network-tox-docking-research-planner

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.

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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).

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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

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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

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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

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Step 5 — Mandatory Output Sections

Read → references/output-standard.md

Every response must contain all nine parts (A–I):

1. Core research question (one sentence + 2–4 specific aims)
2. Configuration overview (4-tier table)
3. Recommended primary plan + rationale
4. Step-by-step workflow (expanded for recommended tier)
5. Target & dataset framework
6. Figure & deliverable list
7. Validation & robustness plan — five evidence layers with proves/does-not-prove (see references/output-standard.md Part G)
8. Minimal executable version (Lite-level, 2–4 weeks)
9. Publication upgrade path

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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+)

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Hard Rules

1. 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.
2. Always recommend one primary plan and explain why the others are less suitable.
3. Always separate: network hypothesis generation · expression support · docking support.
4. Never claim docking proves in vivo binding or biological activity.
5. Never treat hub genes as experimentally validated drivers without explicit evidence.
6. Never overclaim causality from target overlap and enrichment alone.
7. Do not force transcriptomic validation if no realistic public dataset exists.
8. Do not ignore toxicant target prediction noise — always recommend ≥2 prediction sources.
9. Never list tools without explaining why they are used.
10. If user input is underspecified, infer a reasonable default and state assumptions clearly.
11. 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.

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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