molclaw-boltz2-affinity

molclaw-boltz2-affinity is a skill for Claude Code, Codex from InternScience/MolClaw. It costs 28 tokens per session (657 once invoked), scanned A, original, MIT.

A tool that predicts how strongly a small molecule may bind to a target protein from its sequence and a SMILES string. A SMILES string is a text representation of a chemical structure, and Boltz-2 is the prediction model used.

In plain words
What is it for?
Use it to predict binding affinity for a protein and small-molecule ligand, after retrieving or supplying the protein sequence.
Why use it?
It estimates protein–molecule binding without requiring the molecule to be represented as a peptide or another protein.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add skills/internscience/molclaw/molclaw-boltz2-affinity
Any agent
npx skills add InternScience/MolClaw --skill molclaw-boltz2-affinity
Clone the repo
git clone --depth 1 https://github.com/InternScience/MolClaw

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for molclaw-boltz2-affinity

README.md
[![agentmods](https://agentmods.dev/badge/skills/internscience/molclaw/molclaw-boltz2-affinity.svg)](https://agentmods.dev/skills/internscience/molclaw/molclaw-boltz2-affinity)
Your own site
<a href="https://agentmods.dev/skills/internscience/molclaw/molclaw-boltz2-affinity"><img src="https://agentmods.dev/badge/skills/internscience/molclaw/molclaw-boltz2-affinity.svg" alt="Measured on agentmods" height="20"></a>
Per session 28 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 657 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00028 $0.00657
Opus 5 $0.00014 $0.00329
Sonnet 5 $0.00006 $0.00131
Haiku 4.5 $0.00003 $0.00066

Measured 6d ago against content hash 8a2632bc8126, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-06, from the pricing page.

Security

Grade A, and why

molclaw-boltz2-affinity scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 6d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/L1_tools/molclaw-boltz2-affinity/SKILL.md · 52 lines

What it actually says

Boltz-2 Protein-Ligand Binding

Note:

  • Local files are not directly accessible by the server. Please upload them to the server using molclaw-file-transfer before execution.
  • For PDB file inputs, it is recommended to preprocess them using molclaw-pdbfixer before execution.
  • Please refer to skill molclaw-scp-server to complete tool invocation.

step 1. Use skill molclaw-protein-sequence-retrieve to get the target protein sequence information. If the target protein sequence has been provided, skip this step.

step 2. Finally use tool pred_binding_affinity_boltz2 to predict the binding affinity.

Tool description:

Use Boltz to predict binding affinity between protein (receptor) and small molecule (ligand).
The server selects the output directory. This tool is for small-molecule ligands, not peptide/protein partners; ligands exceeding the Boltz affinity atom limit are returned as a structured model-capability error rather than a timeout.
Args:
    protein (List[dict]): Protein chains, each element contains 'chain' and 'sequence' (e.g., [{{'chain': 'A', 'sequence': 'MGNAAAAKKGSEQASQRRSSLEQP*'}}])
    smiles (str): Input SMILES string (e.g., "N[C@@H](Cc1ccc(O)cc1)C(=O)O")
Return:
    status (str): success/error
    msg (str): message
    affinity_probability_binary (float): Represents the predicted probability (ranging from 0 to 1) that a ligand is a binder, making it ideal for distinguishing active compounds from decoys during the hit-discovery stage. A value below 0.5 indicates uncertain or weak binding.
    affinity_pred_value (float): Estimates the specific binding affinity as log10(IC50) in μM to quantify how small molecular modifications affect potency, serving as a key metric for ligand optimization phases like hit-to-lead and lead-optimization.
    complex_cif_file (str): Structure file of the protein–molecule complex

Tool usage:

response = await client.session.call_tool(
    "pred_binding_affinity_boltz2",
    arguments={
        "protein": protein_chains,
        "smiles": smiles
    }
)
result = client.parse_result(response)
affinity_probability_binary = result["affinity_probability_binary"]
affinity_pred_value = result["affinity_pred_value"]

Current capability boundary: Boltz affinity rejects ligands with more than 128 atoms. For peptide ligands such as PTHrP/TIP39 fragments, this is expected behavior; use protein-peptide structure/docking workflows such as Chai-1/HDOCK plus interaction_visualizer(mode="peptide") instead of interpreting the Boltz rejection as a server failure.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 6d ago First seen · 52 lines · 28 tokens per session scan A 8a2632bc8126

Subscribe to this mod's changes

molclaw-boltz2-affinity is a skill published in the GitHub repository InternScience/MolClaw (33 stars, last pushed 1mo ago), licensed MIT. It adds 28 tokens to every session and 657 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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