ligandmpnn SKILL.md skillA

ligandmpnn from pengqianhan/ai-human-research-os is an agent skill: a SKILL.md folder an AI agent loads when a task calls for it.

Install it, pinned
mdr add pengqianhan/ai-human-research-os/ligandmpnn@git:20260715.38d79be

Pins git:20260715.38d79be in mdr.lock. Pin the fingerprint instead.

Audit grade
A, 17 of 17 checks passed
Kind
SKILL.md skill
Repository
pengqianhan/ai-human-research-os
Path
research-skills-hub/claude-science-skills/skills/ligandmpnn/SKILL.md
Versions recorded
1
Latest version
git:20260715.38d79be, committed Jul 15, 2026
Size
6,217 bytes
Repository stars
3
License
MIT

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.

Collected from public GitHub. Every version is saved exactly as it was and never changes. Install it at an exact version with the mdr tool, and every file is checked before it reaches your agent. The main file is checked against the SHA-256 fingerprint recorded here, and the others against the fingerprints GitHub keeps for its source commit. The automatic checks below grade the latest version, and the same checks always give the same file the same grade.

What the file says

# 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 \
…

Read the whole file at its exact version.

How to install

The exact contents, by fingerprint
mdr add pengqianhan/ai-human-research-os/ligandmpnn@sha256:004f25fe7caab394

The line at the top of this page pins the label git:20260715.38d79be, which is what most people want. This one pins the sha256 fingerprint of the contents instead, so it survives a label being moved. Either way the exact version is written to mdr.lock, and mdr install downloads and checks those contents again, so it installs them exactly or stops with an error.

Badge

mdr badge

[![mdr](https://markdownregistry.com/badge/art_3wjvcqclc2jy5bad.svg)](https://markdownregistry.com/a/art_3wjvcqclc2jy5bad)

2 badge image fetches in 30 days. What the badge shows.

Versions

The registry has recorded 1 version of this file, committed on Jul 15, 2026.

versioncommittedcommitsizeaudit
git:20260715.38d79be latest2026-07-15 38d79be 6,217 BA view

Audit of the latest version

A  17 of 17 checks passed. Fixed automatic checks with no AI involved, so the same checks give the same answer every run.
  • pass: Frontmatter block present
  • pass: Frontmatter declares a name
  • pass: Frontmatter declares a description
  • pass: Size between 200 bytes and 200 KB (6217 bytes)
  • pass: No zero-width or bidi control characters
  • pass: No instruction hidden inside an HTML comment
  • pass: No link to an exfiltration or paste host
  • pass: No credential-shaped string
  • pass: No instruction to send local credentials anywhere
  • pass: No text hidden with inline styles
  • pass: No prompt-injection phrasing
  • pass: No curl or wget piped into a shell
  • pass: No recursive delete of root, home or parent
  • pass: No instruction to read or print local credentials
  • pass: No base64 blob over 200 characters
  • pass: No link to a raw IP address
  • pass: No script tag

Source

GitHub

pengqianhan/ai-human-research-os · 3 stars · license MIT · pushed 2026-10-19 · branch main

API

GET https://markdownregistry.com/api/v1/artifacts/art_3wjvcqclc2jy5bad
GET https://markdownregistry.com/api/v1/resolve?ref=pengqianhan/ai-human-research-os/ligandmpnn
GET https://markdownregistry.com/api/v1/blob/004f25fe7caab394424fb7e2660b9428ad7079d0b9ff1d573079ab6340938396

Your AI assistant does the legwork. You hear about the deals worth your word. Hand yours the standing instructions at modelranch.com and it joins the network that reads files like this one.

Guides: the SKILL.md format and its required fields · what to check before you install a skill · how to pin a skill to an exact version · where to find agent skills · keeping a team on the same skills · which agent file does what.

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Every file in pengqianhan/ai-human-research-os

Other files named ligandmpnn

ligandmpnn SKILL.md skill
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ligandmpnn SKILL.md skill
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ligandmpnn SKILL.md skill
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