How do I install the Alterlab Boltz skill?

Run npx skillmds@latest add alterlab-ieu/alterlab-boltz in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Alterlab Boltz safe to use?

SkillMD's automated safety review verdict for this skill is PASS. Independent scanners report: SkillSpector: PASS, Skill Scanner: PASS. Capability flags: docs only, makes network calls. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Alterlab Boltz?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Alterlab Boltz free to use?

Yes. Installing skills from SkillMD is free. This skill is licensed under MIT.

Who published Alterlab Boltz?

AlterLab-IEU (@alterlab-ieu) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Alterlab Boltz by alterlab-ieu — Agent Skill

Name: Alterlab Boltz
Rating: 4.2 (5 reviews)
Author: AlterLab-IEU

Alterlab Boltz

Co-fold biomolecular complexes with Boltz-2, an open AlphaFold3-style model — predict protein + ligand (SMILES/CCD), protein + nucleic-acid, and multi-chain structures in one pass, with binding-affinity prediction. Use when folding a protein together with a small-molecule ligand, predicting a holo (ligand-bound) complex or its binding affinity, or co-folding protein–DNA/RNA assemblies. For protein-only or protein–protein folding without ligands prefer alterlab-alphafold; for antibody–antigen complexes prefer alterlab-chai; to dock a ligand into a FIXED receptor structure prefer alterlab-diffdock; to look up an existing structure prefer alterlab-pdb. Part of the AlterLab Academic Skills suite.

AlterLab-IEU Updated Aug 19, 2026 60 repo stars

File contents

Boltz-2 (open AlphaFold3-style co-folding)

Overview

Boltz-2 (Passaro, Wohlwend et al. 2025; jwohlwend/boltz) is an open, commercially usable biomolecular structure model in the AlphaFold3 family: it co-folds proteins together with small-molecule ligands, nucleic acids, and multiple chains in a single prediction, and can predict binding affinity — capabilities AlphaFold2/ColabFold does not have. Use it when the biology is a complex with a ligand or other molecule types, not a bare protein.

When to Use This Skill

Use this skill when the user wants to:

Co-fold a protein with a small-molecule ligand (SMILES or CCD code) into a holo complex.
Predict a binding affinity alongside a co-folded pose.
Fold protein–nucleic-acid or multi-entity assemblies in one pass.
Get an open AlphaFold3-style prediction without proprietary access.

Does NOT Trigger

Scenario	Use instead
Protein-only or protein–protein folding, no ligand	`alterlab-alphafold`
Antibody–antigen / general one-FASTA multi-entity complex	`alterlab-chai`
Dock a ligand into an existing, fixed receptor structure	`alterlab-diffdock`
Retrieve an experimentally determined structure	`alterlab-pdb`
Design a binding-pocket sequence around a ligand	`alterlab-ligandmpnn`

Core Capabilities

1. Protein + ligand co-folding

Describe the complex in a YAML spec (chains + ligand by SMILES or CCD), then predict:

# complex.yaml (schema — TODO(verify) against installed boltz)
version: 1
sequences:
  - protein: { id: A, sequence: "MKT...GGG" }
  - ligand:  { id: L, smiles: "CC(=O)Oc1ccccc1C(=O)O" }

boltz predict complex.yaml --out_dir out/ --use_msa_server

Outputs the co-folded structure (protein + placed ligand) plus per-model confidence. --use_msa_server fetches the protein MSA from the hosted service (disclose for sensitive sequences); a local MSA can be supplied instead.

2. Binding-affinity prediction

Boltz-2 can predict a binding-affinity value for a protein–ligand pair alongside the pose — useful for triage/ranking in virtual screening. Treat predicted affinities as a ranking signal, not a measured constant; confirm hits experimentally or against measured data (alterlab-bindingdb). TODO(verify) the exact affinity-output flag/field per version.

3. Confidence and validation

Read the per-model confidence (and, for the interface, the model's interface score) to decide which pose to trust. For a ligand pose specifically, sanity-check that the ligand sits in a plausible pocket and that protein confidence around the site is high. Cross-check a docked alternative with alterlab-diffdock when the receptor structure is already known and fixed.

