Boltz Structure And Binding
OpenAIOfficialCreated by BoltzDistributed by OpenAI
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- Tool integration
- Science and research
- MIT
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AutoGineer summary
Written by AutoGineer from the skill’s own files, not by the author
This skill provides a structured workflow for using the Boltz API to perform structural prediction and binding analysis on specific molecular complexes. It covers the entire process from input normalization and cost estimation to job submission and asynchronous result retrieval. It is designed for single-complex analysis rather than high-throughput screening or molecular design.
What it does
- Normalizes molecular entities including proteins, RNA, DNA, and ligands into the required API format.
- Configures binding analysis for ligand-protein or protein-protein interactions.
- Estimates computational costs before job submission.
- Manages asynchronous job execution and background result downloading.
- Summarizes key metrics such as binding confidence and optimization scores from output files.
When to use it
- When you need to fold a specific protein, RNA, DNA, or ligand complex.
- When you need to predict the interface or binding affinity of a defined complex.
- When you need to dock a single ligand into a protein structure.
- When you have a specific molecular system and require structural confidence metrics.
Advantages
- Ensures correct payload formatting to avoid common API validation errors.
- Provides a robust, non-blocking workflow for handling long-running structural predictions.
- Standardizes the handling of complex molecular inputs and binding configurations.
- Facilitates clear reporting of structural and binding confidence metrics.
Usage examples
- “Predict the structure of a protein-ligand complex with protein chain A and ligand chain B.”
- The agent will normalize the entities, request a cost estimate, submit the job, and initiate a background download of the resulting CIF structure and metrics.
- “What is the binding confidence for this protein-protein complex?”
- The agent will configure the binding block for protein-protein interaction, run the prediction, and extract the binding_confidence metric from the results.
Install
Download the skill as a ZIP:
https://autogineer.com/agent-skills/openai/boltz-structure-and-binding/downloadUnzip it into the cross-client skills folder of your project:
mkdir -p .agents/skills && unzip boltz-structure-and-binding.zip -d .agents/skills
Documentation by Boltz
Workflow
If boltz-api is missing from PATH, use boltz-cli-setup for install/update guidance before retrying.
If a command reports missing or expired authentication, use boltz-cli-setup to start boltz-api auth login --device-code before retrying; do not ask permission first.
If the agent host sandbox blocks boltz-api install/auth/API calls, use boltz-cli-setup to request the host sandbox bypass/escalation needed for user-wide CLI install, browser login, credential storage, temp files, or API access before retrying.
Use this skill for one defined complex, not a library workflow.
Normalize the inputs into
entities. Each entity is{type, chain_ids, value}— note pluralchain_ids(an array, even for one chain) and the field isvalue, notsequence:{"entities": [{"type": "protein", "chain_ids": ["A"], "value": "MKTAYIAKQRQISFVKSHFSRQ"}]}typeis one ofprotein | rna | dna | ligand_smiles | ligand_ccd. Chain IDs go in entity order (A,B,C, …) unless the user specifies otherwise. Readreferences/api.mdfor per-type field variants (cyclic,modifications, ligand CCD codes, etc.) before authoring your first payload — agent guesses likesequence:orchain_id: "A"(singular) fail with unclear 400 errors.If the user wants binding metrics, add a flat
bindingblock with an explicittypefield. For ligand-protein binding use:binding: type: ligand_protein_binding binder_chain_id: BFor protein-protein binding use:
binding: type: protein_protein_binding binder_chain_ids: [B]Do not nest the variant name under
binding(for example, nobinding.ligand_protein_bindingobject).Supported optional features include
constraints,bonds,modifications,model_options, and binding metrics; only add them if the user asks. Read references/api.md for exact shapes and examples.Author the payload YAML or JSON, run
estimate-cost, show the USD cost, wait for explicit confirmation.startto submit (synchronous). Capture the ID.Launch
download-resultswith the agent runtime's background/non-blocking command facility so polling + download continue without blocking the agent session. In Claude Code, use Bash withrun_in_background: true. In Codex, rundownload-resultsas a foreground shell command withyield_time_ms: 1000; if Codex returns asession_id, keep it for optional same-thread polling, but treatdownload-statusplus the run directory as the durable source of truth. In Codex app/desktop runtimes that expose same-thread heartbeat automations, create a heartbeat that checksdownload-statusperiodically and posts a concise completion or failure update when the download reaches a terminal state. After launching the downloader, always report the job ID, run name, and output directory. Include the next check cadence if the heartbeat was created; otherwise include thedownload-statuscommand.
