| name | complexa-design |
| description | End-to-end Proteina-Complexa design pipeline driver. Reach for this skill whenever the user wants to "design a binder", "design binders for X", "run complexa design", "de novo binder", "PDL1 binder", "TrkA binder", "design proteins for target", "protein binder design", "ligand binder", "design a small-molecule binder", "ATP-binding protein", "AME motif scaffolding", "scaffold a motif near a ligand", "motif + ligand design", "enzyme scaffolding", "flow matching protein design", "beam-search binder", "FK steering", "MCTS protein design", "refold with AF2", "refold with RF3", or wants success rates, interface pAE, scRMSD, or FoldSeek diversity from a single command. This is the scientific anchor of the skill set: it drives `complexa design <pipeline>` from target picking to manifest emission and tells the user how many designs passed.
|
| compatibility | complexa CLI installed (pip install -e .); .env populated; 1x CUDA GPU >=40GB VRAM (A100/H100/L40S); 24 CPUs; ~50GB disk |
| allowed-tools | Bash, Read, Write, AskUserQuestion |
Complexa Design Skill
Drive the full four-stage complexa design pipeline: generate (flow matching +
search) -> filter (top-N by reward) -> evaluate (refold with AF2/RF3) ->
analyze (success rate, FoldSeek/MMseqs diversity). Pick the right pipeline
config for the design intent, validate the run upfront so the user does not
discover a missing ckpt mid-folding, run it, and emit a replayable manifest +
per-design success CSV.
What this skill enables
- Protein binder design for protein targets (AF2 reward + ColabDesign refold).
- Ligand binder design for small-molecule targets (RF3 reward + RF3 refold).
- AME motif scaffolding with ligand context (motif + ligand features, RF3).
- Search-based optimization: single-pass, best-of-n, beam-search, fk-steering, mcts.
- Refold backends: ColabDesign (AF2), RF3, Boltz2, ESMFold (fast iteration).
- Pass-rate + diversity analysis with per-
result_type thresholds.
Step 1: Pre-flight
Always run the shared preflight before launching a design — generation needs the
GPU and the right checkpoint, evaluation needs AF2/RF3 weights and tool
binaries. Bail early if the host cannot run the chosen pipeline.
bash .claude/skills/_shared/scripts/preflight.sh
Read ./complexa_setup/preflight.json and bail if any of these are missing for
the chosen pipeline:
gpu.available: false -> all pipelines fail.gpu.vram_gb < 40 -> generation OOMs at default batch_size: 16; lower to 8.ckpts.complexa[.ckpt] -> required for protein binder.ckpts.complexa_ligand[.ckpt] -> required for ligand binder.ckpts.complexa_ame[.ckpt] -> required for AME.env.AF2_DIR missing -> protein binder default eval (colabdesign) fails.env.RF3_CKPT_PATH or env.RF3_EXEC_PATH missing -> ligand binder / AME default eval (rf3_latest) fails.
If a ckpt is missing, point at complexa-setup and have the user run
complexa download --complexa-<variant> first.
Step 2: Pick the pipeline
Complexa has one default pipeline (protein binder) and two extensions
(ligand binder, AME / enzyme). The pipeline is selected entirely
by the configs/search_*_pipeline.yaml you pass to complexa design — each
YAML pins its own model checkpoint, autoencoder, targets dict, default reward,
and default refold backend. Switching pipelines is just "swap the config path
and the target name comes from a different dict".
Default — protein binder
complexa design configs/search_binder_local_pipeline.yaml \
++run_name=pdl1_v1 ++generation.task_name=02_PDL1
Use this when the user says "design a binder for X", "PDL1 binder",
"de novo binder", "design proteins for a target", etc. — i.e. the target is
a protein surface. The config pins complexa.ckpt + complexa_ae.ckpt, reads
targets from configs/targets/targets_dict.yaml, rewards with AF2
(af2folding), inverse-folds with SolubleMPNN, and evaluates with
ColabDesign / AF2. If the user did not specify, this is what they want.
