ligandmpnn
Inverse-fold a backbone with ligand, nucleic-acid, and metal context using LigandMPNN (Dauparas et al. 2023, github.com/dauparas/LigandMPNN). Reach for this skill to redesign the residues lining a binding pocket around a bound small molecule or cofactor, to design metal-coordinating sites where the geometry must be respected, or to get threaded designed-sequence PDBs out of any MPNN run.
Profile →npx skills add xuzhougeng/wisp-science --skill ligandmpnn --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 LigandMPNN extends the ProteinMPNN graph with non-protein atoms — small molecules, nucleic acids, and metals are visible to the network — so it is the right inverse-folding tool whenever the design surface includes a bound ligand or cofactor that vanilla `proteinmpnn` would ignore. The same `run.py` is also the most convenient runner for the other MPNN families because, unlike the original ProteinMPNN script, it threads designs back onto the input structure and writes PDBs alongside the FASTA. Code and weights are MIT (github.com/dauparas/LigandMPNN). The model is small enough to run on CPU — for a handful of designs on one structure that is seconds and usually faster than dispatching, so the normal path is local with `pip install torch numpy biopython ProDy ml_collections dm-tree`; a GPU helps for batched campaigns. ## Running it ```bash pip install torch numpy biopython ProDy ml_collections dm-tree git clone --depth 1 https://github.com/dauparas/LigandMPNN.git ligandmpnn cd ligandmpnn sed -i 's/np\.int\b/np.int64/g' openfold/np/residue_constants.py # repo pins numpy 1.23; alias removed in >=1.24 bash get_model_params.sh ./model_params python run.py \ --model_type lig
- Running it
- Model types — which one to pick
- ProDy compiles from source on py3.11 — pip install fails without a C compiler
- Turning ligand context off changes the answer, not the model
- Wisp execution
- Errors worth recognizing
pip install torch numpy biopython ProDy ml_collections dm-tree git clone --depth 1 https://github.com/dauparas/LigandMPNN.git ligandmpnn cd ligandmpnn sed -i 's/np\.int\b/np.int64/g' openfold/np/residue_constants.py # repo pins numpy 1.23; alias removed in >=1.24 bash get_model_params.sh ./model_params python run.py \
What does the ligandmpnn skill do?
Inverse-fold a backbone with ligand, nucleic-acid, and metal context using LigandMPNN (Dauparas et al. 2023, github.com/dauparas/LigandMPNN). Reach for this skill to redesign the residues lining a binding pocket around a bound small molecule or cofactor, to design metal-coordinating sites where the geometry must be respected, or to get threaded designed-sequence PDBs out of any MPNN run.
How do I install it?
Run `npx skills add xuzhougeng/wisp-science --skill ligandmpnn --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 xuzhougeng/wisp-science, a repository with 895 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.