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skill CX Codex GP ChatGPT official · OpenAI

boltz-small-molecule-screen — Codex skill: screen molecule libraries

Docks a supplied SMILES library against a target, ranks the hits, and returns ADME numbers for every molecule.

Install into Codex · Skills CLI (Vercel) All methods and platforms ↓
npx skills add openai/plugins --skill boltz-small-molecule-screen -a codex
Open on GitHub

What boltz-small-molecule-screen does

This skill works on molecules you already have. Feed it a library — raw SMILES, a CSV with the SMILES column detected automatically, .smi, or .txt — plus one or more protein sequences. The skill normalizes both into a payload with molecules and target keys. For the target you can pass pocket_residues (0-based) and reference_ligands, the SMILES of known binders that help pin down the pocket.

From there it writes the YAML or JSON payload, runs estimate-cost, prints the USD figure and waits for you to confirm. Only then does start submit the job. The download-results command runs in the background: it polls, paginates list-results, pulls down each per-hit structure and exits at a terminal state. Ranking reads binding_confidence when you are hunting hits, or optimization_score when you are optimizing a lead — two separate intents, not a fallback chain. Every result also carries an adme block with solubility, permeability and lipophilicity.

One thing to settle first: boltz-api has to be on PATH. Without it nothing runs, and the skill hands you off to boltz-cli-setup. The same setup skill covers expired authentication through boltz-api auth login --device-code. Molecule filtering stays server-side by default, so add your own molecule_filters only when you actually want them — a stray filter can cut compounds you needed.

What it helps with

  • rank a compound library against a given protein target
  • pull out hits by binding_confidence
  • sort candidates by optimization_score for lead optimization
  • get solubility, permeability and logD per molecule
  • check the USD cost before submitting a screen

Example prompts for the agent

  • Screen library.csv against this target and show the top 10 hits by binding_confidence
  • Sort the results by optimization_score and include the ADME block for each candidate
  • Estimate the cost of this screen before I run it

How to install boltz-small-molecule-screen

1Skills CLI (Vercel)

npx skills add openai/plugins --skill boltz-small-molecule-screen -a codex

2Manually: put the files in place

.agents/skills/boltz-small-molecule-screen/SKILL.md

For all projects use ~/.agents/skills/

Step by step, with commands for each platform: Codex skills: how to install, where they live, how to call them

Questions about boltz-small-molecule-screen

How do I install boltz-small-molecule-screen in Claude?

Run npx skills add openai/plugins --skill boltz-small-molecule-screen -a codex. The skill lands in .agents/skills/boltz-small-molecule-screen/SKILL.md.

What does boltz-small-molecule-screen do?

It docks and ranks an existing molecule library against a protein target, and returns Tier-1 ADME values — solubility, permeability, lipophilicity — for each molecule.

How is it different from boltz-small-molecule-adme?

This one needs a target and a candidate library. For ADME on bare SMILES with no target, one-off docking, or de novo design, use boltz-small-molecule-adme.

Source text

SKILL.md · MIT
---
name: boltz-small-molecule-screen
description: Screen existing small-molecule libraries with Boltz. Use when docking, scoring, or ranking a supplied SMILES or compound library against a target; also returns free Tier-1 ADME/ADMET (solubility, permeability, lipophilicity/logD) per molecule. Not for de novo molecule design, one-off docking, or ADME on bare SMILES with no target (use boltz-small-molecule-adme).
---

## 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 when the user already has candidate molecules.

1. Normalize the library from raw SMILES, a CSV (auto-detect the SMILES column), `.smi`, or `.txt` into the `molecules` list. Each entry is `{smiles, id?}`; the optional `id` is echoed back as `external_id` on each result.
2. Normalize the target: one or more protein sequences into `target.entities`, plus optional `pocket_residues` (0-based) and/or `reference_ligands` (SMILES of known binders to help locate the pocket).
3. Keep default server-side filtering unless the user asks for custom filters — only add `molecule_filters` on explicit request.
4. Author the payload YAML or JSON, run `estimate-cost`, show the USD cost, wait for explicit confirmation.
5. `start` to submit (synchronous). Capture the ID.
6. Launch `download-results` with the agent runtime's background/non-blocking command facility — it polls, paginates `list-results`, downloads every per-hit structure, and exits when terminal. In Claude Code, use Bash with `run_in_background: true`. In Codex, run `download-results` as a foreground shell command with `yield_time_ms: 1000`; if Codex returns a `session_id`, keep it for optional same-thread polling, but treat `download-status` plus the run directory as the durable source of truth. In Codex app/desktop runtimes that expose same-thread heartbeat automations, create a heartbeat that checks `download-status` periodically 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 the `download-status` command.
7. When done, rank from `<output-root>/<run-name>/results/index.jsonl`. Sort by `binding_confidence` for hit discovery or `optimization_score` for lead optimization; these are parallel intents, not a fallback hierarchy. Report the top 5-10 hits with `smiles`, the chosen ranking metric, key confidence metrics, and structure path. Each result also carries a free `adme` block (`solubility`, `permeability`, `lipophilicity`) — include it for developability triage when the user cares about ADME, or when a top hit looks risky. Read [references/results.md](references/results.md) for output layout, metrics, ADME, and filtered-input accounting.

