Back to Details

tooluniverse-drug-target-validation

Compare original and translation side by side

🇺🇸

Original

English
🇨🇳

Translation

Chinese

Drug Target Validation Pipeline

药物靶点验证流程

Validate drug target hypotheses using multi-dimensional computational evidence before committing to wet-lab work. Produces a quantitative Target Validation Score (0-100) with priority tier classification and GO/NO-GO recommendation.
KEY PRINCIPLES:
  1. Report-first approach - Create report file FIRST, then populate progressively
  2. Target disambiguation FIRST - Resolve all identifiers before analysis
  3. Evidence grading - Grade all evidence as T1 (experimental) to T4 (computational)
  4. Disease-specific - Tailor analysis to disease context when provided
  5. Modality-aware - Consider small molecule vs biologics tractability
  6. Safety-first - Prominently flag safety concerns early
  7. Quantitative scoring - Every dimension scored numerically (0-100 composite)
  8. Negative results documented - "No data" is data; empty sections are failures
  9. Source references - Every statement must cite tool/database
  10. Completeness checklist - Mandatory section showing analysis coverage
  11. English-first queries - Always use English terms in tool calls. Respond in user's language
在开展湿实验工作前，使用多维度计算证据验证药物靶点假说。生成0-100分的定量靶点验证评分，同时给出优先级层级分类和GO/NO-GO建议。
核心原则:
  1. 报告优先原则 - 先创建报告文件，再逐步填充内容
  2. 靶点消歧优先 - 在分析前解析所有标识符
  3. 证据分级 - 将所有证据分为T1（实验性）至T4（计算性）四个等级
  4. 疾病特异性 - 当提供疾病背景时，针对性调整分析内容
  5. 适配治疗模态 - 考虑小分子与生物制剂的可开发性
  6. 安全优先 - 尽早突出标记安全隐患
  7. 定量评分 - 每个维度均采用0-100的数值评分（综合总分0-100）
  8. 记录阴性结果 - “无数据”本身也是数据；空白部分视为分析失败
  9. 来源引用 - 所有结论必须标注工具/数据库来源
  10. 完整性检查清单 - 必须包含显示分析覆盖范围的章节
  11. 英文优先查询 - 工具调用时始终使用英文术语，以用户语言回复

When to Use This Skill

何时使用该技能

Apply when users:
  • Ask "Is [target] a good drug target for [disease]?"
  • Need target validation or druggability assessment
  • Want to compare targets for drug discovery prioritization
  • Ask about safety risks of modulating a target
  • Need chemical starting points for target validation
  • Ask about pathway context for a target
  • Need a GO/NO-GO recommendation for a target
  • Want a comprehensive target dossier for investment decisions
NOT for (use other skills instead):
  • General target biology overview -> Use 
    tooluniverse-target-research
  • Drug compound profiling -> Use 
    tooluniverse-drug-research
  • Variant interpretation -> Use 
    tooluniverse-variant-interpretation
  • Disease research -> Use 
    tooluniverse-disease-research
当用户有以下需求时适用：
  • 询问“[靶点]是否是[疾病]的良好药物靶点？”
  • 需要靶点验证或成药性评估
  • 希望对比靶点以进行药物研发优先级排序
  • 询问调控某一靶点的安全风险
  • 需要靶点验证的化学起始点
  • 询问靶点的通路背景
  • 需要针对某一靶点的GO/NO-GO建议
  • 用于投资决策的全面靶点档案
不适用场景（请使用其他技能）:
  • 靶点生物学概述 -> 使用
    tooluniverse-target-research
  • 药物化合物分析 -> 使用
    tooluniverse-drug-research
  • 变异解读 -> 使用
    tooluniverse-variant-interpretation
  • 疾病研究 -> 使用
    tooluniverse-disease-research

Input Parameters

输入参数

ParameterRequiredDescriptionExample
targetYesGene symbol, protein name, or UniProt ID
EGFR
, 
P00533
, 
Epidermal growth factor receptor
diseaseNoDisease/indication for context
Non-small cell lung cancer
, 
Pancreatic cancer
modalityNoPreferred therapeutic modality
small molecule
, 
antibody
, 
protein therapeutic
, 
PROTAC
参数是否必填描述示例
target是基因符号、蛋白质名称或UniProt ID
EGFR
, 
P00533
, 
Epidermal growth factor receptor
disease否用于提供背景的疾病/适应症
Non-small cell lung cancer
, 
Pancreatic cancer
modality否首选治疗模态
small molecule
, 
antibody
, 
protein therapeutic
, 
PROTAC

Target Validation Scoring System

靶点验证评分系统

Score Components (Total: 0-100)

评分构成（总分：0-100）

Disease Association (0-30 points):
  • Genetic evidence: 0-10 (GWAS, rare variants, somatic mutations)
  • Literature evidence: 0-10 (publications, clinical studies)
  • Pathway evidence: 0-10 (disease pathway involvement)
Druggability (0-25 points):
  • Structural tractability: 0-10 (structure quality, binding pockets)
  • Chemical matter: 0-10 (known compounds, bioactivity data)
  • Target class: 0-5 (validated target family bonus)
Safety Profile (0-20 points):
  • Tissue expression selectivity: 0-5 (expression in critical tissues)
  • Genetic validation: 0-10 (knockout phenotypes, human genetics)
  • Known adverse events: 0-5 (safety signals from modulators)
Clinical Precedent (0-15 points):
  • Approved drugs: 15 (strong precedent, validated target)
  • Clinical trials: 10 (moderate precedent)
  • Preclinical compounds: 5 (weak precedent)
  • None: 0 (novel target)
Validation Evidence (0-10 points):
  • Functional studies: 0-5 (CRISPR, siRNA, biochemical)
  • Disease models: 0-5 (animal models, patient data)
疾病关联性（0-30分）:
  • 遗传证据: 0-10（GWAS、罕见变异、体细胞突变）
  • 文献证据: 0-10（出版物、临床研究）
  • 通路证据: 0-10（疾病通路参与度）
成药性（0-25分）:
  • 结构可开发性: 0-10（结构质量、结合口袋）
  • 化学物质: 0-10（已知化合物、生物活性数据）
  • 靶点类别: 0-5（已验证靶点家族加分）
安全性概况（0-20分）:
  • 组织表达选择性: 0-5（关键组织中的表达情况）
  • 遗传验证: 0-10（敲除表型、人类遗传学数据）
  • 已知不良事件: 0-5（调控剂的安全信号）
临床先例（0-15分）:
  • 已获批药物: 15分（强先例，已验证靶点）
  • 临床试验: 10分（中等先例）
  • 临床前化合物: 5分（弱先例）
  • 无: 0分（全新靶点）
验证证据（0-10分）:
  • 功能研究: 0-5（CRISPR、siRNA、生化实验）
  • 疾病模型: 0-5（动物模型、患者数据）

Priority Tiers

优先级层级

ScoreTierRecommendation
80-100Tier 1Highly validated - proceed with confidence
60-79Tier 2Good target - needs focused validation
40-59Tier 3Moderate risk - significant validation needed
0-39Tier 4High risk - consider alternatives
评分层级建议
80-100层级1高度验证 - 可放心推进
60-79层级2良好靶点 - 需要针对性验证
40-59层级3中等风险 - 需要大量验证工作
0-39层级4高风险 - 考虑替代靶点

Evidence Grading System

证据分级系统

TierSymbolCriteriaExamples
T1[T1]Direct mechanistic, human clinical proofFDA-approved drug, crystal structure with mechanism, patient mutation
T2[T2]Functional studies, model organismsiRNA phenotype, mouse KO, biochemical assay, CRISPR screen
T3[T3]Association, screen hits, computationalGWAS hit, DepMap essentiality, expression correlation
T4[T4]Mention, review, text-mined, predictedReview article, database annotation, AlphaFold prediction
层级符号标准示例
T1[T1]直接机制性、人类临床证据FDA获批药物、带机制解析的晶体结构、患者突变
T2[T2]功能研究、模式生物数据siRNA表型、基因敲除小鼠、生化实验、CRISPR筛选
T3[T3]关联性、筛选命中、计算数据GWAS命中结果、DepMap必需性、表达相关性
T4[T4]提及、综述、文本挖掘、预测数据综述文章、数据库注释、AlphaFold预测结果

