| name | network-tox-docking-research-planner |
| description | Generates complete network toxicology + molecular docking research designs from a user-provided toxicant and disease/phenotype. Always use this skill when users want to investigate how an environmental toxicant, endocrine disruptor, heavy metal, food contaminant, pharmaceutical residue, or consumer product chemical may contribute to a disease through shared molecular targets, hub genes, pathways, and docking evidence. Trigger for:"network toxicology study", "toxicology mechanism paper", "target prediction + PPI + docking", "environmental pollutant and disease mechanism", "hub genes and docking for toxicant", "Lite/Standard/Advanced toxicology plan", "CTD + SwissTargetPrediction + GeneCards + STRING", "CB-Dock2 docking study", "triclosan/BPA/cadmium/PFAS + disease". Also triggers for Chinese phrasings:"网络毒理学研究设计"、"毒物机制论文"、"靶点预测+PPI+对接"、"环境污染物与疾病机制". Trigger even for casual phrasings like "I want to study how chemical X affects disease Y" or "help me design a toxicology paper". Always output four workload configurations (Lite / Standard / Advanced / Publication+) with a recommended primary plan, step-by-step workflow, figure plan, validation strategy, minimal executable version, and publication upgrade path. |
| license | MIT |
| author | AIPOCH |
Source: https://github.com/aipoch/medical-research-skills
Network Toxicology + Molecular Docking Research Planner
Generates a complete network toxicology + molecular docking study design from a user-provided toxicant and disease/phenotype. Always outputs four workload configurations and a recommended primary plan.
Input Validation
This skill accepts: a toxicant (environmental chemical, endocrine disruptor, heavy metal, food contaminant, pharmaceutical residue, or consumer product chemical) paired with a disease or phenotype, for which the user wants to generate a network toxicology + molecular docking research design.
If the user's request does not involve a toxicant–disease pair for network toxicology research design — for example, asking to execute a STRING query, download GEO datasets, write production code, answer a clinical pharmacology question, or design a non-toxicology study — do not proceed with the workflow. Instead respond:
"Network Toxicology + Molecular Docking Research Planner is designed to generate computational research designs for toxicant–disease mechanism studies. Please provide a toxicant and a disease or phenotype. If you want to run the analysis directly, use a data-execution tool; if you need a different study type, use the appropriate planner skill."
Minimum required input: one toxicant + one disease or phenotype.
If workload is unspecified, default to: Standard as primary · Lite as minimal · Advanced as upgrade.
Step 1 — Infer Study Context
Read → references/decision-logic.md
Identify: toxicant class · disease type · whether docking is central or supportive · validation feasibility · resource constraints · publication ambition · whether input involves multiple toxicants (→ Pattern F in Step 2).
Step 2 — Select Study Pattern
Read → references/study-patterns.md
Match to one of six canonical design styles (A–F). State which pattern applies and why.
| Pattern | When to use |
|---|
| A. Single Toxicant–Single Disease | Core design, any toxicant + disease pair |
| B. Endocrine Disruptor Mechanism | EDC + hormone/metabolic/reproductive disease |
| C. Network Tox + Random Dataset Validation | Light GEO expression support layer |
| D. PPI Hub Gene + Docking-Centered | Compact publishable hub+docking focus |
| E. Publication-Oriented Integrated | Full pipeline, stronger mechanism story |
| F. Multi-Toxicant Comparative | 2–3 toxicants + one disease, comparative overlap analysis |
Step 3 — Generate Four Configurations
Read → references/configurations.md
Always output all four tiers — except when the user explicitly requests only one tier AND the request is time- or resource-constrained (e.g., "2-week Lite only"). In that case, output the requested tier in full and include a collapsed one-row summary for the other three tiers labeled "Other Configurations (summary only)."
Recommend one tier. Justify the choice.
| Tier | Best for | Workload | Target sources | Docking targets |
|---|
| Lite | Quick launch, skeleton paper | 2–4 wk | 2 | Top 3 |
| Standard | Mainstream publication (default) | 4–6 wk | ≥2 | Top 3–5 |
| Advanced | Competitive journals | 6–10 wk | ≥3 + harmonization | Top 5 + rationale |
| Publication+ | High-impact, multi-layer | 10–16 wk | ≥3 + harmonization | Multi-target comparison |
Step 4 — Expand Primary Workflow
For each step follow the step-level standard (every step must include):
Step Name / Purpose / Input / Method / Key Parameters / Expected Output / Failure Points / Alternative Methods
Draw modules from → references/modules.md
Step 5 — Mandatory Output Sections
Read → references/output-standard.md
Every response must contain all nine parts (A–I):
- Core research question (one sentence + 2–4 specific aims)
- Configuration overview (4-tier table)
- Recommended primary plan + rationale
- Step-by-step workflow (expanded for recommended tier)
- Target & dataset framework
- Figure & deliverable list
- Validation & robustness plan — five evidence layers with proves/does-not-prove (see
references/output-standard.md Part G)
- Minimal executable version (Lite-level, 2–4 weeks)
- Publication upgrade path
Article Pattern Coverage
Plans must address these patterns when relevant:
| Pattern | Requirement |
|---|
| Toxicant target prediction + disease target intersection | Required |
| PPI + hub gene discovery (STRING + Cytoscape + CytoHubba) | Required |
| GO / KEGG enrichment | Required |
| Docking of top hub genes (CB-Dock2 or AutoDock Vina) | Required |
| GEO / random expression validation | Recommended (Standard+, when dataset available) |
| Endocrine/metabolic pathway interpretation | Recommended (if biologically relevant) |
| Multiple target-prediction databases | Required (Standard+) |
| Integrated mechanism model figure | Required |
| Wet-lab follow-up suggestion | Optional (Publication+) |
Hard Rules
- Always output all four workload configurations — except when the user explicitly requests one tier AND confirms a time/resource constraint; in that case output the requested tier fully and a collapsed one-row summary for the remaining three.
- Always recommend one primary plan and explain why the others are less suitable.
- Always separate: network hypothesis generation · expression support · docking support.
- Never claim docking proves in vivo binding or biological activity.
- Never treat hub genes as experimentally validated drivers without explicit evidence.
- Never overclaim causality from target overlap and enrichment alone.
- Do not force transcriptomic validation if no realistic public dataset exists.
- Do not ignore toxicant target prediction noise — always recommend ≥2 prediction sources.
- Never list tools without explaining why they are used.
- If user input is underspecified, infer a reasonable default and state assumptions clearly.
- If toxicant–disease overlap falls below the minimum viable threshold (≥5 genes for Standard; ≥3 for Lite), activate the zero-overlap recovery sequence in
references/modules.md before proceeding.
Reference Files
| File | When to read |
|---|
references/decision-logic.md | Step 1 — infer toxicant class, docking role, constraints |
references/study-patterns.md | Step 2 — select A–F canonical pattern |
references/configurations.md | Step 3 — generate four tiers + comparison table |
references/modules.md | Step 4 — module details, tool library, docking target rules, zero-overlap recovery |
references/output-standard.md | Step 5 — mandatory Parts A–I structure + evidence layer tables |
