   src/boltz/model/modules/diffusion.py).
+
+2. Protenix / Protenix-v2
+   https://github.com/bytedance/Protenix
+   Copyright (c) ByteDance Ltd. and/or its affiliates
+   Apache License 2.0.
+
+   The separation of steering into a standalone, config-driven subpackage with
+   a potential class registry follows the structure of `protenix/tfg/`. The
+   sp2-planarity, sp3-non-planarity, and amide-planarity terms respond to the
+   revised validity criteria described in the Protenix-v2 technical report.
+
+Constraint extraction uses RDKit (BSD 3-Clause), https://www.rdkit.org/
+
+The OpenFold3 adapter targets https://github.com/aqlaboratory/openfold-3
+(Apache License 2.0) but vendors no OpenFold3 code; OpenFold3 is imported
+lazily and is not a required dependency.

NOTICE

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+# foldsteer
+
+Inference-time chemical steering for AF3-style biomolecular structure
+prediction. Improves the chemical validity of predicted ligand geometry
+(stereochemistry, planarity, bond geometry, steric clash) without retraining
+and without modifying the host model's weights.
+
+Implements the two mechanisms used by Boltz-1x and Protenix-v2:
+
+* **physical guidance** — gradient descent on a flat-bottom chemical energy,
+  applied to the denoiser's `x0` prediction at each diffusion step;
+* **Feynman-Kac steering** — sample a particle population and resample it
+  toward low-energy trajectories.
+
+The engine is host-agnostic. It consumes a `ChemicalContext` of atom-indexed
+constraint tensors and exposes two pure functions (`guide`, `resample_indices`);
+it never sees the denoiser, the noise schedule, or the model. Adding a host
+means writing one adapter.
+
+## Install
+
+```bash
+pip install -e .                 # engine only, no folding model needed
+pip install -e '.[openfold3]'    # with the OpenFold3 adapter
+```
+
+## Use with OpenFold3
+
+```python
+from foldsteer import default_config
+from foldsteer.adapters.openfold3 import patch_sample_diffusion
+
+unpatch = patch_sample_diffusion(model, default_config(num_particles=3))
+# ... run inference as normal ...
+unpatch()
+```
+
+Guidance alone (cheaper, most of the benefit):
+
+```python
+patch_sample_diffusion(model, default_config(fk_steering=False))
+```
+
+## Use standalone
+
+```python
+from foldsteer import SteeringEngine, default_config
+from foldsteer.adapters.rdkit_source import context_from_mols
+
+ctx = context_from_mols([mol], [{i: i for i in range(mol.GetNumAtoms())}],
+                        n_atoms=coords.shape[-2])
+engine = SteeringEngine(default_config(), ctx)
+coords = coords + engine.guide(coords, t=0.5)   # t: 1 = noisy, 0 = clean
+```
+
+## Validation
+
+![validation](docs_validation.png)
+
+**(a)** Guidance relaxes a distorted ligand. **(b)** Ablation over 6 seeds
+against a mock sampler reproducing OpenFold3's rollout contract. **(c)** The
+package detects the failure modes Protenix-v2 reports slipping past standard
+PoseBusters validity; valid geometry scores exactly zero.
+
+Every analytic gradient is verified against autograd to ~1e-15. See
+[DESIGN.md](DESIGN.md) §7 for the full results and caveats.
+
+## Potentials
+
+`BoundsMatrixPotential`, `VDWOverlapPotential`, `ChiralAtomPotential`,
+`StereoBondPotential`, `PlanarBondPotential`, `Sp2CenterPotential`,
+`Sp3CenterPotential`, `ConjugatedTorsionPotential`, `ConnectionsPotential`.
+
+All flat-bottom: zero energy and zero gradient on valid geometry, so steering
+is inert on structures the model already got right.
+
+## Tests
+
+```bash
+pytest tests/ -q      # 17 tests, no GPU or model weights required
+```
+
+Every analytic gradient is checked against autograd (max error ~1e-15). See
+`DESIGN.md` for the full analysis, the OpenFold3 integration points, and
+validation results.
+
+## Status
+
+Prototype. The engine, potentials, and RDKit extraction are tested. The
+OpenFold3 `AtomArray` reconstruction path is written against the OF3 API but
+has only been exercised on synthetic arrays — run it against real inference
+output before relying on it.