Phase 0: Target Disambiguation & ID Resolution (ALWAYS FIRST)

阶段0：靶点消歧与标识符解析（必须首先执行）

Objective: Resolve target to ALL needed identifiers before any analysis.
目标: 在进行任何分析前，将靶点解析为所有所需标识符。

Resolution Strategy

解析策略

python
undefined
python
undefined

Step 1: Determine input type and get initial identifiers

Step 1: Determine input type and get initial identifiers

If gene symbol (e.g., "EGFR"):

If gene symbol (e.g., "EGFR"):

mygene = tu.tools.MyGene_query_genes(query="EGFR", species="human", fields="symbol,name,ensembl.gene,uniprot.Swiss-Prot,entrezgene")
mygene = tu.tools.MyGene_query_genes(query="EGFR", species="human", fields="symbol,name,ensembl.gene,uniprot.Swiss-Prot,entrezgene")

Extract: ensembl_id, uniprot_id, entrez_id, symbol, name

Extract: ensembl_id, uniprot_id, entrez_id, symbol, name

If UniProt ID (e.g., "P00533"):

If UniProt ID (e.g., "P00533"):

uniprot = tu.tools.UniProt_get_entry_by_accession(accession="P00533")
uniprot = tu.tools.UniProt_get_entry_by_accession(accession="P00533")

Extract: gene names, Ensembl xrefs, function

Extract: gene names, Ensembl xrefs, function

Step 2: Resolve Ensembl ID and get versioned ID for GTEx

Step 2: Resolve Ensembl ID and get versioned ID for GTEx

ensembl = tu.tools.ensembl_lookup_gene(gene_id=ensembl_id, species="homo_sapiens")
ensembl = tu.tools.ensembl_lookup_gene(gene_id=ensembl_id, species="homo_sapiens")

CRITICAL: species parameter is REQUIRED

CRITICAL: species parameter is REQUIRED

CRITICAL: Response is wrapped in {status, data, url, content_type} - access via ensembl['data']

CRITICAL: Response is wrapped in {status, data, url, content_type} - access via ensembl['data']

ensembl_data = ensembl.get('data', ensembl) if isinstance(ensembl, dict) else ensembl
ensembl_data = ensembl.get('data', ensembl) if isinstance(ensembl, dict) else ensembl

Extract: version for versioned_id (e.g., "ENSG00000146648.18")

Extract: version for versioned_id (e.g., "ENSG00000146648.18")

Step 3: Get Ensembl cross-references

Step 3: Get Ensembl cross-references

xrefs = tu.tools.ensembl_get_xrefs(id=ensembl_id)
xrefs = tu.tools.ensembl_get_xrefs(id=ensembl_id)

Extract: HGNC, UniProt, EntrezGene mappings

Extract: HGNC, UniProt, EntrezGene mappings

Step 4: Get OpenTargets target info

Step 4: Get OpenTargets target info

ot_target = tu.tools.OpenTargets_get_target_id_description_by_name(targetName="EGFR")
ot_target = tu.tools.OpenTargets_get_target_id_description_by_name(targetName="EGFR")

Verify ensemblId matches

Verify ensemblId matches

Step 5: Get ChEMBL target ID

Step 5: Get ChEMBL target ID

chembl_targets = tu.tools.ChEMBL_search_targets(pref_name__contains="EGFR", organism="Homo sapiens", limit=5)
chembl_targets = tu.tools.ChEMBL_search_targets(pref_name__contains="EGFR", organism="Homo sapiens", limit=5)

Extract: target_chembl_id for later use

Extract: target_chembl_id for later use

Step 6: Get UniProt function summary

Step 6: Get UniProt function summary

function_info = tu.tools.UniProt_get_function_by_accession(accession=uniprot_id)
function_info = tu.tools.UniProt_get_function_by_accession(accession=uniprot_id)

Returns list of strings (NOT dict)

Returns list of strings (NOT dict)

Step 7: Get alternative names for collision detection

Step 7: Get alternative names for collision detection

alt_names = tu.tools.UniProt_get_alternative_names_by_accession(accession=uniprot_id)
undefined
alt_names = tu.tools.UniProt_get_alternative_names_by_accession(accession=uniprot_id)
undefined

Identifier Resolution Output

标识符解析输出

markdown
undefined
markdown
undefined

1. Target Identity

1. 靶点标识

DatabaseIdentifierVerified
Gene SymbolEGFRYes
Full NameEpidermal growth factor receptorYes
EnsemblENSG00000146648Yes
Ensembl (versioned)ENSG00000146648.18Yes
UniProtP00533Yes
Entrez Gene1956Yes
ChEMBLCHEMBL203Yes
HGNCHGNC:3236Yes
Protein Function: [from UniProt_get_function_by_accession] Subcellular Location: [from UniProt_get_subcellular_location_by_accession] Target Class: [from OpenTargets_get_target_classes_by_ensemblID]
undefined
数据库标识符已验证
基因符号EGFR是
全称Epidermal growth factor receptor是
EnsemblENSG00000146648是
Ensembl（带版本）ENSG00000146648.18是
UniProtP00533是
Entrez Gene1956是
ChEMBLCHEMBL203是
HGNCHGNC:3236是
蛋白质功能: [来自UniProt_get_function_by_accession] 亚细胞定位: [来自UniProt_get_subcellular_location_by_accession] 靶点类别: [来自OpenTargets_get_target_classes_by_ensemblID]
undefined

Known Parameter Corrections

已知参数修正

ToolWRONG ParameterCORRECT Parameter
ensembl_lookup_gene
id
gene_id
(+ 
species="homo_sapiens"
REQUIRED)
Reactome_map_uniprot_to_pathways
uniprot_id
id
ensembl_get_xrefs
gene_id
id
GTEx_get_median_gene_expression
gencode_id
only
gencode_id
+ 
operation="median"
OpenTargets_*
ensemblID
(uppercase)
ensemblId
(camelCase)
OpenTargets_get_publications_*
ensemblId
entityId
OpenTargets_get_associated_drugs_by_target_ensemblID
ensemblId
only
ensemblId
+ 
size
(REQUIRED)
MyGene_query_genes
q
query
PubMed_search_articles
returns 
{articles: [...]}
returns plain list of dicts
UniProt_get_function_by_accession
returns dictreturns list of strings
HPA_get_rna_expression_by_source
ensembl_id
gene_name
+ 
source_type
+ 
source_name
(ALL required)
alphafold_get_prediction
uniprot_accession
qualifier
drugbank_get_safety_*
simple params
query
, 
case_sensitive
, 
exact_match
, 
limit
(ALL required)
工具错误参数正确参数
ensembl_lookup_gene
id
gene_id
(+ 必须添加
species="homo_sapiens"
)
Reactome_map_uniprot_to_pathways
uniprot_id
id
ensembl_get_xrefs
gene_id
id
GTEx_get_median_gene_expression
仅
gencode_id
gencode_id
+ 
operation="median"
OpenTargets_*
ensemblID
（大写）
ensemblId
（驼峰式）
OpenTargets_get_publications_*
ensemblId
entityId
OpenTargets_get_associated_drugs_by_target_ensemblID
仅
ensemblId
ensemblId
+ 
size
（必填）
MyGene_query_genes
q
query
PubMed_search_articles
返回
{articles: [...]}
返回纯字典列表
UniProt_get_function_by_accession
返回字典返回字符串列表
HPA_get_rna_expression_by_source
ensembl_id
gene_name
+ 
source_type
+ 
source_name
（全部必填）
alphafold_get_prediction
uniprot_accession
qualifier
drugbank_get_safety_*
简单参数
query
, 
case_sensitive
, 
exact_match
, 
limit
（全部必填）

Phase 1: Disease Association Evidence (0-30 points)

阶段1：疾病关联性证据（0-30分）

Objective: Quantify the strength of target-disease association from genetic, literature, and pathway evidence.
目标: 从遗传、文献和通路证据量化靶点与疾病的关联强度。

1A. OpenTargets Disease Associations (Primary)

1A. OpenTargets疾病关联性（主要来源）

python
undefined
python
undefined

Get ALL disease associations for target

获取靶点的所有疾病关联

diseases = tu.tools.OpenTargets_get_diseases_phenotypes_by_target_ensembl(ensemblId=ensembl_id)
diseases = tu.tools.OpenTargets_get_diseases_phenotypes_by_target_ensembl(ensemblId=ensembl_id)