Extension A — ligand binder (small-molecule pocket)
complexa design configs/search_ligand_binder_local_pipeline.yaml \
++run_name=v11_v1 ++generation.task_name=39_7V11_LIGAND \
++metric.binder_folding_method=rf3_latest
Switch to this when the user says "ligand binder", "small-molecule pocket",
"SMILES target", "ATP-binding protein", or names a target ending in
_LIGAND / from the FAD / SAM / OQO / 7V11 / etc. families. The config
also activates LoRA (r=32, lora_alpha=64) which is required for the
released ligand checkpoint — leave the lora: block alone.
Extension B — AME / motif + ligand (enzyme scaffolding)
complexa design configs/search_ame_local_pipeline.yaml \
++run_name=ame_chm ++generation.task_name=M0096_1chm
Switch to this when the user says "scaffold a motif near a ligand",
"active-site design", "enzyme scaffolding", "AME", or names a target like
M0024_1nzy, M0096_1chm (the M####_<pdb> AME task naming). The config sets
env_vars.USE_V2_COMPLEXA_ARCH=True (the CLI runner injects it into the
subprocess) and uses both MotifFeatures and LigandFeatures. Default search
is single-pass; switch to best-of-n only if you also enable the
CompositeRewardModel (commented out in ame_generate.yaml).
Pipeline cheat sheet — what changes when you switch
| Knob | Protein binder (default) | Ligand binder | AME (enzyme) |
|---|
| Pipeline YAML | configs/search_binder_local_pipeline.yaml | configs/search_ligand_binder_local_pipeline.yaml | configs/search_ame_local_pipeline.yaml |
| Model ckpt | complexa.ckpt | complexa_ligand.ckpt | complexa_ame.ckpt |
| Autoencoder ckpt | complexa_ae.ckpt | complexa_ligand_ae.ckpt | complexa_ame_ae.ckpt |
| Targets dict | configs/targets/targets_dict.yaml | configs/targets/ligand_targets_dict.yaml | configs/design_tasks/ame_dict_v2.yaml |
| Task-name pattern | <NN>_<NAME> (e.g. 02_PDL1, 22_DerF21) | <NN>_<PDB>_LIGAND (e.g. 39_7V11_LIGAND) | M####_<pdb> (e.g. M0096_1chm) |
USE_V2_COMPLEXA_ARCH | (unset → v1) | (unset → v1) | "True" (set in YAML) |
| LoRA | (none) | required (r=32, alpha=64) | required |
| Default search algo | best-of-n | best-of-n | single-pass |
| Reward model | AF2 (af2folding) | RF3 (rf3folding) | null (no reward at default) |
| Inverse folder | soluble_mpnn | ligand_mpnn | ligand_mpnn |
For the full per-pipeline breakdown (reward weights, success thresholds,
analysis modes), see reference/pipelines.md.
Step 3: Gather parameters
Use AskUserQuestion to fill in the four parameters that vary every run. Default
to sensible production settings if the user has no preference.
- Target name — must be a key in the relevant dict (
targets_dict.yaml for
protein binder, ligand_targets_dict.yaml for ligand, ame_dict_v2.yaml for
AME). If the user names a target that is not in the dict, hand off to
complexa-target to add it first.
- Run name — a short identifier appended to the output dir (e.g.
pdl1_v1).
- Search algorithm — default to
beam-search with beam_width=8 and
n_branch=4 for production. Use single-pass for a quick smoke test.
- Evaluation refold backend — protein binder defaults to
colabdesign
(AF2); ligand/AME default to rf3_latest. Use esmfold for fast iteration
(worse but seconds per sample).
Step 4: Validate
Validate before running. This is cheap (seconds) and catches missing ckpts,
missing env vars, unknown override keys, and missing target entries — all of
which would otherwise abort the pipeline mid-evaluation after hours of
generation.
complexa validate design configs/search_binder_local_pipeline.yaml \
++generation.task_name=02_PDL1 \
++metric.binder_folding_method=colabdesign
The validator returns non-zero on failure and prints a pass/fail report.
