tooluniverse-network-pharmacology by mims-harvard/tooluniverse
npx skills add https://github.com/mims-harvard/tooluniverse --skill tooluniverse-network-pharmacology构建和分析化合物-靶点-疾病(C-T-D)网络,以识别药物再利用机会、理解多靶点药理作用,并使用系统药理学方法预测药物作用机制。
重要提示:在工具调用中始终使用英文术语(药物名称、疾病名称、靶点名称),即使用户使用其他语言书写。只有在英文查询无结果时,才尝试使用原始语言术语作为备选。请使用用户的语言进行回复。
当用户出现以下情况时应用:
不适用于(请改用其他技能):
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tooluniverse-gwas-snp-interpretation| 参数 | 必需 | 描述 | 示例 |
|---|---|---|---|
| entity | 是 | 化合物名称/ID、靶点基因符号/ID 或疾病名称/ID | metformin, EGFR, Alzheimer disease |
| entity_type | 否 | 类型提示:compound、target 或 disease(如果省略则自动检测) | compound |
| analysis_mode | 否 | compound-to-disease、disease-to-compound、target-centric、bidirectional(默认) | bidirectional |
| secondary_entity | 否 | 用于聚焦分析的第二个实体(例如,针对化合物输入的疾病) | Alzheimer disease |
| 组成部分 | 最高分 | 满分标准 |
|---|---|---|
| 网络邻近性 | 35 | Z < -2, p < 0.01 |
| 临床证据 | 25 | 已获批用于相关适应症 |
| 靶点-疾病关联 | 20 | 强有力的遗传学证据(GWAS、罕见变异) |
| 安全性概况 | 10 | FDA 批准,安全性良好 |
| 机制合理性 | 10 | 具有功能证据的明确通路机制 |
| 评分 | 层级 | 建议 |
|---|---|---|
| 80-100 | 第 1 层 | 高再利用潜力 - 进行实验验证 |
| 60-79 | 第 2 层 | 良好潜力 - 需要机制验证 |
| 40-59 | 第 3 层 | 中等潜力 - 高风险/高回报 |
| 0-39 | 第 4 层 | 低潜力 - 考虑替代方法 |
| 层级 | 标准 | 示例 |
|---|---|---|
| T1 | 人体临床证据、监管证据 | FDA 批准、III 期临床试验 |
| T2 | 功能性实验证据 | IC50 < 1 uM、CRISPR 筛选 |
| T3 | 关联/计算证据 | GWAS 命中、网络邻近性 |
| T4 | 预测、注释、文本挖掘 | AlphaFold、文献共提及 |
完整评分详情:SCORING_REFERENCE.md
OpenTargets_get_drug_chembId_by_generic_name, drugbank_get_drug_basic_info_by_drug_name_or_id, PubChem_get_CID_by_compound_name, OpenTargets_get_target_id_description_by_name, OpenTargets_get_disease_id_description_by_nameOpenTargets_get_drug_mechanisms_of_action_by_chemblId, OpenTargets_get_associated_targets_by_drug_chemblId, drugbank_get_targets_by_drug_name_or_drugbank_id, DGIdb_get_drug_gene_interactions, CTD_get_chemical_gene_interactions, OpenTargets_get_associated_targets_by_disease_efoId, Pharos_get_targetChEMBL_get_target_activities, OpenTargets_target_disease_evidence, GWAS_search_associations_by_gene, search_clinical_trials, CTD_get_chemical_diseases, STRING_get_interaction_partners, STRING_get_network, intact_search_interactions, humanbase_ppi_analysisSTRING_functional_enrichment, STRING_ppi_enrichment, enrichr_gene_enrichment_analysis, ReactomeAnalysis_pathway_enrichmentOpenTargets_get_associated_drugs_by_target_ensemblID, drugbank_get_drug_name_and_description_by_target_name, drugbank_get_pathways_reactions_by_drug_or_idOpenTargets_get_target_classes_by_ensemblID, DGIdb_get_gene_druggability, OpenTargets_get_target_tractability_by_ensemblIDFAERS_calculate_disproportionality, FAERS_filter_serious_events, FAERS_count_death_related_by_drug, FDA_get_warnings_and_cautions_by_drug_name, OpenTargets_get_drug_adverse_events_by_chemblId, OpenTargets_get_target_safety_profile_by_ensemblID, gnomad_get_gene_constraintssearch_clinical_trials, clinical_trials_get_details, PubMed_search_articles, EuropePMC_search_articles, ADMETAI_predict_toxicity, PharmGKB_get_drug_details完整的分步代码示例:ANALYSIS_PROCEDURES.md 报告模板:REPORT_TEMPLATE.md
query、case_sensitive、exact_match、limit(4 个参数,全部必需)operation 参数medicinalproduct 而不是 drug_name{data: {entity: {field: ...}}} 结构{articles: [...]}identifiers 字符串,而不是数组species='homo_sapiens' 参数完整的工具参数参考和响应结构:TOOL_REFERENCE.md
| 阶段 | 主要工具 | 备选 1 | 备选 2 |
|---|---|---|---|
| 化合物 ID | OpenTargets 药物查找 | ChEMBL 搜索 | PubChem CID 查找 |
| 靶点 ID | OpenTargets 靶点查找 | ensembl_lookup_gene | MyGene_query_genes |