If specific disease provided, get detailed evidence

如果提供了特定疾病，获取详细证据

if disease_name: disease_info = tu.tools.OpenTargets_get_disease_id_description_by_name(diseaseName=disease_name) efo_id = disease_info.get('id') # e.g., "EFO_0003060"
evidence = tu.tools.OpenTargets_target_disease_evidence(
    efoId=efo_id, ensemblId=ensembl_id
)

# Get evidence by data source for detailed breakdown
datasource_evidence = tu.tools.OpenTargets_get_evidence_by_datasource(
    efoId=efo_id, ensemblId=ensembl_id,
    datasourceIds=["ot_genetics_portal", "eva", "gene2phenotype", "genomics_england", "uniprot_literature"],
    size=100
)
undefined
if disease_name: disease_info = tu.tools.OpenTargets_get_disease_id_description_by_name(diseaseName=disease_name) efo_id = disease_info.get('id') # 例如 "EFO_0003060"
evidence = tu.tools.OpenTargets_target_disease_evidence(
    efoId=efo_id, ensemblId=ensembl_id
)

# 按数据源获取详细证据细分
datasource_evidence = tu.tools.OpenTargets_get_evidence_by_datasource(
    efoId=efo_id, ensemblId=ensembl_id,
    datasourceIds=["ot_genetics_portal", "eva", "gene2phenotype", "genomics_england", "uniprot_literature"],
    size=100
)
undefined

1B. GWAS Genetic Evidence

1B. GWAS遗传证据

python
undefined
python
undefined

GWAS associations for target gene

靶点基因的GWAS关联

gwas_snps = tu.tools.gwas_get_snps_for_gene(mapped_gene=gene_symbol, size=50)
gwas_snps = tu.tools.gwas_get_snps_for_gene(mapped_gene=gene_symbol, size=50)

If specific disease, search for trait-specific associations

如果提供了特定疾病，搜索该疾病相关的关联研究

if disease_name: gwas_studies = tu.tools.gwas_search_studies(query=disease_name, size=20)
undefined
if disease_name: gwas_studies = tu.tools.gwas_search_studies(query=disease_name, size=20)
undefined

1C. Constraint Scores (gnomAD)

1C. 约束评分（gnomAD）

python
undefined
python
undefined

Genetic constraint - intolerance to loss of function

遗传约束 - 功能缺失耐受性

constraints = tu.tools.gnomad_get_gene_constraints(gene_symbol=gene_symbol)
constraints = tu.tools.gnomad_get_gene_constraints(gene_symbol=gene_symbol)

Extract: pLI, LOEUF, missense_z, pRec

提取：pLI, LOEUF, missense_z, pRec

High pLI (>0.9) = highly intolerant to LoF = likely essential

高pLI(>0.9) = 对功能缺失高度不耐受 = 可能是必需基因

undefined
undefined

1D. Literature Evidence

1D. 文献证据

python
undefined
python
undefined

PubMed for target-disease association

PubMed中靶点-疾病关联的文献

articles = tu.tools.PubMed_search_articles( query=f'"{gene_symbol}" AND "{disease_name}" AND (target OR therapeutic OR inhibitor)', limit=50 )
articles = tu.tools.PubMed_search_articles( query=f'"{gene_symbol}" AND "{disease_name}" AND (target OR therapeutic OR inhibitor)', limit=50 )

PubMed_search_articles returns a plain list of dicts

PubMed_search_articles返回纯字典列表

OpenTargets publications

OpenTargets相关出版物

pubs = tu.tools.OpenTargets_get_publications_by_target_ensemblID(entityId=ensembl_id)
undefined
pubs = tu.tools.OpenTargets_get_publications_by_target_ensemblID(entityId=ensembl_id)
undefined

Scoring Logic - Disease Association

疾病关联性评分逻辑

Genetic Evidence (0-10):
  - GWAS hits for specific disease: +3 per significant locus (max 6)
  - Rare variant evidence (ClinVar pathogenic): +2
  - Somatic mutations in disease: +2
  - pLI > 0.9 (essential gene): +2

Literature Evidence (0-10):
  - >100 publications on target+disease: 10
  - 50-100 publications: 7
  - 10-50 publications: 5
  - 1-10 publications: 3
  - 0 publications: 0

Pathway Evidence (0-10):
  - OpenTargets overall score > 0.8: 10
  - Score 0.5-0.8: 7
  - Score 0.2-0.5: 4
  - Score < 0.2: 1
遗传证据（0-10分）:
  - 特定疾病的GWAS命中位点：每个显著位点+3分（最高6分）
  - 罕见变异证据（ClinVar致病性）：+2分
  - 疾病中的体细胞突变：+2分
  - pLI>0.9（必需基因）：+2分

文献证据（0-10分）:
  - 靶点+疾病相关出版物>100篇：10分
  - 50-100篇：7分
  - 10-50篇：5分
  - 1-10篇：3分
  - 0篇：0分

通路证据（0-10分）:
  - OpenTargets总体评分>0.8：10分
  - 评分0.5-0.8：7分
  - 评分0.2-0.5：4分
  - 评分<0.2：1分

Phase 2: Druggability Assessment (0-25 points)

阶段2：成药性评估（0-25分）

Objective: Assess whether the target is amenable to therapeutic intervention.
目标: 评估靶点是否适合进行治疗干预。

2A. OpenTargets Tractability

2A. OpenTargets可开发性

python
undefined
python
undefined

Tractability assessment across modalities

跨模态的可开发性评估

tractability = tu.tools.OpenTargets_get_target_tractability_by_ensemblID(ensemblId=ensembl_id)
tractability = tu.tools.OpenTargets_get_target_tractability_by_ensemblID(ensemblId=ensembl_id)

Returns: label, modality (SM, AB, PR, OC), value (boolean/score)

返回：label, modality (SM, AB, PR, OC), value (布尔值/评分)

Modalities: Small Molecule, Antibody, PROTAC, Other Clinical

治疗模态：Small Molecule（小分子）、Antibody（抗体）、PROTAC、Other Clinical（其他临床类型）

undefined
undefined

2B. Target Class & Family

2B. 靶点类别与家族

python
undefined
python
undefined

Target classification (kinase, GPCR, ion channel, etc.)

靶点分类（激酶、GPCR、离子通道等）

target_classes = tu.tools.OpenTargets_get_target_classes_by_ensemblID(ensemblId=ensembl_id)
target_classes = tu.tools.OpenTargets_get_target_classes_by_ensemblID(ensemblId=ensembl_id)

Pharos target development level

Pharos靶点开发阶段

pharos = tu.tools.Pharos_get_target(gene=gene_symbol)
pharos = tu.tools.Pharos_get_target(gene=gene_symbol)

TDL: Tclin (approved drug) > Tchem (compounds) > Tbio (biology) > Tdark (unknown)

TDL: Tclin（已获批药物） > Tchem（有化合物） > Tbio（有生物学数据） > Tdark（未知）

DGIdb druggability categories

DGIdb成药性分类

druggability = tu.tools.DGIdb_get_gene_druggability(genes=[gene_symbol])
undefined
druggability = tu.tools.DGIdb_get_gene_druggability(genes=[gene_symbol])
undefined

2C. Structural Tractability

2C. 结构可开发性

python
undefined
python
undefined

PDB structures available

可用的PDB结构

if uniprot_id: uniprot_entry = tu.tools.UniProt_get_entry_by_accession(accession=uniprot_id) # Extract PDB cross-references from entry
if uniprot_id: uniprot_entry = tu.tools.UniProt_get_entry_by_accession(accession=uniprot_id) # 从条目中提取PDB交叉引用

AlphaFold prediction

AlphaFold预测结果

alphafold = tu.tools.alphafold_get_prediction(qualifier=uniprot_id) alphafold_summary = tu.tools.alphafold_get_summary(qualifier=uniprot_id)
alphafold = tu.tools.alphafold_get_prediction(qualifier=uniprot_id) alphafold_summary = tu.tools.alphafold_get_summary(qualifier=uniprot_id)

