Boltz-2 Protein-Ligand Binding
SkillDev toolsPredict binding affinity between target protein sequence and small molecule SMILES using Boltz-2.
Available today. Use it from your connected AI after setup.
No other account needed.
Connect ahel once, and every AI you use reads what you have installed.
Then ask your AI: use the Boltz-2 Protein-Ligand Binding skill
What this skill tells your AI
The instructions your AI receives, as published by internscience/molclaw in skills/L1_tools/molclaw-boltz2-affinity/SKILL.md and read by ahel’s review.
Note:
- Local files are not directly accessible by the server. Please upload them to the server using
molclaw-file-transferbefore execution. - For PDB file inputs, it is recommended to preprocess them using
molclaw-pdbfixerbefore execution. - Please refer to skill
molclaw-scp-serverto 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.
Signals
- GitHub stars
- 33
- Forks
- 3
- Last commit
- Aug 2026
Advanced
- Catalog kind
- skill
- Gateway key
molclaw-boltz2-affinity- Source
- github.com/internscience/molclaw