Re-run the command with the suggested overrides until it returns clean.
Step 5: Run the pipeline
complexa design is the right tool for the full 4-stage run — it orchestrates
generate → filter → evaluate → analyze as sequential subprocesses with a
shared run name, log dir, and multi-GPU split (see run_design_pipeline in
src/proteinfoundation/cli/cli_runner.py). Re-implementing that manually
loses the per-stage log routing and progress prints.
Use ++ (forced) Hydra overrides; they apply to all stages. The minimal
production protein-binder invocation:
complexa design configs/search_binder_local_pipeline.yaml \
++run_name=pdl1_v1 \
++generation.task_name=02_PDL1 \
++generation.search.algorithm=beam-search \
++generation.search.beam_search.beam_width=8 \
++metric.binder_folding_method=colabdesign
For ligand binder / AME, swap the pipeline YAML and target name per Step 2's
cheat sheet — every other override above is pipeline-agnostic and can be
reused as-is.
Add --verbose to stream logs to the terminal instead of ./logs/. The skill
does not poll progress — the user re-invokes if they want a status; point them
at complexa status and ./logs/design_pipeline_*/.
Direct module invocation (debug fallback)
To debug a single stage without pipeline orchestration, invoke the underlying
Hydra module directly. complexa generate CONFIG is just a logged subprocess
wrapper around this:
python -m proteinfoundation.generate \
--config-path "$(realpath configs)" \
--config-name search_binder_local_pipeline \
++run_name=debug_pdl1 \
++generation.task_name=02_PDL1
Same pattern for proteinfoundation.{filter,evaluate,analyze}. Use this when
you want to attach ipdb, run under nsys, or skip the pipeline log dir.
Prefer complexa generate/filter/evaluate/analyze for normal one-shot runs
(you get logging and parallel job splitting for free).
AME-specific gotcha (when running AME with RF3 refold): RF3 will try to
complete missing atoms on the ligand based on its CCD code, which produces
shape errors in RMSD calculations and the wrong structure. Before RF3 sees the
PDB, rename the ligand residue to L:0 so RF3 treats it as a generic ligand
and skips atom completion. If your AME generation outputs already encode the
ligand this way (the canonical Complexa pipeline does), no extra step is
needed; otherwise patch each PDB with atomworks.io:
from atomworks.io import load_any, to_pdb_file
atom_array = load_any("my_design.pdb")[0]
ligand_mask = atom_array.chain_id == "A"
atom_array.res_name[ligand_mask] = "L:0"
to_pdb_file(atom_array, "my_design_rf3_ready.pdb")
Same applies if you're piping AME outputs into the complexa-evaluate-pdbs
skill with an RF3 backend. Skip the rename for AF2/colabdesign refold or for
non-AME pipelines.
Wall-clock at default (nsteps=400, beam_width=8, batch_size=16, 100
designs, colabdesign eval) is ~30–120 minutes on a single A100/H100.
Step 6: Collect results
Outputs land in two directories. Surface both:
ls ./inference/${CONFIG_STEM}_${TASK}_*${RUN_NAME}/
ls ./evaluation_results/${RUN_NAME}/
Read the combined results CSV and summarize:
ls ./evaluation_results/*/binder_results_*_combined.csv
ls ./evaluation_results/*/motif_binder_results_*_combined.csv
ls ./evaluation_results/*/res_filter_*_pass_*.csv
ls ./evaluation_results/*/res_div_foldseek_*.csv
Pull the success rate from res_filter_binder_pass_*.csv, the per-design
metrics (interface pAE, pLDDT, scRMSD) from the combined CSV, and FoldSeek
TM-score diversity from res_div_foldseek_*.csv. Report top-N designs by
i_pAE (protein binder) or min_ipAE (ligand binder).
Step 7: Emit manifest
Drop a JSON manifest beside the results so the run is replayable. The shared
helper captures the command, config, git SHA, and pointers to the result CSVs.
python3 .claude/skills/_shared/scripts/write_manifest.py \
--output-dir ./evaluation_results/${RUN_NAME} \
--command "complexa design configs/search_binder_local_pipeline.yaml ++run_name=${RUN_NAME} ++generation.task_name=${TASK}" \
--skill complexa-design \
--out ./run_manifest.json
Surface the manifest path and the result CSV to the user.