For top PDB structures, analyze binding pockets

针对顶级PDB结构，分析结合口袋

ProteinsPlus DoGSiteScorer for pocket detection

使用ProteinsPlus DoGSiteScorer进行口袋检测

for pdb_id in top_pdb_ids[:3]: pockets = tu.tools.ProteinsPlus_predict_binding_sites(pdb_id=pdb_id) # Returns predicted druggable pockets with scores
undefined
for pdb_id in top_pdb_ids[:3]: pockets = tu.tools.ProteinsPlus_predict_binding_sites(pdb_id=pdb_id) # 返回预测的可成药口袋及评分
undefined

2D. Chemical Probes & Enabling Packages

2D. 化学探针与靶点赋能包

python
undefined
python
undefined

Chemical probes (validated tool compounds)

化学探针（经过验证的工具化合物）

probes = tu.tools.OpenTargets_get_chemical_probes_by_target_ensemblID(ensemblId=ensembl_id)
probes = tu.tools.OpenTargets_get_chemical_probes_by_target_ensemblID(ensemblId=ensembl_id)

Target Enabling Packages (TEPs)

靶点赋能包（TEPs）

teps = tu.tools.OpenTargets_get_target_enabling_packages_by_ensemblID(ensemblId=ensembl_id)
undefined
teps = tu.tools.OpenTargets_get_target_enabling_packages_by_ensemblID(ensemblId=ensembl_id)
undefined

Scoring Logic - Druggability

成药性评分逻辑

Structural Tractability (0-10):
  - High-res co-crystal structure with ligand: 10
  - PDB structure available, pockets detected: 7
  - AlphaFold only, confident pocket prediction: 5
  - AlphaFold low confidence / no structure: 2
  - No structural data: 0

Chemical Matter (0-10):
  - Known drug-like compounds (IC50 < 100nM): 10
  - Tool compounds (IC50 < 1uM): 7
  - HTS hits only (IC50 > 1uM): 4
  - No known ligands: 0

Target Class Bonus (0-5):
  - Validated druggable family (kinase, GPCR, nuclear receptor): 5
  - Enzyme, ion channel: 4
  - Protein-protein interaction, transporter: 2
  - Novel/unknown class: 0
结构可开发性（0-10分）:
  - 带配体的高分辨率共晶结构：10分
  - 有PDB结构且检测到口袋：7分
  - 仅AlphaFold结构，口袋预测可信度高：5分
  - AlphaFold可信度低/无结构：2分
  - 无结构数据：0分

化学物质（0-10分）:
  - 已知类药化合物（IC50 < 100nM）：10分
  - 工具化合物（IC50 < 1uM）：7分
  - 仅HTS命中化合物（IC50 > 1uM）：4分
  - 无已知配体：0分

靶点类别加分（0-5分）:
  - 已验证可成药家族（激酶、GPCR、核受体）：5分
  - 酶、离子通道：4分
  - 蛋白质-蛋白质相互作用、转运体：2分
  - 新型/未知类别：0分

Phase 3: Known Modulators & Chemical Matter (Feeds into Phase 2 scoring)

阶段3：已知调控剂与化学物质（为阶段2评分提供数据）

Objective: Identify existing chemical starting points for target validation.
目标: 识别靶点验证的现有化学起始点。

3A. ChEMBL Bioactivity

3A. ChEMBL生物活性

python
undefined
python
undefined

Search for ChEMBL target

搜索ChEMBL靶点

chembl_targets = tu.tools.ChEMBL_search_targets( pref_name__contains=gene_symbol, organism="Homo sapiens", limit=10 )
chembl_targets = tu.tools.ChEMBL_search_targets( pref_name__contains=gene_symbol, organism="Homo sapiens", limit=10 )

Get activities for best matching target

获取最佳匹配靶点的活性数据

target_chembl_id = chembl_targets[0]['target_chembl_id'] activities = tu.tools.ChEMBL_get_target_activities( target_chembl_id__exact=target_chembl_id, limit=100 )
target_chembl_id = chembl_targets[0]['target_chembl_id'] activities = tu.tools.ChEMBL_get_target_activities( target_chembl_id__exact=target_chembl_id, limit=100 )

Parse: compound IDs, pChEMBL values, activity types (IC50, Ki, Kd)

解析：化合物ID、pChEMBL值、活性类型（IC50, Ki, Kd）

Filter: potent compounds (pChEMBL >= 6.0 = IC50 <= 1uM)

筛选：强效化合物（pChEMBL >= 6.0 = IC50 <= 1uM）

undefined
undefined

3B. BindingDB Ligands

3B. BindingDB配体

python
undefined
python
undefined

Experimental binding data

实验结合数据

ligands = tu.tools.BindingDB_get_ligands_by_uniprot( uniprot=uniprot_id, affinity_cutoff=10000 # nM )
ligands = tu.tools.BindingDB_get_ligands_by_uniprot( uniprot=uniprot_id, affinity_cutoff=10000 # nM )

Returns: SMILES, affinity_type (Ki/IC50/Kd), affinity value, PMID

返回：SMILES、亲和力类型（Ki/IC50/Kd）、亲和力值、PMID

undefined
undefined

3C. PubChem Bioassays

3C. PubChem生物测定

python
undefined
python
undefined

HTS screening data

HTS筛选数据

assays = tu.tools.PubChem_search_assays_by_target_gene(gene_symbol=gene_symbol)
assays = tu.tools.PubChem_search_assays_by_target_gene(gene_symbol=gene_symbol)

Get details for top assays

获取顶级测定的详细信息

for aid in assay_ids[:5]: summary = tu.tools.PubChem_get_assay_summary(aid=str(aid)) targets = tu.tools.PubChem_get_assay_targets(aid=str(aid)) actives = tu.tools.PubChem_get_assay_active_compounds(aid=str(aid))
undefined
for aid in assay_ids[:5]: summary = tu.tools.PubChem_get_assay_summary(aid=str(aid)) targets = tu.tools.PubChem_get_assay_targets(aid=str(aid)) actives = tu.tools.PubChem_get_assay_active_compounds(aid=str(aid))
undefined

3D. Known Drugs Targeting This Protein

3D. 靶向该蛋白质的已知药物

python
undefined
python
undefined

OpenTargets known drugs

OpenTargets已知药物

drugs = tu.tools.OpenTargets_get_associated_drugs_by_target_ensemblID( ensemblId=ensembl_id, size=25 )
drugs = tu.tools.OpenTargets_get_associated_drugs_by_target_ensemblID( ensemblId=ensembl_id, size=25 )

ChEMBL drug mechanisms

ChEMBL药物作用机制

drug_mechanisms = tu.tools.ChEMBL_search_mechanisms( target_chembl_id=target_chembl_id, limit=50 )
drug_mechanisms = tu.tools.ChEMBL_search_mechanisms( target_chembl_id=target_chembl_id, limit=50 )

Drug interaction databases

药物相互作用数据库

dgidb = tu.tools.DGIdb_get_gene_info(genes=[gene_symbol])
undefined
dgidb = tu.tools.DGIdb_get_gene_info(genes=[gene_symbol])
undefined

Report Format - Chemical Matter

化学物质报告格式

markdown
undefined
markdown
undefined

4. Known Modulators & Chemical Matter

4. 已知调控剂与化学物质

4.1 Approved Drugs

4.1 已获批药物

DrugChEMBL IDMechanismPhaseIndicationSource
ErlotinibCHEMBL553Inhibitor4NSCLC[T1] OpenTargets
GefitinibCHEMBL939Inhibitor4NSCLC[T1] OpenTargets
药物ChEMBL ID作用机制阶段适应症来源
ErlotinibCHEMBL553抑制剂4NSCLC[T1] OpenTargets
GefitinibCHEMBL939抑制剂4NSCLC[T1] OpenTargets

4.2 ChEMBL Bioactivity Summary

4.2 ChEMBL生物活性摘要

Total Activities: 12,456 datapoints across 2,341 assays Most Potent Compound: CHEMBL413456 (IC50 = 0.3 nM) [T1] Chemical Series: 8 distinct scaffolds with pChEMBL >= 7.0 Selectivity Data: Available for 45 compounds (kinase panel)
总活性数据点: 12,456个，覆盖2,341个测定 最强效化合物: CHEMBL413456 (IC50 = 0.3 nM) [T1] 化学系列: 8个不同骨架，pChEMBL >= 7.0 选择性数据: 45种化合物有激酶面板选择性数据