| 疾病 ID | OpenTargets 疾病查找 | ols_search_efo_terms | CTD_get_chemical_diseases |
| 药物靶点 | OpenTargets 药物机制 | DrugBank 靶点 | DGIdb 相互作用 |
| 疾病靶点 | OpenTargets 疾病靶点 | CTD 基因-疾病 | GWAS 关联 |
| PPI 网络 | STRING 相互作用 | OpenTargets 相互作用 | IntAct 相互作用 |
| 通路 | ReactomeAnalysis 富集 | enrichr 富集 | STRING 功能富集 |
| 临床试验 | search_clinical_trials | clinical_trials_search | PubMed 临床 |
| 安全性 | FAERS + FDA | OpenTargets 不良事件 | DrugBank 安全性 |
| 文献 | PubMed 搜索 | EuropePMC 搜索 | OpenTargets 出版物 |
| 文件 | 内容 |
|---|---|
| ANALYSIS_PROCEDURES.md | 每个阶段(阶段 0-8)的完整代码示例 |
| REPORT_TEMPLATE.md | 最终报告输出的 Markdown 模板 |
| SCORING_REFERENCE.md | 详细的评分标准和计算方法 |
| TOOL_REFERENCE.md | 工具签名、响应结构、故障排除 |
| USE_PATTERNS.md | 常见分析模式和边缘情况策略 |
| QUICK_START.md | 包含最小示例的快速入门指南 |
每周安装次数
129
代码库
GitHub 星标数
1.2K
首次出现
2026年2月19日
安全审计
安装于
codex125
gemini-cli124
opencode124
github-copilot123
cursor121
kimi-cli120
Construct and analyze compound-target-disease (C-T-D) networks to identify drug repurposing opportunities, understand polypharmacology, and predict drug mechanisms using systems pharmacology approaches.
IMPORTANT : Always use English terms in tool calls (drug names, disease names, target names), even if the user writes in another language. Only try original-language terms as a fallback if English returns no results. Respond in the user's language.
Apply when users:
NOT for (use other skills instead):
tooluniverse-drug-repurposingtooluniverse-drug-target-validationtooluniverse-adverse-event-detectiontooluniverse-disease-researchtooluniverse-gwas-snp-interpretation| Parameter | Required | Description | Example |
|---|---|---|---|
| entity | Yes | Compound name/ID, target gene symbol/ID, or disease name/ID | metformin, EGFR, Alzheimer disease |
| entity_type | No | Type hint: compound, target, or disease (auto-detected if omitted) | compound |
| Component | Max Points | Criteria for Max |
|---|---|---|
| Network Proximity | 35 | Z < -2, p < 0.01 |
| Clinical Evidence | 25 | Approved for related indication |
| Target-Disease Association | 20 | Strong genetic evidence (GWAS, rare variants) |
| Safety Profile | 10 | FDA-approved, favorable safety |
| Mechanism Plausibility | 10 | Clear pathway mechanism with functional evidence |
| Score | Tier | Recommendation |
|---|---|---|
| 80-100 | Tier 1 | High repurposing potential - proceed with experimental validation |
| 60-79 | Tier 2 | Good potential - needs mechanistic validation |
| 40-59 | Tier 3 | Moderate potential - high-risk/high-reward |
| 0-39 | Tier 4 | Low potential - consider alternative approaches |
| Tier | Criteria | Examples |
|---|---|---|
| T1 | Human clinical proof, regulatory evidence | FDA-approved, Phase III trial |
| T2 | Functional experimental evidence | IC50 < 1 uM, CRISPR screen |
| T3 | Association/computational evidence | GWAS hit, network proximity |
| T4 | Prediction, annotation, text-mining | AlphaFold, literature co-mention |
Full scoring details: SCORING_REFERENCE.md
