network-tox-docking-research-planner

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.

53 stars

Best use case

network-tox-docking-research-planner is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

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.

Teams using network-tox-docking-research-planner should expect a more consistent output, faster repeated execution, less prompt rewriting.

When to use this skill

  • You want a reusable workflow that can be run more than once with consistent structure.

When not to use this skill

  • You only need a quick one-off answer and do not need a reusable workflow.
  • You cannot install or maintain the underlying files, dependencies, or repository context.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/network-tox-docking-research-planner/SKILL.md --create-dirs "https://raw.githubusercontent.com/aipoch/medical-research-skills/main/scientific-skills/Protocol Design/network-tox-docking-research-planner/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/network-tox-docking-research-planner/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How network-tox-docking-research-planner Compares

Feature / Agentnetwork-tox-docking-research-plannerStandard Approach
Platform SupportNot specifiedLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/A

Frequently Asked Questions

What does this skill do?

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.

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

Related Guides

SKILL.md Source

> **Source**: [https://github.com/aipoch/medical-research-skills](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):

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**

---

## 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

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.

---

## 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 |

Related Skills

two-sample-mr-research-planner

53
from aipoch/medical-research-skills

Generates complete two-sample Mendelian randomization (MR) research designs from a user-provided research direction. Use when users want to design, plan, or build a study using two-sample MR to test causal relationships. Triggers:"design a two-sample MR study", "build a publishable MR paper", "test whether this biomarker causally affects this disease", "generate Lite/Standard/Advanced MR plans", "screen multiple exposures with MR", "bidirectional MR design", "causal inference using GWAS summary statistics", or "I want to study X and Y using MR". Always outputs 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.

research-proposal-generator

53
from aipoch/medical-research-skills

Generates a comprehensive research proposal design based on input literature, including hypothesis, mechanism verification, and budget. Use when the user wants to design a research project from a paper.

research-grants

53
from aipoch/medical-research-skills

Write competitive research proposals for NSF, NIH, DOE, DARPA, and Taiwan's NSTC when you need agency-compliant narratives, budgets, and review-criteria alignment for a specific solicitation/FOA/BAA.

non-tumor-ml-research-planner

53
from aipoch/medical-research-skills

Generates complete non-tumor biomedical machine learning research designs from a user-provided research direction. Always use this skill when users want to plan bioinformatics + ML papers for non-cancer diseases (metabolic, cardiovascular, kidney, inflammatory, autoimmune, infectious, neurological, endocrine, wound healing, chronic multifactor), design diagnostic biomarker studies, combine GEO datasets with feature selection and ML modeling, or generate Lite/Standard/Advanced/Publication+ workload plans. Trigger for:"non-tumor ML study", "bioinformatics paper outside oncology", "key genes and diagnostic model for a disease", "pyroptosis/ferroptosis/senescence/autophagy + disease", "GEO datasets + machine learning", "RF + LASSO diagnostic model", "DEG + feature selection + validation", "immune infiltration + biomarker", "non-cancer biomarker paper". Trigger even for casual phrasings like "I want to study X using machine learning", "help me design a non-tumor bioinformatics paper", or "how do I build a diagnostic model for disease Y".

faers-multi-drug-soc-planner

53
from aipoch/medical-research-skills

Generates complete FAERS-based multi-drug single-SOC safety comparison research designs from a user-provided drug set, comparator, and adverse event domain. Always use this skill when users want to compare safety signals across multiple drugs using FAERS or OpenFDA data within one System Organ Class (SOC) or bounded AE domain. Trigger for:"FAERS study comparing drugs within one SOC", "publishable FAERS safety comparison paper", "compare neuropsychiatric adverse events across beta-blockers", "Lite/Standard/Advanced FAERS safety plans", "active-comparator restricted disproportionality", "adjusted ROR logistic regression FAERS", "within-class head-to-head drug comparison", "pharmacovigilance signal comparison", "single-SOC PT-level FAERS design", or any phrasing like "I want to compare drug X and drug Y for adverse events in FAERS" or "build a comparative pharmacovigilance 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.

dual-disease-transcriptomic-ml-planner

53
from aipoch/medical-research-skills

Generates complete dual-disease transcriptomic + machine learning research designs from a user-provided disease pair. Use when users want to identify shared DEGs, common hub genes, cross-disease biomarkers, or shared molecular mechanisms between two diseases using public GEO data. Triggers:"shared biomarker study for two diseases", "dual-disease transcriptomic ML paper", "identify common DEGs between disease A and B", "cross-disease hub gene discovery", "shared DEG + PPI + ROC design", "immune infiltration shared biomarker", or "I want to study disease X and Y together". Always outputs 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.

clinic-research-design

53
from aipoch/medical-research-skills

Generates a structured prompt framework for clinical study protocols. Supports Diagnostic, Efficacy, Etiology, and Prognosis studies. Calculates sample size and provides logic guides for LLMs.

basic-research-design

53
from aipoch/medical-research-skills

A biomedical research topic designer that generates progressive experimental subtitles and detailed research outlines based on a given subject. Use when the user wants to design a research proposal, outline experiments for a topic, or structure a biomedical study.

time-zone-planner

53
from aipoch/medical-research-skills

Plan cross-time-zone meeting windows for distributed teams, providing region-by-region local time mappings and tradeoff analysis for scheduling decisions.

market-research-report-generator

53
from aipoch/medical-research-skills

Generates professional market research reports by analyzing business intent, decision levels, and conducting multi-source data retrieval (Web, PubMed, Clinical Trials).

research-paper-downloader

53
from aipoch/medical-research-skills

Download academic papers from open-access sources when the user provides a DOI/arXiv ID or requests a keyword-based paper search, and return the saved PDF path.

research-hotspot-analysis

53
from aipoch/medical-research-skills

Analyzes research hotspots and recommends literature based on a disease or topic. Use when the user wants to identify current research trends, hot topics, or get literature recommendations for a specific medical field or disease.