Most-common overrides
The 10 overrides that cover ~90% of runs. Full reference (every key, type,
default) is in reference/overrides.md.
| Override | Default | What it controls |
|---|
++generation.task_name=<name> | (per config) | Which target / AME task to design for |
++run_name=<str> | (config stem) | Output dir suffix and CSV tag |
++generation.search.algorithm=beam-search | best-of-n (binder/ligand), single-pass (AME) | Search strategy |
++generation.search.beam_search.beam_width=8 | 4 | Beam-search width (more = better designs, slower) |
++generation.args.nsteps=200 | 400 | Diffusion steps (fewer = faster, lower quality) |
++generation.dataloader.batch_size=8 | 16 (binder/ligand/AME) | Drop to 8 on a 40GB GPU |
++generation.filter.filter_samples_limit=500 | 1000 | Top-N samples to keep after filtering |
++metric.binder_folding_method=esmfold | colabdesign (binder), rf3_latest (ligand/AME) | Evaluation refold backend |
++metric.num_redesign_seqs=8 | 2 | ProteinMPNN/LigandMPNN/SolubleMPNN sequences per design |
++aggregation.success_thresholds.i_pAE.threshold=10.0 | 7.0 (protein binder) | Loosen / tighten success criteria |
Hardware requirements
| Resource | Minimum | Recommended |
|---|
| GPU | 1x CUDA GPU, 40 GB VRAM | A100 / H100 / L40S, 80 GB VRAM |
| CPUs | 16 | 24 (the ncpus_ default in every pipeline config) |
| Disk | 50 GB at ./inference/ + ./evaluation_results/ | 200 GB for sweep runs |
| RAM | 32 GB | 64 GB+ |
Typical wall-clock for 100 designs, beam_width=8, default nsteps=400:
- Protein binder + colabdesign refold: ~60–120 min on 1x A100/H100.
- Ligand binder + RF3 refold: ~90–180 min (RF3 dominates).
- AME + RF3 refold: ~120–240 min.
- Any pipeline + ESMFold refold: ~30–60 min (fast iteration).
Bumping gen_njobs=2 and eval_njobs=2 halves wall-clock on a 2-GPU host. See
.claude/skills/_shared/reference/hardware.md for per-pipeline VRAM tables.
Troubleshooting (common cases)
| Symptom | Cause | Fix |
|---|
CUDA out of memory in generate | batch_size: 16 too big on 40GB GPU | ++generation.dataloader.batch_size=8 |
CUDA out of memory in evaluate | AF2 / RF3 batched too aggressively | ++eval_njobs=1 and ++metric.num_redesign_seqs=2 |
InterpolationKeyError: AF2_DIR | colabdesign eval but .env does not set AF2_DIR | Set AF2_DIR in .env or ++metric.binder_folding_method=esmfold |
InterpolationKeyError: RF3_CKPT_PATH | RF3 eval but RF3 not installed | complexa download --all or switch eval backend |
KeyError: 'task_name' not in target_dict_cfg | Target not in targets_dict.yaml / ligand_targets_dict.yaml / ame_dict_v2.yaml | Use complexa-target skill to add it |
| 0 designs pass success thresholds | Defaults too strict for this target | Loosen via ++aggregation.success_thresholds.* |
For the full list (chain-ID mismatches, hotspot residues, ligand residue
renaming for RF3, missing inverse-folding models, etc.) see
reference/troubleshooting.md.
For per-pipeline details (which model, reward, inverse folding, evaluation
backend, result_type, LoRA settings, USE_V2_COMPLEXA_ARCH toggle), see
reference/pipelines.md.
For the full override reference (every generation.*, metric.*,
aggregation.* key with type, default, example, and effect), see
reference/overrides.md.
For all troubleshooting cases (cause + fix + source), see
reference/troubleshooting.md.