4.3 BindingDB Ligands

4.3 BindingDB配体

Total Ligands: 856 with measured affinity Best Affinity: 0.1 nM (Ki) Affinity Distribution: <1nM: 23, 1-10nM: 89, 10-100nM: 234, 100nM-1uM: 510
总配体数: 856个，带有测量亲和力 最佳亲和力: 0.1 nM (Ki) 亲和力分布: <1nM: 23个, 1-10nM: 89个, 10-100nM: 234个, 100nM-1uM: 510个

4.4 Chemical Probes

4.4 化学探针

ProbeSourcePotencySelectivityUse
SGC-1234SGCIC50=5nM>100xIn vitro

---
探针来源效力选择性用途
SGC-1234SGCIC50=5nM>100倍体外实验

---

Phase 4: Clinical Precedent (0-15 points)

阶段4：临床先例（0-15分）

Objective: Assess clinical validation from approved drugs and clinical trials.
目标: 从已获批药物和临床试验评估临床验证情况。

4A. FDA-Approved Drugs

4A. FDA获批药物

python
undefined
python
undefined

FDA label information

FDA标签信息

fda_moa = tu.tools.FDA_get_mechanism_of_action_by_drug_name(drug_name=gene_symbol) fda_indications = tu.tools.FDA_get_indications_by_drug_name(drug_name=known_drug_name)
fda_moa = tu.tools.FDA_get_mechanism_of_action_by_drug_name(drug_name=gene_symbol) fda_indications = tu.tools.FDA_get_indications_by_drug_name(drug_name=known_drug_name)

DrugBank pharmacology

DrugBank药理学

drugbank_targets = tu.tools.drugbank_get_targets_by_drug_name_or_drugbank_id( query=known_drug_name, case_sensitive=False, exact_match=False, limit=10 )
drugbank_targets = tu.tools.drugbank_get_targets_by_drug_name_or_drugbank_id( query=known_drug_name, case_sensitive=False, exact_match=False, limit=10 )

DrugBank safety info

DrugBank安全信息

drugbank_safety = tu.tools.drugbank_get_safety_by_drug_name_or_drugbank_id( query=known_drug_name, case_sensitive=False, exact_match=False, limit=10 )
undefined
drugbank_safety = tu.tools.drugbank_get_safety_by_drug_name_or_drugbank_id( query=known_drug_name, case_sensitive=False, exact_match=False, limit=10 )
undefined

4B. Clinical Trials

4B. 临床试验

python
undefined
python
undefined

Active clinical trials targeting this protein

针对该蛋白质的活跃临床试验

trials = tu.tools.search_clinical_trials( query_term=gene_symbol, intervention=gene_symbol, pageSize=50 )
trials = tu.tools.search_clinical_trials( query_term=gene_symbol, intervention=gene_symbol, pageSize=50 )

If specific disease context

如果有特定疾病背景

if disease_name: disease_trials = tu.tools.search_clinical_trials( query_term=gene_symbol, condition=disease_name, pageSize=50 )
undefined
if disease_name: disease_trials = tu.tools.search_clinical_trials( query_term=gene_symbol, condition=disease_name, pageSize=50 )
undefined

4C. Failed Programs (Learn from Failures)

4C. 失败项目（从失败中学习）

python
undefined
python
undefined

Drug warnings and withdrawals

药物警告与撤市信息

for drug_chembl_id in known_drug_ids: warnings = tu.tools.OpenTargets_get_drug_warnings_by_chemblId(chemblId=drug_chembl_id) adverse = tu.tools.OpenTargets_get_drug_adverse_events_by_chemblId(chemblId=drug_chembl_id)
undefined
for drug_chembl_id in known_drug_ids: warnings = tu.tools.OpenTargets_get_drug_warnings_by_chemblId(chemblId=drug_chembl_id) adverse = tu.tools.OpenTargets_get_drug_adverse_events_by_chemblId(chemblId=drug_chembl_id)
undefined

Scoring Logic - Clinical Precedent

临床先例评分逻辑

Clinical Precedent (0-15):
  - FDA-approved drug for SAME disease: 15
  - FDA-approved drug for DIFFERENT disease: 12
  - Phase 3 clinical trial: 10
  - Phase 2 clinical trial: 7
  - Phase 1 clinical trial: 5
  - Preclinical compounds only: 3
  - No clinical development: 0

Adjustment factors:
  - Failed clinical program for safety: -3
  - Drug withdrawal: -5
  - Multiple approved drugs (validated class): +2
临床先例（0-15分）:
  - FDA获批用于同一疾病的药物：15分
  - FDA获批用于其他疾病的药物：12分
  - 3期临床试验：10分
  - 2期临床试验：7分
  - 1期临床试验：5分
  - 仅临床前化合物：3分
  - 无临床开发：0分

调整因素:
  - 因安全性失败的临床项目：-3分
  - 药物撤市：-5分
  - 多种获批药物（已验证类别）：+2分

Phase 5: Safety & Toxicity Considerations (0-20 points)

阶段5：安全性与毒性考量（0-20分）

Objective: Identify safety risks from expression, genetics, and known adverse events.
目标: 从表达、遗传学和已知不良事件中识别安全风险。

5A. OpenTargets Safety Profile

5A. OpenTargets安全性概况

python
safety = tu.tools.OpenTargets_get_target_safety_profile_by_ensemblID(ensemblId=ensembl_id)
python
safety = tu.tools.OpenTargets_get_target_safety_profile_by_ensemblID(ensemblId=ensembl_id)

Returns: safety liabilities, adverse effects, experimental toxicity

返回：安全隐患、不良反应、实验毒性

undefined
undefined

5B. Expression in Critical Tissues

5B. 关键组织中的表达

python
undefined
python
undefined

GTEx tissue expression (identifies essential organ expression)

GTEx组织表达（识别重要器官中的表达）

gtex = tu.tools.GTEx_get_median_gene_expression( operation="median", gencode_id=ensembl_versioned_id )
gtex = tu.tools.GTEx_get_median_gene_expression( operation="median", gencode_id=ensembl_versioned_id )

If empty, try unversioned ID

如果为空，尝试不带版本的ID

HPA expression

HPA表达

NOTE: HPA_get_rna_expression_by_source requires gene_name, source_type, source_name

注意：HPA_get_rna_expression_by_source需要gene_name, source_type, source_name

hpa = tu.tools.HPA_search_genes_by_query(search_query=gene_symbol) hpa_details = tu.tools.HPA_get_comprehensive_gene_details_by_ensembl_id(ensembl_id=ensembl_id)
hpa = tu.tools.HPA_search_genes_by_query(search_query=gene_symbol) hpa_details = tu.tools.HPA_get_comprehensive_gene_details_by_ensembl_id(ensembl_id=ensembl_id)

Check expression in safety-critical tissues

检查安全关键组织中的表达

Heart, liver, kidney, brain, bone marrow = high risk if target is expressed

心脏、肝脏、肾脏、大脑、骨髓 = 如果靶点在此表达则风险高

undefined
undefined

5C. Knockout Phenotypes

5C. 敲除表型

python
undefined
python
undefined

Mouse model phenotypes

小鼠模型表型

mouse_models = tu.tools.OpenTargets_get_biological_mouse_models_by_ensemblID(ensemblId=ensembl_id)
mouse_models = tu.tools.OpenTargets_get_biological_mouse_models_by_ensemblID(ensemblId=ensembl_id)

Genetic constraint (proxy for essentiality)

遗传约束（必需性的替代指标）

constraints = tu.tools.gnomad_get_gene_constraints(gene_symbol=gene_symbol)
constraints = tu.tools.gnomad_get_gene_constraints(gene_symbol=gene_symbol)

