# Diffdock

Predicts protein-small-molecule binding poses with DiffDock and DiffDock-L from PDB or...

- Repository: https://github.com/K-Dense-AI/scientific-agent-skills (55651 stars)
- Category: Engineering
- License: MIT license
- Last updated: 2026-10-26
- Runs scripts: yes, review before use

## Install

Claude Code: `npx skills add K-Dense-AI/scientific-agent-skills --skill diffdock -g -a claude-code -y`
Codex: `npx skills add K-Dense-AI/scientific-agent-skills --skill diffdock -g -a codex -y`
Cursor: `npx skills add K-Dense-AI/scientific-agent-skills --skill diffdock -g -a cursor -y`
Gemini CLI: `npx skills add K-Dense-AI/scientific-agent-skills --skill diffdock -g -a gemini-cli -y`

## Description

Predicts protein-small-molecule binding poses with DiffDock and DiffDock-L from PDB or sequence plus SMILES/SDF/MOL2. Covers batch docking, pose triage, confidence interpretation, and validation. Use for molecular docking and virtual-screening pose generation, not binding-affinity prediction.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# DiffDock: protein-small-molecule docking

DiffDock-L generates candidate ligand poses and ranks them by model confidence.
Confidence is neither a measured probability of correctness nor binding affinity.
Use this skill for one pair, batches, or separate receptor conformations; treat
library-wide confidence sorting as pose triage, not hit identification.

## Verified scope

Targets the current [v1.1.3 release](https://github.com/gcorso/DiffDock/releases/tag/v1.1.3).
Released source and current main's identical `inference.py` were checked on
2026-09-30. Bundled helpers were tested on tiny synthetic inputs; pretrained docking,
ESMFold, CUDA, Docker, GNINA, and hosted-demo execution were **not** run in this
review. Commands requiring those components are source-verified recipes, not
successful end-to-end demonstrations.

## Set up the upstream environment

```bash
git clone --branch v1.1.3 --depth 1 https://github.com/gcorso/DiffDock.git
cd DiffDock
conda env create --file environment.yml
conda activate diffdock
```

The upstream environment pins Python 3.9.18, CUDA 11.7 Torch/PyG wheels and old
scientific dependencies. Do not substitute current `torch` or the unrelated PyPI
`esm` package for `fair-esm`. The published CUDA environment is not a macOS-native
installation recipe. Follow upstream Docker instructions if suitable:

```bash
docker pull rbgcsail/diffdock
docker run -it --gpus all --entrypoint /bin/bash rbgcsail/diffdock
micromamba activate diffdock
```

Record the image digest: its unversioned tag need not equal the checked source.
PDB-based inference has a CPU path; sequence folding calls `.cuda()` unconditionally.
ESM2 embeddings are needed even for PDB inputs. First use can download docking,
ESM2 and (for sequence inputs) ESMFold weights and build SO(2)/SO(3) tables. Budget
storage and memory for all components; do not assume a single small checkpoint.

Run this skill's checker from the **DiffDock checkout** using its absolute path:

The rest: https://github.com/K-Dense-AI/scientific-agent-skills/blob/HEAD/skills/diffdock/SKILL.md