OpenTargets_get_drug_chembId_by_generic_name, drugbank_get_drug_basic_info_by_drug_name_or_id, PubChem_get_CID_by_compound_name, OpenTargets_get_target_id_description_by_name, OpenTargets_get_disease_id_description_by_nameOpenTargets_get_drug_mechanisms_of_action_by_chemblId, OpenTargets_get_associated_targets_by_drug_chemblId, drugbank_get_targets_by_drug_name_or_drugbank_id, DGIdb_get_drug_gene_interactions, CTD_get_chemical_gene_interactions, OpenTargets_get_associated_targets_by_disease_efoId, Pharos_get_targetChEMBL_get_target_activities, OpenTargets_target_disease_evidence, GWAS_search_associations_by_gene, search_clinical_trials, CTD_get_chemical_diseases, STRING_get_interaction_partners, STRING_get_network, intact_search_interactions, STRING_functional_enrichment, STRING_ppi_enrichment, enrichr_gene_enrichment_analysis, ReactomeAnalysis_pathway_enrichmentOpenTargets_get_associated_drugs_by_target_ensemblID, drugbank_get_drug_name_and_description_by_target_name, drugbank_get_pathways_reactions_by_drug_or_idOpenTargets_get_target_classes_by_ensemblID, DGIdb_get_gene_druggability, OpenTargets_get_target_tractability_by_ensemblIDFAERS_calculate_disproportionality, FAERS_filter_serious_events, FAERS_count_death_related_by_drug, FDA_get_warnings_and_cautions_by_drug_name, OpenTargets_get_drug_adverse_events_by_chemblId, OpenTargets_get_target_safety_profile_by_ensemblID, gnomad_get_gene_constraintssearch_clinical_trials, clinical_trials_get_details, PubMed_search_articles, EuropePMC_search_articles, ADMETAI_predict_toxicity, PharmGKB_get_drug_detailsFull step-by-step code examples: ANALYSIS_PROCEDURES.md Report template: REPORT_TEMPLATE.md
query, case_sensitive, exact_match, limit (4 params, ALL required)operation parametermedicinalproduct NOT drug_name{data: {entity: {field: ...}}} structure{articles: [...]}Full tool parameter reference and response structures: TOOL_REFERENCE.md
| Phase | Primary Tool | Fallback 1 | Fallback 2 |
|---|---|---|---|
| Compound ID | OpenTargets drug lookup | ChEMBL search | PubChem CID lookup |
| Target ID | OpenTargets target lookup | ensembl_lookup_gene | MyGene_query_genes |
| Disease ID | OpenTargets disease lookup | ols_search_efo_terms | CTD_get_chemical_diseases |
| Drug targets | OpenTargets drug mechanisms | DrugBank targets | DGIdb interactions |
| Disease targets | OpenTargets disease targets | CTD gene-diseases | GWAS associations |
| PPI network | STRING interactions | OpenTargets interactions | IntAct interactions |
| Pathways | ReactomeAnalysis enrichment |
| File | Contents |
|---|---|
| ANALYSIS_PROCEDURES.md | Full code examples for each phase (Phases 0-8) |
| REPORT_TEMPLATE.md | Markdown template for final report output |
| SCORING_REFERENCE.md | Detailed scoring rubric and computation method |
| TOOL_REFERENCE.md | Tool signatures, response structures, troubleshooting |
| USE_PATTERNS.md | Common analysis patterns and edge case strategies |
| QUICK_START.md | Quick-start guide with minimal examples |
Weekly Installs
129
Repository
GitHub Stars
1.2K
First Seen
Feb 19, 2026
Security Audits
Gen Agent Trust HubPassSocketPassSnykWarn
Installed on
codex125
gemini-cli124
opencode124
github-copilot123
cursor121
kimi-cli120
Excel财务建模规范与xlsx文件处理指南:专业格式、零错误公式与数据分析
44,500 周安装
| analysis_mode | No | compound-to-disease, disease-to-compound, target-centric, bidirectional (default) | bidirectional |
| secondary_entity | No | Second entity for focused analysis (e.g., disease for compound input) | Alzheimer disease |
humanbase_ppi_analysisidentifiers string, NOT arrayspecies='homo_sapiens' parameter| enrichr enrichment |
| STRING functional enrichment |
| Clinical trials | search_clinical_trials | clinical_trials_search | PubMed clinical |
| Safety | FAERS + FDA | OpenTargets AEs | DrugBank safety |
| Literature | PubMed search | EuropePMC search | OpenTargets publications |