High pLI = essential gene = potential safety concern

高pLI = 必需基因 = 潜在安全隐患

undefined
undefined

5D. Known Adverse Events from Target Modulation

5D. 靶点调控的已知不良事件

python
undefined
python
undefined

For known drugs targeting this protein

针对靶向该蛋白质的已知药物

for drug_name in known_drug_names: fda_adr = tu.tools.FDA_get_adverse_reactions_by_drug_name(drug_name=drug_name) fda_warnings = tu.tools.FDA_get_warnings_and_cautions_by_drug_name(drug_name=drug_name) fda_boxed = tu.tools.FDA_get_boxed_warning_info_by_drug_name(drug_name=drug_name) fda_contraindications = tu.tools.FDA_get_contraindications_by_drug_name(drug_name=drug_name)
undefined
for drug_name in known_drug_names: fda_adr = tu.tools.FDA_get_adverse_reactions_by_drug_name(drug_name=drug_name) fda_warnings = tu.tools.FDA_get_warnings_and_cautions_by_drug_name(drug_name=drug_name) fda_boxed = tu.tools.FDA_get_boxed_warning_info_by_drug_name(drug_name=drug_name) fda_contraindications = tu.tools.FDA_get_contraindications_by_drug_name(drug_name=drug_name)
undefined

5E. Homologs & Off-Target Risks

5E. 同源物与脱靶风险

python
undefined
python
undefined

Paralogs (close family members that might be hit)

旁系同源物（可能被命中的近缘家族成员）

homologs = tu.tools.OpenTargets_get_target_homologues_by_ensemblID(ensemblId=ensembl_id)
homologs = tu.tools.OpenTargets_get_target_homologues_by_ensemblID(ensemblId=ensembl_id)

Paralogs with high sequence identity = selectivity challenge

序列同一性高的旁系同源物 = 选择性挑战

undefined
undefined

Scoring Logic - Safety

安全性评分逻辑

Tissue Expression Selectivity (0-5):
  - Target restricted to disease tissue: 5
  - Low expression in heart/liver/kidney/brain: 4
  - Moderate expression in 1-2 critical tissues: 2
  - High expression in multiple critical tissues: 0

Genetic Validation (0-10):
  - Mouse KO viable, no severe phenotype: 10
  - Mouse KO viable with mild phenotype: 7
  - Mouse KO has concerning phenotype: 3
  - Mouse KO lethal: 0
  - No KO data, low pLI (<0.5): 5
  - No KO data, high pLI (>0.9): 2

Known Adverse Events (0-5):
  - No known safety signals: 5
  - Mild, manageable ADRs: 3
  - Serious ADRs reported: 1
  - Black box warning or drug withdrawal: 0
组织表达选择性（0-5分）:
  - 靶点仅在疾病组织中表达：5分
  - 心脏/肝脏/肾脏/大脑中低表达：4分
  - 在1-2个关键组织中中度表达：2分
  - 在多个关键组织中高表达：0分

遗传验证（0-10分）:
  - 基因敲除小鼠存活，无严重表型：10分
  - 基因敲除小鼠存活，有轻度表型：7分
  - 基因敲除小鼠有相关表型：3分
  - 基因敲除小鼠致死：0分
  - 无敲除数据，低pLI(<0.5)：5分
  - 无敲除数据，高pLI(>0.9)：2分

已知不良事件（0-5分）:
  - 无已知安全信号：5分
  - 轻度、可管理的ADR：3分
  - 报告严重ADR：1分
  - 黑框警告或药物撤市：0分

Phase 6: Pathway Context & Network Analysis

阶段6：通路背景与网络分析

Objective: Understand the target's role in biological networks and disease pathways.
目标: 理解靶点在生物网络和疾病通路中的作用。

6A. Reactome Pathways

6A. Reactome通路

python
undefined
python
undefined

Map target to pathways

将靶点映射到通路

pathways = tu.tools.Reactome_map_uniprot_to_pathways(id=uniprot_id)
pathways = tu.tools.Reactome_map_uniprot_to_pathways(id=uniprot_id)

Get pathway details for top pathways

获取顶级通路的详细信息

for pathway in top_pathways[:5]: detail = tu.tools.Reactome_get_pathway(id=pathway['stId']) reactions = tu.tools.Reactome_get_pathway_reactions(id=pathway['stId'])
undefined
for pathway in top_pathways[:5]: detail = tu.tools.Reactome_get_pathway(id=pathway['stId']) reactions = tu.tools.Reactome_get_pathway_reactions(id=pathway['stId'])
undefined

6B. Protein-Protein Interactions

6B. 蛋白质-蛋白质相互作用

python
undefined
python
undefined

STRING network

STRING网络

string_ppi = tu.tools.STRING_get_protein_interactions( protein_ids=[gene_symbol], species=9606, confidence_score=0.7 )
string_ppi = tu.tools.STRING_get_protein_interactions( protein_ids=[gene_symbol], species=9606, confidence_score=0.7 )

Higher confidence = more reliable

置信度越高 = 越可靠

IntAct interactions (experimental)

IntAct相互作用（实验性）

intact_ppi = tu.tools.intact_get_interactions(identifier=uniprot_id)
intact_ppi = tu.tools.intact_get_interactions(identifier=uniprot_id)

OpenTargets interactions

OpenTargets相互作用

ot_ppi = tu.tools.OpenTargets_get_target_interactions_by_ensemblID(ensemblId=ensembl_id)
undefined
ot_ppi = tu.tools.OpenTargets_get_target_interactions_by_ensemblID(ensemblId=ensembl_id)
undefined

6C. Functional Enrichment

6C. 功能富集

python
undefined
python
undefined

GO annotations

GO注释

go_terms = tu.tools.OpenTargets_get_target_gene_ontology_by_ensemblID(ensemblId=ensembl_id)
go_terms = tu.tools.OpenTargets_get_target_gene_ontology_by_ensemblID(ensemblId=ensembl_id)

Direct GO query

直接GO查询

go_annotations = tu.tools.GO_get_annotations_for_gene(gene_id=gene_symbol)
go_annotations = tu.tools.GO_get_annotations_for_gene(gene_id=gene_symbol)

STRING functional enrichment of interaction partners

相互作用伙伴的STRING功能富集

enrichment = tu.tools.STRING_functional_enrichment( protein_ids=[gene_symbol], species=9606 )
undefined
enrichment = tu.tools.STRING_functional_enrichment( protein_ids=[gene_symbol], species=9606 )
undefined

Report Format - Pathway Context

通路背景报告格式

markdown
undefined
markdown
undefined

7. Pathway Context & Network Analysis

7. 通路背景与网络分析

7.1 Key Pathways

7.1 关键通路

PathwayReactome IDRelevance to DiseaseEvidence
EGFR signalingR-HSA-177929Driver pathway in NSCLC[T1]
RAS-RAF-MEK-ERKR-HSA-5673001Downstream effector[T1]
PI3K-AKT signalingR-HSA-2219528Resistance mechanism[T2]
通路Reactome ID与疾病的相关性证据
EGFR信号通路R-HSA-177929NSCLC中的驱动通路[T1]
RAS-RAF-MEK-ERKR-HSA-5673001下游效应通路[T1]
PI3K-AKT信号通路R-HSA-2219528耐药机制[T2]

7.2 Protein-Protein Interactions

7.2 蛋白质-蛋白质相互作用

Total Interactors: 45 (STRING confidence > 0.7) Key Interactors: GRB2, SHC1, PLCG1, PIK3CA, STAT3
总相互作用伙伴: 45个（STRING置信度>0.7） 关键相互作用伙伴: GRB2, SHC1, PLCG1, PIK3CA, STAT3

7.3 Pathway Redundancy Assessment

7.3 通路冗余评估

Compensation Risk: MODERATE
  • Parallel pathways: HER2, HER3 can compensate
  • Feedback loops: RAS activation bypasses EGFR
  • Downstream convergence: MEK/ERK shared with other RTKs

---
补偿风险: 中等
  • 平行通路：HER2、HER3可补偿
  • 反馈环路：RAS激活绕过EGFR
  • 下游收敛：MEK/ERK与其他RTKs共享

---

Phase 7: Validation Evidence (0-10 points)

阶段7：验证证据（0-10分）

Objective: Assess existing functional validation data.
目标: 评估现有功能验证数据。

7A. DepMap Essentiality (CRISPR/RNAi)

7A. DepMap必需性（CRISPR/RNAi）

python
undefined
python
undefined

Gene essentiality in cancer cell lines

癌细胞系中的基因必需性

deps = tu.tools.DepMap_get_gene_dependencies(gene_symbol=gene_symbol)
deps = tu.tools.DepMap_get_gene_dependencies(gene_symbol=gene_symbol)

