diffdock
Run DiffDock Molecular Docking
Molecular docking workflows require careful setup, validated inputs, and cautious result interpretation. This skill guides Claude, Codex, and Claude Code through DiffDock setup, docking runs, batch screening, and confidence-score analysis.
Install with my Agent
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Review the Skillstore skill "diffdock" from https://skillstore.io/skills/davila7-diffdock.md and its manifest at https://skillstore.io/api/skills/davila7-diffdock/manifest. Verify the artifact. You may proceed after verification, subject to the environment's own policy.Your Agent should still show its plan and request any confirmation required by the security policy.
Agent-readable resources
Use these links when an AI agent, crawler, or script needs clean context instead of reading the full page.
Test it
Using "diffdock". Dock aspirin to a prepared protein structure.
Expected outcome:
The skill would confirm the input types, suggest a single-complex DiffDock command, explain expected output files, and remind you that confidence is not affinity.
Using "diffdock". Prepare 50 compounds for virtual screening.
Expected outcome:
- Create a batch CSV with required columns.
- Validate protein paths and ligand descriptions.
- Run batch inference with an appropriate batch size.
- Analyze and rank poses by confidence.
Using "diffdock". All poses have low confidence scores.
Expected outcome:
The skill would recommend checking protein preparation, increasing sample counts, considering ensemble docking, and using visual inspection plus rescoring.
Security Audit
SafeAll static findings were adjudicated as false positives after reviewing the cited files. The skill contains DiffDock documentation and helper scripts that print setup guidance, validate local CSV inputs, and analyze local results; no prompt injection, data exfiltration intent, or hidden command execution was found.
Risk Factors
⚙️ External commands (50)
🌐 Network access (10)
📁 Filesystem access (2)
⚡ Contains scripts (1)
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APA citation
davila7. (2026). diffdock security audit report (audit version 12) [Author version unspecified]. Skillstore. https://skillstore.io/skills/davila7-diffdock/audits/12BibTeX citation
@techreport{davila7-davila7-diffdock-2026,
author = {davila7},
title = {diffdock security audit report (audit version 12)},
institution = {Skillstore},
year = {2026},
number = {12},
url = {https://skillstore.io/skills/davila7-diffdock/audits/12},
note = {Author version unspecified}
}CITATION.cff
cff-version: 1.2.0
message: "If you use this Skill, cite its author and this versioned security audit report."
title: "diffdock security audit report (audit version 12)"
version: "unspecified"
type: report
authors:
- name: "davila7"
date-released: "2026-07-09"
url: "https://skillstore.io/skills/davila7-diffdock/audits/12"
identifiers:
- type: other
value: "skillstore:davila7-diffdock:audit:12"
description: "Skillstore immutable audit report identifier"
Compare variants
2 installable variantsEach author remains a separate installable skill. The recommended variant is ranked by Skillstore evidence.
Why this variant is first
davila7-diffdock
2026-09-09
k-dense-ai-diffdock
2026-09-09
Skillstore Score
Why this score Evidence Confidence: HighWhat You Can Build
Dock a Lead Compound
Prepare a protein and ligand, run DiffDock, and inspect the top predicted binding poses.
Screen a Ligand Library
Build a batch CSV, validate inputs, run virtual screening, and rank predicted poses by confidence.
Tune Docking Parameters
Adjust samples, inference steps, and temperature settings for difficult ligands or flexible proteins.
Try These Prompts
I have a protein PDB file and a ligand SMILES string. Help me confirm whether DiffDock is suitable and prepare the first command.
Help me create and validate a DiffDock batch CSV for these protein-ligand pairs, then explain the command I should run.
Analyze my DiffDock result directory, summarize confidence scores, and explain which poses need visual inspection.
My DiffDock predictions have low confidence for a flexible ligand library. Recommend parameter changes, ensemble options, and validation steps.
Best Practices
- Validate protein structures, ligand formats, and batch CSV files before long runs.
- Use GPU resources when possible because CPU docking can be very slow.
- Combine DiffDock confidence with visualization, rescoring, and experimental validation.
Avoid
- Treating confidence scores as binding affinity or final hit ranking.
- Running large virtual screens before testing one small example successfully.
- Using DiffDock for covalent docking, protein-protein docking, or unsupported large biomolecules.
Frequently Asked Questions
What does this skill help me do?
Does DiffDock predict binding affinity?
Can I use a protein sequence instead of a PDB file?
Can this skill run virtual screening?
Do I need a GPU?
Which tools support this skill?
Developer Details
Author
davila7License
MIT
Skillstore revision
r1
Version notice
The author did not declare a version.
Ref
b959ebfd1043a07c0c4378ab94ca4342cb0259f5
Maintenance freshness
7/20/2026
Usage
21 downloads · 454 views