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davidanthonyndavidanthonyn/claude-skills/diffdock/SKILL.md

diffdock

Skill0 starsChanged 3 months agoNo licenceView on GitHub

Diffusion-based molecular docking. Predict protein-ligand binding poses from PDB/SMILES, confidence scores, virtual screening, for structure-based drug design. Not for affinity prediction.

16 other projects use a version of this, 10 of them edited.

Adopted by

Most recently taken up by

Used with

Projects with it also use boltz, chai1 and 10 more.

Other files people use alongside this one.

Files found in the same public projects as a version of this one, more often than they turn up anywhere else. Each project counts once, large collections count for less, and this file's own repository is left out.
  • naitySkill

    boltz

    Skill for biomolecular structure and binding affinity prediction with Boltz-2. Use this skill when a user wants to predict protein-ligand complex structures, estimate binding affinities (IC50/ΔG), screen compound libraries, optimize lead compounds, model protein-DNA or protein-RNA interactions, specify binding pockets as constraints, or work with cyclic peptides. Boltz-2 is MIT-licensed (commercial use allowed), supports affinity prediction alongside structure prediction, and uses YAML-based input. Also trigger when the user mentions Boltz, Boltz-1, Boltz-2, binding affinity prediction, FEP alternatives, or hit discovery screening.

    Also in 5 projects that use this

  • naitySkill

    chai1

    Skill for biomolecular structure prediction with Chai-1 from Chai Discovery. Use this skill when a user wants to predict protein structures, protein-ligand complexes, protein-nucleic acid complexes, or multi-chain biomolecular assemblies. Also trigger when the user mentions Chai-1, chai-lab, or wants an AlphaFold3/Boltz-2 alternative that supports proteins, small molecules, RNA, and DNA. Chai-1 is Apache 2.0 licensed (commercial use allowed).

    Also in 4 projects that use this

  • JimLiuSkill

    esmfold2

    Biohub ESMFold2 / ESMFold2-Fast all-atom co-folding (Candido et al. 2026, github.com/Biohub/esm). Single-sequence and MSA modes; protein, DNA, RNA, ligand (CCD/SMILES), modified residues. FoldBench Ab-Ag 50-55%, PPI 70-77% DockQ-pass. Also covers the ESMC-{300M,600M,6B} protein language models from the same release: masked-LM logits, hidden states, mutation scoring, contact prediction, and the SAE interpretability head. MIT-licensed weights on HuggingFace org `biohub`. Use this skill when: (1) Predicting complex structures with single-sequence input, (2) Validating designed binders with ESMFold2-Fast, (3) Running ESMFold2 with MSA input, (4) Getting ESMC embeddings or per-residue mutation scores, (5) Choosing kernel backend and sampling-step settings for paper-faithful throughput.

    Also in 3 projects that use this

  • JimLiuSkill

    fair-esm2

    Embed proteins with Meta AI's ESM-2 (`fair-esm` package). Use this skill when: (1) Extracting per-residue or per-sequence embeddings for downstream ML, (2) Masked-LM likelihood / mutation effect scoring, (3) Contact prediction from a sequence.

    Also in 3 projects that use this

  • +3 more from naity
  • +5 more from JimLiu

The file

Show its rules and text

We split this skill into the rules it gives an agent

25 rules. 19 are common, 0 are distinctive, and 6 are very specific to this project.

  • 19

    Common

    Most of the 16 edited versions we read keep these.

  • 0

    Distinctive

    This author's own choices. Worth a look.

  • 6

    Very specific

    Tied to their project's names, files or tools. Probably skip.

No licence file, so all rights are reserved — read it at the source. We count its rules but don't quote them. Read them on GitHub.

“Common” is measured: kept by at least 6 in 10 of the 16 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

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