ligandmpnn — independently scanned and version-tracked by SaferSkills.
SaferSkills independently audited ligandmpnn (Agent Skill) and scored it 100/100 (green). The audit ran 55 deterministic rules across Security, Supply Chain, Maintenance, Transparency, and Community; it found 0 high-severity and 0 lower-severity findings. The full rule-by-rule trace and per-finding evidence are below. Free, methodology-open.
Findings & checks · 0 flagged
Every scanned point with the score it earned and what moved between them.
First recorded scan — no prior version to compare against.
The primary manifest — the file an agent reads to learn what this artifact does.
| Requirement | Minimum | Recommended |
|---|---|---|
| Python | 3.8+ | 3.10 |
| CUDA | 11.0+ | 11.7+ |
| GPU VRAM | 8GB | 16GB (T4) |
| RAM | 8GB | 16GB |
First time? See Getting started to set up Modal and biomodals.
cd biomodals
# modal_ligandmpnn.py takes --input-pdb; LigandMPNN run.py args go in --params-str
modal run modal_ligandmpnn.py \
--input-pdb protein_ligand.pdb \
--params-str "--model_type ligand_mpnn --number_of_batches 16 --temperature 0.1"GPU: A10G default | Timeout: 900s default
git clone https://github.com/dauparas/LigandMPNN.git
cd LigandMPNN
python run.py \
--model_type ligand_mpnn \
--pdb_path protein_ligand.pdb \
--out_folder output/ \
--number_of_batches 16 \
--temperature 0.1| Parameter | Default | Description |
|---|---|---|
--pdb_path | required | PDB with ligand |
--model_type | protein_mpnn | ligand_mpnn, soluble_mpnn, etc. |
--temperature | 0.1 | Sampling temperature |
--number_of_batches | 1 | Batches (sequences = batch_size x batches) |
--batch_size | 1 | Sequences per batch |
--ligand_mpnn_use_side_chain_context | 0 | Use ligand side-chain context |
Ligand must be present as HETATM records:
ATOM ...protein atoms...
HETATM 1 C1 LIG A 999 x.xxx y.yyy z.zzz 1.00 0.00 Coutput/
├── seqs/
│ └── protein.fa # FASTA sequences
└── protein_pdb/
└── protein_0001.pdb # PDBs with designed sequence$ python run.py --pdb_path enzyme_substrate.pdb --out_folder output/ --num_seq_per_target 8
Loading LigandMPNN model weights...
Processing enzyme_substrate.pdb
Found ligand: LIG (12 atoms)
Generated 8 sequences in 3.1 seconds
output/seqs/enzyme_substrate.fa:
>enzyme_substrate_0001, score=1.45, global_score=1.38
MKTAYIAKQRQISFVKSHFSRQLE...
>enzyme_substrate_0002, score=1.52, global_score=1.41
MKTAYIAKQRQISFVKSQFSRQLD...What good output looks like:
Should I use LigandMPNN?
│
├─ What's in your binding site?
│ ├─ Small molecule / ligand → LigandMPNN ✓
│ ├─ Metal ion (Zn, Fe, etc.) → LigandMPNN ✓
│ ├─ Cofactor (NAD, FAD, ATP) → LigandMPNN ✓
│ ├─ DNA/RNA → LigandMPNN ✓
│ └─ Nothing / protein only → Use ProteinMPNN
│
├─ What type of design?
│ ├─ Enzyme active site → LigandMPNN ✓
│ ├─ Metal binding site → LigandMPNN ✓
│ ├─ Protein-protein binder → Use ProteinMPNN
│ └─ De novo scaffold → Use ProteinMPNN
│
└─ Priority?
├─ Solubility/expression → Consider SolubleMPNN
└─ Ligand context accuracy → LigandMPNN ✓| Campaign Size | Time (T4) | Cost (Modal) | Notes |
|---|---|---|---|
| 100 backbones × 8 seq | 15-20 min | ~$2 | Standard |
| 500 backbones × 8 seq | 1-1.5h | ~$8 | Large campaign |
Throughput: ~50-100 sequences/minute on T4 GPU.
grep -c "^>" output/seqs/*.fa # Should match backbone_count × num_seq_per_targetLigand not recognized: Check HETATM format, verify ligand residue name Poor binding residues: Increase sampling around active site Missing contacts: Verify ligand coordinates in PDB
| Error | Cause | Fix |
|---|---|---|
RuntimeError: CUDA out of memory | Long protein or large batch | Reduce batch_size |
KeyError: 'LIG' | Ligand not found in PDB | Check HETATM records |
ValueError: no ligand atoms | Empty ligand | Verify ligand has atoms in PDB |
Next: Structure prediction for validation → protein-qc for filtering.
~30 seconds. Free. No account. Every finding cites a rule and a line of evidence.