Single-Drug Adverse-Effect Hub-First Network Pharmacology Planner
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About this skill
A Research Design Tool for Single-Drug Adverse Effects
When a study is built around one fixed drug and one toxicity phenotype, such as Escitalopram with long QT syndrome or Cisplatin with nephrotoxicity, the difficult part is not collecting targets. It is defining a defensible overlap space, using PPI topology to prioritize hubs, separating enrichment interpretation from docking plausibility, and avoiding a literature list that looks comprehensive but is not executable. This skill constrains input to drug + adverse-effect endpoint + research emphasis, and asks for a concrete study plan rather than a generic method review.
Workflow, Evidence Discipline, and Limits
It infers the study type, selects a hub-first pattern such as Canonical, Cardiotoxicity, Immune-Inflammatory, Organ-Toxicity, or Translational Validation, and always returns Lite, Standard, Advanced, and Publication+ configurations. It then recommends one primary path, checks dependency consistency, and maps steps such as fixed drug + fixed adverse-effect endpoint + overlap targets + hub prioritization + enrichment. The output includes the scientific question, configuration table, step-by-step workflow, figure plan, validation hierarchy, and a minimal executable version. Citations require stable identifiers such as DOI or PMID, and unverified papers must not be listed as formal references. The skill is not for patient-specific medication advice, dosing decisions, multi-drug exposure comparison, pure pharmacovigilance without a network backbone, or wet-lab-only toxicology without computational design.
Use Cases
- Build a hub-first network pharmacology plan around one fixed drug and one toxicity phenotype.
- Compare `Lite`, `Standard`, and `Advanced` configs to choose an executable submission path.
- Sequence target overlap, `PPI` hubs, `enrichment`, and `docking` into dependency-aware steps.
- Prepare verified references, evidence limits, and figure plans for a single-drug ADR mechanism draft.
Best For
- Computational toxicology graduate students who need an executable single-drug ADR plan and figure roadmap.
- PhD candidates preparing submissions who need to compare workload tiers and select a primary path.
- Drug-mechanism researchers who need dependency-aware network pharmacology design for a fixed drug and endpoint.
- Bioinformatics scientists who need `PPI`, enrichment, and docking claims constrained to conservative evidence levels.
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