Command Pattern
# Replace placeholders with concrete absolute paths before running.
# Use a short descriptive run name, for example: sab-<target>-<ligand>-v1
# 1. estimate
boltz-api predictions:structure-and-binding estimate-cost \
--model boltz-2.1 \
--input @yaml:///absolute/path/payload.yaml
# 2. confirm with user, then submit
boltz-api predictions:structure-and-binding start \
--model boltz-2.1 \
--idempotency-key "<run-name>" \
--input @yaml:///absolute/path/payload.yaml \
--raw-output --transform id
# 3. Copy the printed job ID into this command, then launch it in the agent
# runtime's background/non-blocking mode.
# Claude Code: Bash with run_in_background=true.
# Codex: foreground shell command with yield_time_ms=1000; keep the returned session_id if one is provided.
# Do not append "&" or use nohup in Codex.
boltz-api download-results \
--id "<job-id-from-start>" --name "<run-name>" \
--root-dir "/absolute/path/boltz-experiments" \
--poll-interval-seconds 10
# -> /absolute/path/boltz-experiments/<run-name>/outputs/archive.tar.gz, .boltz-run.jsonAlways Do This
Keep payload field names exactly as the API body names shown in
references/api.md; then pass the merged payload with--input @yaml:///absolute/path/payload.yamlor@json:///absolute/path/payload.json. Never use@./payload.yamlor@file://for object-typed payloads.Use absolute paths for the output root, payload files, and embedded structure files. Do not
cdinto the run directory for follow-up commands; pass the same--root-dirand use absolute paths so later relative paths do not drift.Residue indices are 0-based wherever the payload asks for residue positions (constraints, modifications, contact tokens).
For CIF/PDB bytes embedded in
--target/structure.data, use@data:///absolute/path/file.cif— it detects binary and base64-encodes. Don't use bare@pathfor binary data.Use the same slug as both
--idempotency-keyat submit time and--nameat download time so re-runs are idempotent and resume from.boltz-run.json.In permission-gated agents such as Claude Code, keep each Boltz call as a top-level command that starts with
boltz-api. Prefer concrete arguments oversh -c, inline environment assignments, aliases, wrapper scripts, loops, or pipelines around theboltz-apiinvocation unless the user already allowed that exact command form. Use--raw-output --transform id, read the printed ID, then paste that literal ID into the nextdownload-resultscommand.Prefer the agent runtime's background/non-blocking command mode for
download-results. In Codex specifically, keepdownload-resultsin the foreground and set the shell tool yield to 1000 ms; Codex will return asession_idif the command is still running. Do not append&or usenohupin Codex because the tool runner may clean up shell-backgrounded descendants before.boltz-run.jsonis fully written.After the background/session starts, do not manually wait on it or run ad hoc polling loops.
download-resultsemits JSONL progress on stderr by default; add--progress-format text --verboseonly when you explicitly want human-readable logs.In Codex app/desktop runtimes with same-thread heartbeat automation support, schedule a heartbeat after launching
download-results. The heartbeat should runboltz-api --format json download-status --name "<run-name>" --root-dir "/absolute/path/boltz-experiments"on a sensible cadence, post only material status changes or terminal completion/failure, and stop once terminal. Poll the savedsession_idwith an emptywrite_stdinonly for interactive, user-requested progress checks. Do not loopretrieveyourself.If the current host has no heartbeat automation support, do not claim an automatic next check. Report the job ID, run name, output directory, and the command needed to check
download-status.If detached download needs to be restarted, re-run
boltz-api download-resultswith the same--name "<run-name>"and the same--root-dir.Poll interval: keep
--poll-interval-seconds 10for SAB — predictions usually finish in under a few minutes.Cost: there is no published per-unit rate to cite for SAB — run
estimate-costand state only the figure it returns. Don't estimate or comment on cost.
Escape Hatch
For anything not covered in references/api.md:
Payload reference: https://api.boltz.bio/docs/api/python/resources/predictions/subresources/structure_and_binding/methods/start
CLI flag names:
boltz-api predictions:structure-and-binding start --help(schema details aren't there — just flag names and types)
Read references/api.md for entity shapes, binding variants, bonds, constraints, model options, and input examples. Read references/results.md when summarizing downloaded outputs, metrics, or validation quirks.
Outputs
Summarize metrics.json and point the user at the downloaded CIF path. Read references/results.md for the local layout, nested metrics, binding metric variants, and SAB validation quirks.
SAB 400 validation quirk
If the server rejects a payload with only {"code":"VALIDATION_ERROR","message":"Request validation failed"}, inspect entities, binding, and constraints; read references/results.md for details.