Negative scores = essential (cells die upon KO)

负分 = 必需（敲除后细胞死亡）

Score < -0.5: moderately essential

评分 < -0.5: 中度必需

Score < -1.0: strongly essential

评分 < -1.0: 高度必需

undefined
undefined

7B. Literature Validation Evidence

7B. 文献验证证据

python
undefined
python
undefined

Search for functional studies

搜索功能研究

validation_papers = tu.tools.PubMed_search_articles( query=f'"{gene_symbol}" AND (CRISPR OR siRNA OR knockdown OR knockout OR "loss of function") AND "{disease_name}"', limit=30 )
validation_papers = tu.tools.PubMed_search_articles( query=f'"{gene_symbol}" AND (CRISPR OR siRNA OR knockdown OR knockout OR "loss of function") AND "{disease_name}"', limit=30 )

Search for biomarker studies

搜索生物标志物研究

biomarker_papers = tu.tools.PubMed_search_articles( query=f'"{gene_symbol}" AND (biomarker OR "target engagement" OR "pharmacodynamic")', limit=20 )
undefined
biomarker_papers = tu.tools.PubMed_search_articles( query=f'"{gene_symbol}" AND (biomarker OR "target engagement" OR "pharmacodynamic")', limit=20 )
undefined

7C. Animal Model Evidence

7C. 动物模型证据

python
undefined
python
undefined

Mouse phenotypes from OpenTargets (already retrieved in Phase 5)

OpenTargets中的小鼠表型（已在阶段5获取）

Reuse mouse_models data

复用mouse_models数据

CTD gene-disease associations (complementary)

CTD基因-疾病关联（补充数据）

ctd_diseases = tu.tools.CTD_get_gene_diseases(input_terms=gene_symbol)
undefined
ctd_diseases = tu.tools.CTD_get_gene_diseases(input_terms=gene_symbol)
undefined

Scoring Logic - Validation Evidence

验证证据评分逻辑

Functional Studies (0-5):
  - CRISPR KO shows disease-relevant phenotype: 5
  - siRNA knockdown shows phenotype: 4
  - Biochemical assay validates mechanism: 3
  - Overexpression study only: 2
  - No functional data: 0

Disease Models (0-5):
  - Patient-derived xenograft (PDX) response: 5
  - Genetically engineered mouse model: 4
  - Cell line model: 3
  - In silico model only: 1
  - No model data: 0
功能研究（0-5分）:
  - CRISPR敲除显示疾病相关表型：5分
  - siRNA敲低显示表型：4分
  - 生化实验验证机制：3分
  - 仅过表达研究：2分
  - 无功能数据：0分

疾病模型（0-5分）:
  - 患者来源异种移植（PDX）有响应：5分
  - 基因工程小鼠模型：4分
  - 细胞系模型：3分
  - 仅计算机模型：1分
  - 无模型数据：0分

Phase 8: Structural Insights

阶段8：结构见解

Objective: Leverage structural biology for druggability and mechanism understanding.
目标: 利用结构生物学理解成药性和作用机制。

8A. PDB Structures

8A. PDB结构

python
undefined
python
undefined

Get PDB entries from UniProt cross-references

从UniProt交叉引用获取PDB条目

uniprot_entry = tu.tools.UniProt_get_entry_by_accession(accession=uniprot_id)
uniprot_entry = tu.tools.UniProt_get_entry_by_accession(accession=uniprot_id)

Parse: uniProtKBCrossReferences where database == "PDB"

解析：database为"PDB"的uniProtKBCrossReferences

Get details for each PDB

获取每个PDB的详细信息

for pdb_id in pdb_ids[:10]: metadata = tu.tools.get_protein_metadata_by_pdb_id(pdb_id=pdb_id) quality = tu.tools.pdbe_get_entry_quality(pdb_id=pdb_id) summary = tu.tools.pdbe_get_entry_summary(pdb_id=pdb_id) experiment = tu.tools.pdbe_get_entry_experiment(pdb_id=pdb_id) molecules = tu.tools.pdbe_get_entry_molecules(pdb_id=pdb_id)
undefined
for pdb_id in pdb_ids[:10]: metadata = tu.tools.get_protein_metadata_by_pdb_id(pdb_id=pdb_id) quality = tu.tools.pdbe_get_entry_quality(pdb_id=pdb_id) summary = tu.tools.pdbe_get_entry_summary(pdb_id=pdb_id) experiment = tu.tools.pdbe_get_entry_experiment(pdb_id=pdb_id) molecules = tu.tools.pdbe_get_entry_molecules(pdb_id=pdb_id)
undefined

8B. AlphaFold Prediction

8B. AlphaFold预测

python
alphafold = tu.tools.alphafold_get_prediction(qualifier=uniprot_id)
alphafold_info = tu.tools.alphafold_get_summary(qualifier=uniprot_id)
python
alphafold = tu.tools.alphafold_get_prediction(qualifier=uniprot_id)
alphafold_info = tu.tools.alphafold_get_summary(qualifier=uniprot_id)

Check pLDDT scores for confidence

检查pLDDT评分以确认置信度

undefined
undefined

8C. Binding Pocket Analysis

8C. 结合口袋分析

python
undefined
python
undefined

ProteinsPlus DoGSiteScorer for best PDB structure

针对最佳PDB结构使用ProteinsPlus DoGSiteScorer

pockets = tu.tools.ProteinsPlus_predict_binding_sites(pdb_id=best_pdb_id)
pockets = tu.tools.ProteinsPlus_predict_binding_sites(pdb_id=best_pdb_id)

Returns: pocket locations, druggability scores, volume, surface

返回：口袋位置、成药性评分、体积、表面信息

Interaction diagram for co-crystal structures

共晶结构的相互作用图

if has_ligand: diagram = tu.tools.ProteinsPlus_generate_interaction_diagram(pdb_id=pdb_id)
undefined
if has_ligand: diagram = tu.tools.ProteinsPlus_generate_interaction_diagram(pdb_id=pdb_id)
undefined

8D. Domain Architecture

8D. 结构域架构

python
undefined
python
undefined

InterPro domains

InterPro结构域

domains = tu.tools.InterPro_get_protein_domains(uniprot_accession=uniprot_id)
domains = tu.tools.InterPro_get_protein_domains(uniprot_accession=uniprot_id)

Domain details for key domains

关键结构域的详细信息

for domain in domains[:5]: detail = tu.tools.InterPro_get_domain_details(entry_id=domain['accession'])

---
for domain in domains[:5]: detail = tu.tools.InterPro_get_domain_details(entry_id=domain['accession'])

---

Phase 9: Literature Deep Dive

阶段9：文献深度挖掘

Objective: Comprehensive literature analysis with collision-aware search.
目标: 结合碰撞检测的全面文献分析。

9A. Collision Detection

9A. 碰撞检测

python
undefined
python
undefined

Detect naming collisions before literature search

文献搜索前检测命名冲突

test_results = tu.tools.PubMed_search_articles( query=f'"{gene_symbol}"[Title]', limit=20 )
test_results = tu.tools.PubMed_search_articles( query=f'"{gene_symbol}"[Title]', limit=20 )

PubMed returns plain list of dicts

PubMed返回纯字典列表

Check if >20% of results are off-topic (no biology terms)

检查是否>20%的结果偏离主题（无生物学术语）

If collision detected, add filters: AND (protein OR gene OR receptor OR kinase)

如果检测到冲突，添加过滤条件：AND (protein OR gene OR receptor OR kinase)

undefined
undefined

9B. Publication Metrics

9B. 出版物指标

python
undefined
python
undefined

Total publications

总出版物数量

total = tu.tools.PubMed_search_articles( query=f'"{gene_symbol}" AND (protein OR gene)', limit=1 )
total = tu.tools.PubMed_search_articles( query=f'"{gene_symbol}" AND (protein OR gene)', limit=1 )

Check total_count field

检查total_count字段

Recent publications (5-year trend)

近期出版物（5年趋势）

recent = tu.tools.PubMed_search_articles( query=f'"{gene_symbol}" AND (protein OR gene) AND ("2021"[PDAT] : "2026"[PDAT])', limit=50 )
recent = tu.tools.PubMed_search_articles( query=f'"{gene_symbol}" AND (protein OR gene) AND ("2021"[PDAT] : "2026"[PDAT])', limit=50 )

