ligandmpnn
Ligand-aware protein sequence design using LigandMPNN. Use this skill when: (1) Designing sequences around small molecules, (2) Enzyme active site design, (3) Ligand binding pocket optimization, (4) Metal coordination site design, (5) Cofactor binding proteins. For standard protein design, use proteinmpnn. For solubility optimization, use solublempnn.
npx skills add majiayu000/claude-skill-registry --skill ligandmpnn-adaptyvbio-protein-design-skill-2 --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# LigandMPNN Ligand-Aware Design ## Prerequisites | Requirement | Minimum | Recommended | |-------------|---------|-------------| | Python | 3.8+ | 3.10 | | CUDA | 11.0+ | 11.7+ | | GPU VRAM | 8GB | 16GB (T4) | | RAM | 8GB | 16GB | ## How to run > **First time?** See [Installation Guide](../../docs/installation.md) to set up Modal and biomodals. ### Option 1: Modal (recommended) ```bash cd biomodals modal run modal_ligandmpnn.py \ --pdb-path protein_ligand.pdb \ --num-seq-per-target 16 \ --sampling-temp 0.1 ``` **GPU**: T4 (16GB) | **Timeout**: 600s default ### Option 2: Local installation ```bash git clone https://github.com/dauparas/LigandMPNN.git cd LigandMPNN python run.py \ --pdb_path protein_ligand.pdb \ --out_folder output/ \ --num_seq_per_target 16 ``` ## Key parameters | Parameter | Default | Range | Description | |-----------|---------|-------|-------------| | `--pdb_path` | required | path | PDB with ligand | | `--num_seq_per_target` | 1 | 1-1000 | Sequences per structure | | `--sampling_temp` | "0.1" | "0.0001-1.0" | Temperature (string!) | | `--ligand_mpnn_use_side_chain_context` | true | bool | Use ligand context | ## Ligand Specification ### In PDB File Ligand must b
- Prerequisites
- How to run
- Option 1: Modal (recommended)
- Option 2: Local installation
- Key parameters
- Ligand Specification
- In PDB File
- Supported Ligand Types
- Output format
- Sample output
- Successful run
- Decision tree
- Typical performance
- Verify
cd biomodals modal run modal_ligandmpnn.py \ git clone https://github.com/dauparas/LigandMPNN.git cd LigandMPNN python run.py \ grep -c "^>" output/seqs/*.fa # Should match backbone_count × num_seq_per_target
What does the ligandmpnn skill do?
Ligand-aware protein sequence design using LigandMPNN. Use this skill when: (1) Designing sequences around small molecules, (2) Enzyme active site design, (3) Ligand binding pocket optimization, (4) Metal coordination site design, (5) Cofactor binding proteins. For standard protein design, use proteinmpnn. For solubility optimization, use solublempnn.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill ligandmpnn-adaptyvbio-protein-design-skill-2 --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.