Files
- ATTRIBUTION.mdAdded by AutoGineer932 B
- LICENSELicence file added by AutoGineer1.0 KiB
- SKILL.md8.6 KiB
- agents/openai.yaml243 B
- references/api.md9.6 KiB
- references/results.md1.6 KiB
references/api.md
Structure and Binding — Payload Reference
This covers the predictions:structure-and-binding endpoint. The request body becomes the --input object passed via @yaml:///absolute/path/payload.yaml. Field names below are API body field names (what you write in the YAML) — not CLI flag names.
Contents
Top-level request
Entity types
bindingbondsconstraintsmodel_optionstemplatesMSA control
Structure templates in a constraint / binding setup
Outputs (after
download-results)400 validation quirk
Escape hatch
Top-level request
# payload.yaml
entities:
- type: protein
chain_ids: [A]
value: MKTAYIAKQRQISFVKSHFSRQ
- type: ligand_smiles
chain_ids: [B]
value: CCO
binding:
type: ligand_protein_binding
binder_chain_id: B
num_samples: 1
model_options:
recycling_steps: 3
sampling_steps: 200
step_scale: 1.638Top-level fields:
entities(required) — list of polymer / ligand entities. Chain IDs across entities must be unique.binding(optional) — include only when you want binding metrics. Two variants below.num_samples(optional, 1-10) — structure samples to generate. Omit for the server default.bonds(optional) — custom covalent bonds; see below.constraints(optional) — pocket / contact constraints; see below.model_options(optional) — see below.templates(optional) — up to 4 CIF/PDB templates to guide protein-chain geometry (Boltz-2.1); seetemplates.
Also passed as separate start flags, not inside the body:
--model boltz-2.1(currently the only option)--idempotency-key <slug>--workspace-id <id>(admin keys only)
Entity types
All entities take type, chain_ids, value.
protein
- type: protein
chain_ids: [A]
value: MKTAYIAKQRQISFVKSHFSRQ
cyclic: false # optional
modifications: [] # optional; server defaults to [] if omitted
# msa: <optional> # omit = automatic MSA generation; see "MSA control" belowrna
- type: rna
chain_ids: [B]
value: ACGUN
cyclic: false
modifications: []dna
- type: dna
chain_ids: [C]
value: ACGTN
cyclic: false
modifications: []ligand_smiles
- type: ligand_smiles
chain_ids: [D]
value: "CCO"ligand_ccd
- type: ligand_ccd
chain_ids: [E]
value: ATPPolymer modifications
Each entry in modifications:
modifications:
- residue_index: 12 # 0-based
type: ccd
value: MSEtype must be ccd — SMILES polymer modifications are not supported (the API rejects them with modifications[].type must be "ccd").
binding
Include only when you want binding metrics.
Ligand–protein
binding:
type: ligand_protein_binding
binder_chain_id: DConstraints:
binder_chain_idmust reference a single ligand chain.Binder ligand must have fewer than 50 atoms.
The entity set may only contain proteins and ligands (no RNA / DNA).
Protein–protein
binding:
type: protein_protein_binding
binder_chain_ids: [B]Returned binding metrics (under output.binding_metrics):
For
ligand_protein_binding:{binding_confidence, optimization_score, type: "ligand_protein_binding_metrics"}.For
protein_protein_binding:{binding_confidence, type: "protein_protein_binding_metrics"}— nooptimization_score.
What they mean:
binding_confidence(0–1) — confidence that binding occurs; the primary signal for whether this complex binds.optimization_score— ranks binding strength for lead optimization (higher = stronger predicted binding). Emitted only forligand_protein_binding, notprotein_protein_binding.
bonds
bonds:
- atom1:
type: polymer_atom
chain_id: A
residue_index: 12 # 0-based
atom_name: SG
atom2:
type: ligand_atom
chain_id: D
atom_name: C1Atom variants:
{type: polymer_atom, chain_id, residue_index, atom_name}— residue_index is 0-based.{type: ligand_atom, chain_id, atom_name}. Atom-level ligand references supportligand_ccdonly — referencing aligand_smileschain by atom is rejected (bad_request: … references SMILES ligand chain … Use ligand_ccd instead). Use aligand_ccdentity for any atom-level bond/contact.
constraints
Pocket constraint
constraints:
- type: pocket
binder_chain_id: D
contact_residues:
A: [10, 11, 35] # 0-based residue indices on each target chain
max_distance_angstrom: 6.0
force: false # optionalContact constraint
constraints:
- type: contact
max_distance_angstrom: 5.0
token1:
type: polymer_contact
chain_id: A
residue_index: 42 # 0-based
token2:
type: ligand_contact
chain_id: D
atom_name: C1
force: falseToken variants:
{type: polymer_contact, chain_id, residue_index}— residue_index is 0-based.{type: ligand_contact, chain_id, atom_name}. As with bonds, atom-level ligand contacts supportligand_ccdonly —ligand_smileschains cannot be referenced by atom.