4. Running on a GPU

Boltz-2 needs a CUDA GPU and downloads weights once. Batch predictions (e.g. a ligand series against one target) via alterlab-remote-compute: submit → poll → harvest out/.

Resources

references/boltz_usage.md — install/pinning, YAML/FASTA input schema, MSA options, affinity output, and multi-entity examples. Loaded on demand.

Part of the AlterLab Academic Skills suite.

---name: alterlab-boltzdescription: Co-fold biomolecular complexes with Boltz-2, an open AlphaFold3-style model — predict protein + ligand (SMILES/CCD), protein + nucleic-acid, and multi-chain structures in one pass, with binding-affinity prediction. Use when folding a protein together with a small-molecule ligand, predicting a holo (ligand-bound) complex or its binding affinity, or co-folding protein–DNA/RNA assemblies. For protein-only or protein–protein folding without ligands prefer alterlab-alphafold; for antibody–antigen complexes prefer alterlab-chai; to dock a ligand into a FIXED receptor structure prefer alterlab-diffdock; to look up an existing structure prefer alterlab-pdb. Part of the AlterLab Academic Skills suite.license: MIT---# Boltz-2 (open AlphaFold3-style co-folding)## Overview**Boltz-2** (Passaro, Wohlwend et al. 2025; `jwohlwend/boltz`) is an open, commercially usablebiomolecular structure model in the AlphaFold3 family: it **co-folds** proteins together withsmall-molecule **ligands**, nucleic acids, and multiple chains in a single prediction, and canpredict **binding affinity** — capabilities AlphaFold2/ColabFold does not have. Use it when thebiology is a *complex with a ligand or other molecule types*, not a bare protein.## When to Use This SkillUse this skill when the user wants to:- Co-fold a protein **with a small-molecule ligand** (SMILES or CCD code) into a holo complex.- Predict a **binding affinity** alongside a co-folded pose.- Fold **protein–nucleic-acid** or multi-entity assemblies in one pass.- Get an open AlphaFold3-style prediction without proprietary access.### Does NOT Trigger| Scenario | Use instead ||----------|-------------|| Protein-only or protein–protein folding, no ligand | `alterlab-alphafold` || Antibody–antigen / general one-FASTA multi-entity complex | `alterlab-chai` || Dock a ligand into an **existing, fixed** receptor structure | `alterlab-diffdock` || Retrieve an experimentally determined structure | `alterlab-pdb` || Design a binding-pocket sequence around a ligand | `alterlab-ligandmpnn` |## Core Capabilities### 1. Protein + ligand co-foldingDescribe the complex in a YAML spec (chains + ligand by SMILES or CCD), then predict:```yaml# complex.yaml (schema — TODO(verify) against installed boltz)version: 1sequences: - protein: { id: A, sequence: "MKT...GGG" } - ligand: { id: L, smiles: "CC(=O)Oc1ccccc1C(=O)O" }``````bashboltz predict complex.yaml --out_dir out/ --use_msa_server```Outputs the co-folded structure (protein + placed ligand) plus per-model confidence.`--use_msa_server` fetches the protein MSA from the hosted service (disclose for sensitivesequences); a local MSA can be supplied instead.### 2. Binding-affinity predictionBoltz-2 can predict a binding-affinity value for a protein–ligand pair alongside the pose —useful for triage/ranking in virtual screening. Treat predicted affinities as a *ranking*signal, not a measured constant; confirm hits experimentally or against measured data(`alterlab-bindingdb`). `TODO(verify)` the exact affinity-output flag/field per version.### 3. Confidence and validationRead the per-model confidence (and, for the interface, the model's interface score) to decidewhich pose to trust. For a ligand pose specifically, sanity-check that the ligand sits in aplausible pocket and that protein confidence around the site is high. Cross-check a dockedalternative with `alterlab-diffdock` when the receptor structure is already known and fixed.### 4. Running on a GPUBoltz-2 needs a CUDA GPU and downloads weights once. Batch predictions (e.g. a ligand seriesagainst one target) via `alterlab-remote-compute`: submit → poll → harvest `out/`.## Resources- `references/boltz_usage.md` — install/pinning, YAML/FASTA input schema, MSA options, affinity output, and multi-entity examples. Loaded on demand.Part of the AlterLab Academic Skills suite.

Frequently asked questions