## Command Pattern

```bash
# Replace placeholders with concrete absolute paths before running.
# Use a short descriptive run name, for example: sm-screen-<target>-<library>-v1

boltz-api small-molecule:library-screen estimate-cost \
  --input @yaml:///absolute/path/payload.yaml

boltz-api small-molecule:library-screen start \
       --idempotency-key "<run-name>" \
       --input @yaml:///absolute/path/payload.yaml \
       --raw-output --transform id

# 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 30
# -> /absolute/path/boltz-experiments/<run-name>/results/<pres_*>/...
```

Payload keys are `molecules`, `target`, `molecule_filters` — the API body field names, not the direct CLI flag names `--molecule` / `--target` / `--molecule-filters`.

## Always Do This

- Keep payload field names exactly as the API body names shown in `references/api.md`.
- Use absolute paths for the output root, payload files, and embedded target files. Do not `cd` into the run directory for follow-up commands; pass the same `--root-dir` and use absolute paths so later relative paths do not drift.
- Prefer one merged top-level payload via `--input @yaml:///absolute/path/payload.yaml` or `@json:///absolute/path/payload.json` for `estimate-cost` and `start`. Keep `--idempotency-key` and `--workspace-id` top-level; if they also appear inside `--input`, the top-level flags win.
- Direct object flags still work as overrides, such as `--target @yaml:///absolute/path/target.yaml`, `--molecule-filters @json:///absolute/path/filters.json`, or repeated `--molecule @json:///absolute/path/mol-1.json` entries. Piped YAML / JSON on stdin also works, but it must use API body field names. Never use `@file://` or `@./`.
- Treat pocket residue indices as 0-based.
- Do not invent medicinal-chemistry filters. Only add `molecule_filters` if the user asks; mention the catalog as an option.
- Use the same slug as both `--idempotency-key` at submit and `--name` on `download-results` so re-runs resume via `.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 over `sh -c`, inline environment assignments, aliases, wrapper scripts, loops, or pipelines around the `boltz-api` invocation 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 next `download-results` command.
- Prefer the agent runtime's background/non-blocking command mode for `download-results`. In Codex specifically, keep `download-results` in the foreground and set the shell tool yield to 1000 ms; Codex will return a `session_id` if the command is still running. Do not append `&` or use `nohup` in Codex because the tool runner may clean up shell-backgrounded descendants before `.boltz-run.json` is fully written.
- After the background/session starts, do not manually wait on it or run ad hoc polling loops. `download-results` emits JSONL progress on stderr by default; add `--progress-format text --verbose` only 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 run `boltz-api --format json download-status --name "<run-name>" --root-dir "/absolute/path/boltz-experiments"` and stop once terminal. Choose cadence by molecule count: under 100 -> every 1-2 minutes; 100-1,000 -> every 5 minutes; over 1,000 -> every 15 minutes. Post only material status changes or terminal completion/failure. Poll the saved `session_id` with an empty `write_stdin` only for interactive, user-requested progress checks. Never run a manual poll loop in the current turn.
- 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-results` with the same `--name "<run-name>"` and the same `--root-dir`.
- Cost is a flat $0.025 per molecule (size-independent). `estimate-cost` returns the authoritative total — always use it.
- Poll interval: `--poll-interval-seconds 30` is a reasonable downloader default. Wall-clock time scales roughly with the number of molecules: under 100 often finishes in a few minutes, 100-1,000 may take several minutes to tens of minutes, and larger screens can take longer or hours depending on inputs and system load. Don't quote a fixed duration, and never tell the user a 10-candidate screen will take 30 minutes or hours.

## Escape Hatch

- Payload reference: <https://api.boltz.bio/docs/api/python/resources/small_molecule/subresources/library_screen/methods/start>
- CLI flag names: `boltz-api small-molecule:library-screen start --help`

Read [references/api.md](references/api.md) for the `molecules`, `target`, and `molecule_filters` shapes, including the built-in SMARTS filters and RDKit descriptor ranges. Read [references/results.md](references/results.md) after download when ranking hits or explaining missing/filtered inputs.

## Outputs

Rank from `results/index.jsonl` after `download-results`; use [references/results.md](references/results.md) for the local file layout, metric meanings, and filtered-input accounting.

© Boltz · MIT · openai/plugins

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