model_options
model_options:
recycling_steps: 3 # default 3 (per spec)
sampling_steps: 200 # default 200 (per spec)
step_scale: 1.638 # default 1.638 (per spec)Hosted API Reference bounds:
recycling_steps >= 1sampling_steps >= 11.3 <= step_scale <= 2
templates
Up to 4 CIF/PDB templates to guide protein-chain geometry (Boltz-2.1). Each template maps request chains to chains in the template file:
templates:
- template_structure:
type: url # or base64 (use @data:/// for a local file)
url: "https://files.rcsb.org/download/1ABC.cif"
template_chains:
- input_chain_id: A # chain in THIS request
template_chain_id: A # corresponding chain in the template file
force_threshold_angstroms: 10.0 # optional; omit to use the template without forcingtemplate_chains entries are objects ({input_chain_id, template_chain_id}), not bare chain-ID strings. This is distinct from embedding a CIF as raw structure bytes (see the next section).
MSA control
By default (omit msa on every protein entity) Boltz generates an MSA automatically. To override per protein entity:
msa: {type: empty}— single-sequence mode (no MSA).msa: {type: custom, format: a3m|csv, source: {url: "..."}}— user-provided MSA. Base64 uploads use media typetext/x-a3m(A3M) ortext/csv(CSV).
Custom and automatic MSAs cannot be mixed: if any protein entity uses a custom MSA, every other protein entity must use custom or empty.
Structure templates in a constraint / binding setup
If you're embedding a CIF/PDB file as a source of structure coordinates (for example in a custom constraint body), use @data:// in the CLI, not @file://:
structure:
type: base64
media_type: chemical/x-cif
data: "@data:///abs/path/template.cif"Alternatively, use {type: url, url: "https://..."} to point at a presigned CIF URL.
Outputs (after download-results)
Under <output-root>/<run-name>/:
.boltz-run.json— run metadata, cursor, idempotency key, timingoutputs/archive.tar.gz— containsprediction/metrics.json,prediction/sample_*_predicted_structure.cif,prediction/sample_*_pae.npz
Metrics in metrics.json are split across best_sample.metrics, each all_sample_results[].metrics, and a separate top-level binding_metrics object.
Per-sample metrics (nine lowercase keys, present on both best_sample.metrics and each entry in all_sample_results[].metrics):
structure_confidence— aggregate confidenceptm— predicted TM-scoreiptm— interface predicted TM-scoreligand_iptm— interface iptm restricted to ligand chains (only when ligands are present)protein_iptm— interface iptm restricted to protein chains (only for multi-protein complexes)complex_plddt— pLDDT averaged across the complexcomplex_iplddt— pLDDT restricted to interface residuescomplex_pde— predicted distance error across the complexcomplex_ipde— predicted distance error restricted to interface residues
Binding metrics (top-level binding_metrics, only present when binding was requested):
ligand_protein_binding→{binding_confidence, optimization_score, type: "ligand_protein_binding_metrics"}protein_protein_binding→{binding_confidence, type: "protein_protein_binding_metrics"}— nooptimization_score
Uppercase aliases like pLDDT, pTM, ipTM, PDE, ipDE are not what the server emits — use the lowercase keys above.
400 validation quirk
predictions:structure-and-binding is the one endpoint that may return {"code": "VALIDATION_ERROR", "message": "Request validation failed"} with no details field. If you hit this, inspect entities, binding, and constraints by hand — the other four endpoints surface a field-level path.
Escape hatch
For any field not listed here:
boltz-api predictions:structure-and-binding start --help— flag names only; schema is not in the CLI help.
Requirements and compatibility
Written for: OpenAI Codex
Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta
“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.
Needs: Needs Python
Security
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Attribution and licence
- Created by
- Boltz
- Publisher
- OpenAI (Official)
- Distributed by
- OpenAI
- Source
- openai/plugins · plugins/boltz-api-cli/skills/boltz-structure-and-binding · commit d420d13
- Licence
- MIT License (redistribution allowed)
- LICENSE (added by AutoGineer from plugins/boltz-api-cli/LICENSE)
- ATTRIBUTION.md (written by AutoGineer)
- Synced
AutoGineer is not affiliated with or endorsed by Boltz or OpenAI.
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