diffdock
Run DiffDock Molecular Docking
Protein-ligand docking requires careful setup, parameter choices, and result interpretation. This skill guides DiffDock workflows for poses, confidence scores, and screening.
Install with my Agent
Copy this request to your Agent. It includes the canonical Skill page and manifest.
Review the Skillstore skill "diffdock" from https://skillstore.io/skills/k-dense-ai-diffdock.md and its manifest at https://skillstore.io/api/skills/k-dense-ai-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 against a prepared protein target and explain the expected outputs.
Expected outcome:
The response lists required inputs, recommends a single-complex DiffDock run, and explains ranked pose files plus confidence scores.
Using "diffdock". I have 200 ligands for one target protein. Plan a screening workflow.
Expected outcome:
- Create a batch input table with one row per ligand.
- Validate protein paths, ligand descriptions, and required columns.
- Precompute protein embeddings for repeated target use.
- Rank poses by confidence, then rescore and inspect top candidates.
Using "diffdock". My top DiffDock scores are all low. What should I try next?
Expected outcome:
The response explains likely causes, suggests more samples and ensemble docking, and warns that confidence is not binding affinity.
Security Audit
Low RiskThe static findings are mainly Markdown command examples, public reference URLs, and user-directed helper script input or output. I found no covert network calls, credential access, prompt injection, or unauthorized command execution, but the skill contains one low-risk external platform promotion that should be reviewed.
Confirmed security concerns (1)
Risk Factors
⚙️ External commands (66)
🌐 Network access (10)
📁 Filesystem access (2)
⚡ Contains scripts (1)
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https://skillstore.io/skills/k-dense-ai-diffdock/audits/7?utm_source=security_passport&utm_medium=share&utm_campaign=versioned_reportMarkdown badge
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<iframe src="https://skillstore.io/embed/skills/k-dense-ai-diffdock.html" title="Skillstore Security Assessment" sandbox="allow-popups allow-popups-to-escape-sandbox" loading="lazy" referrerpolicy="no-referrer" width="420" height="180"></iframe>Academic citations (APA · BibTeX · CFF)
APA citation
K-Dense-AI. (2026). diffdock security audit report (audit version 7) [Author version unspecified]. Skillstore. https://skillstore.io/skills/k-dense-ai-diffdock/audits/7BibTeX citation
@techreport{k-dense-ai-k-dense-ai-diffdock-2026,
author = {K-Dense-AI},
title = {diffdock security audit report (audit version 7)},
institution = {Skillstore},
year = {2026},
number = {7},
url = {https://skillstore.io/skills/k-dense-ai-diffdock/audits/7},
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 7)"
version: "unspecified"
type: report
authors:
- name: "K-Dense-AI"
date-released: "2026-07-05"
url: "https://skillstore.io/skills/k-dense-ai-diffdock/audits/7"
identifiers:
- type: other
value: "skillstore:k-dense-ai-diffdock:audit:7"
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
2026-08-21
k-dense-ai-diffdock
2026-08-21
Skillstore Score
Why this score Evidence Confidence: HighWhat You Can Build
Dock One Candidate Ligand
Prepare a protein target and ligand input, run DiffDock, and inspect the top predicted binding poses.
Screen A Compound Set
Build and validate a batch input table, run multiple docking jobs, and rank outputs by confidence score.
Tune Docking Parameters
Adjust sampling, inference steps, and temperature settings for flexible ligands or difficult protein systems.
Try These Prompts
Help me dock one ligand to one protein with DiffDock. I have a PDB file and a SMILES string.
Create a DiffDock batch plan for these protein-ligand pairs and tell me how to validate the CSV before docking.
Review my DiffDock confidence scores and pose files. Explain which predictions are most reliable and what needs validation.
Design an efficient DiffDock virtual screening workflow with precomputed embeddings, sampling choices, and downstream rescoring.
Best Practices
- Validate protein structures, ligand formats, and batch CSV files before starting long docking jobs.
- Generate multiple samples and inspect top poses visually before downstream scoring.
- Use confidence scores for triage, then confirm with rescoring, controls, and experiments.
Avoid
- Treating DiffDock confidence as binding affinity or final activity evidence.
- Running large virtual screens before testing one known protein-ligand case.
- Ignoring missing residues, unrealistic protonation states, or poor ligand preparation.
Frequently Asked Questions
Does this skill predict binding affinity?
Can I use a protein sequence instead of a PDB file?
What inputs are supported for ligands?
Is a GPU required?
How should I use confidence scores?
Can this handle virtual screening?
Developer Details
Author
K-Dense-AILicense
MIT license
Skillstore revision
r1
Version notice
The author did not declare a version.
Repository
https://github.com/K-Dense-AI/claude-scientific-skills/tree/main/scientific-skills/diffdockRef
a25199bc7d6b82598536822d1738eb5d5f54025b
Maintenance freshness
7/18/2026
Usage
12 downloads · 209 views