Drug-focused publications

药物相关出版物

drug_pubs = tu.tools.PubMed_search_articles( query=f'"{gene_symbol}" AND (drug OR therapeutic OR inhibitor OR antibody)', limit=30 )
drug_pubs = tu.tools.PubMed_search_articles( query=f'"{gene_symbol}" AND (drug OR therapeutic OR inhibitor OR antibody)', limit=30 )

EuropePMC for broader coverage

EuropePMC更广泛的覆盖

epmc = tu.tools.EuropePMC_search_articles( query=f'"{gene_symbol}" AND drug target', limit=30 )
undefined
epmc = tu.tools.EuropePMC_search_articles( query=f'"{gene_symbol}" AND drug target', limit=30 )
undefined

9C. Key Reviews and Landmark Papers

9C. 关键综述与里程碑论文

python
undefined
python
undefined

Reviews for target overview

靶点综述

reviews = tu.tools.PubMed_search_articles( query=f'"{gene_symbol}" AND drug target AND review[pt]', limit=10 )
reviews = tu.tools.PubMed_search_articles( query=f'"{gene_symbol}" AND drug target AND review[pt]', limit=10 )

OpenAlex for citation metrics

OpenAlex引用指标

openalex_works = tu.tools.openalex_search_works( query=f'{gene_symbol} drug target', limit=20 )

---
openalex_works = tu.tools.openalex_search_works( query=f'{gene_symbol} drug target', limit=20 )

---

Phase 10: Validation Roadmap (Synthesis)

阶段10：验证路线图（综合）

Objective: Generate actionable recommendations based on all evidence.
This phase synthesizes all previous phases into:
  1. Target Validation Score (0-100)
  2. Priority Tier (1-4)
  3. GO/NO-GO Recommendation
  4. Recommended Experiments
  5. Tool Compounds for Testing
  6. Biomarker Strategy
  7. Key Risks & Mitigations
目标: 基于所有证据生成可执行建议。
本阶段将所有前期阶段的结果综合为：
  1. 靶点验证评分（0-100）
  2. 优先级层级（1-4）
  3. GO/NO-GO建议
  4. 推荐实验
  5. 测试用工具化合物
  6. 生物标志物策略
  7. 关键风险与缓解措施

Score Calculation

评分计算

python
def calculate_validation_score(phase_results):
    """
    Calculate Target Validation Score (0-100).

    Components:
    - Disease Association: 0-30
    - Druggability: 0-25
    - Safety: 0-20
    - Clinical Precedent: 0-15
    - Validation Evidence: 0-10
    """
    score = {
        'disease_genetic': 0,      # 0-10
        'disease_literature': 0,   # 0-10
        'disease_pathway': 0,      # 0-10
        'drug_structural': 0,      # 0-10
        'drug_chemical': 0,        # 0-10
        'drug_class': 0,           # 0-5
        'safety_expression': 0,    # 0-5
        'safety_genetic': 0,       # 0-10
        'safety_adverse': 0,       # 0-5
        'clinical': 0,             # 0-15
        'validation_functional': 0, # 0-5
        'validation_models': 0,    # 0-5
    }

    # ... scoring logic from each phase ...

    total = sum(score.values())

    if total >= 80:
        tier = "Tier 1"
        recommendation = "GO - Highly validated target"
    elif total >= 60:
        tier = "Tier 2"
        recommendation = "CONDITIONAL GO - Needs focused validation"
    elif total >= 40:
        tier = "Tier 3"
        recommendation = "CAUTION - Significant validation needed"
    else:
        tier = "Tier 4"
        recommendation = "NO-GO - Consider alternatives"

    return total, tier, recommendation, score
python
def calculate_validation_score(phase_results):
    """
    计算靶点验证评分（0-100）。

    构成部分:
    - 疾病关联性: 0-30
    - 成药性: 0-25
    - 安全性: 0-20
    - 临床先例: 0-15
    - 验证证据: 0-10
    """
    score = {
        'disease_genetic': 0,      # 0-10
        'disease_literature': 0,   # 0-10
        'disease_pathway': 0,      # 0-10
        'drug_structural': 0,      # 0-10
        'drug_chemical': 0,        # 0-10
        'drug_class': 0,           # 0-5
        'safety_expression': 0,    # 0-5
        'safety_genetic': 0,       # 0-10
        'safety_adverse': 0,       # 0-5
        'clinical': 0,             # 0-15
        'validation_functional': 0, # 0-5
        'validation_models': 0,    # 0-5
    }

    # ... 各阶段的评分逻辑 ...

    total = sum(score.values())

    if total >= 80:
        tier = "Tier 1"
        recommendation = "GO - Highly validated target"
    elif total >= 60:
        tier = "Tier 2"
        recommendation = "CONDITIONAL GO - Needs focused validation"
    elif total >= 40:
        tier = "Tier 3"
        recommendation = "CAUTION - Significant validation needed"
    else:
        tier = "Tier 4"
        recommendation = "NO-GO - Consider alternatives"

    return total, tier, recommendation, score

Report Template

报告模板

File: 
[TARGET]_[DISEASE]_validation_report.md
markdown
undefined
文件: 
[TARGET]_[DISEASE]_validation_report.md
markdown
undefined

Drug Target Validation Report: [TARGET]

药物靶点验证报告: [TARGET]

Target: [Gene Symbol] ([Full Name]) Disease Context: [Disease Name] (if provided) Modality: [Small molecule / Antibody / etc.] (if specified) Generated: [Date] Status: In Progress
靶点: [基因符号] ([全称]) 疾病背景: [疾病名称]（若提供） 治疗模态: [小分子/抗体/等]（若指定） 生成日期: [日期] 状态: 进行中

Executive Summary

执行摘要

Target Validation Score: [XX/100] Priority Tier: [Tier X] - [Description] Recommendation: [GO / CONDITIONAL GO / CAUTION / NO-GO]
Key Findings:
  • [1-sentence disease association strength with evidence grade]
  • [1-sentence druggability assessment]
  • [1-sentence safety profile]
  • [1-sentence clinical precedent]
Critical Risks:
  • [Top risk 1]
  • [Top risk 2]
靶点验证评分: [XX/100] 优先级层级: [层级X] - [描述] 建议: [GO / 条件性GO / 谨慎推进 / NO-GO]
关键发现:
  • [1句话总结疾病关联强度及证据等级]
  • [1句话总结成药性评估]
  • [1句话总结安全性概况]
  • [1句话总结临床先例]
关键风险:
  • [顶级风险1]
  • [顶级风险2]

Validation Scorecard

验证评分卡

DimensionScoreMaxAssessmentKey Evidence
Disease Association30
- Genetic evidence10
- Literature evidence10
- Pathway evidence10
Druggability25
- Structural tractability10
- Chemical matter10
- Target class5
Safety Profile20
- Expression selectivity5
- Genetic validation10
- Known ADRs5
Clinical Precedent15
Validation Evidence10
- Functional studies5
- Disease models5
TOTALXX100[Tier]
维度得分满分评估关键证据
疾病关联性30
- 遗传证据10
- 文献证据10
- 通路证据10
成药性25
- 结构可开发性10
- 化学物质10
- 靶点类别5
安全性概况20
- 表达选择性5
- 遗传验证10
- 已知ADR5
临床先例15
验证证据10
- 功能研究5
- 疾病模型5
总分XX100[层级]

1. Target Identity

1. 靶点标识

[Researching...]
[研究中...]

2. Disease Association Evidence

2. 疾病关联性证据

2.1 OpenTargets Disease Associations

2.1 OpenTargets疾病关联

[Researching...]
[研究中...]

2.2 GWAS Genetic Evidence

2.2 GWAS遗传证据

[Researching...]
[研究中...]

2.3 Constraint Scores (gnomAD)

2.3 约束评分（gnomAD）

[Researching...]
[研究中...]

2.4 Literature Evidence

2.4 文献证据

[Researching...]
[研究中...]

3. Druggability Assessment

3. 成药性评估

3.1 Tractability (OpenTargets)

3.1 可开发性（OpenTargets）

[Researching...]
[研究中...]

3.2 Target Classification

3.2 靶点分类

[Researching...]
[研究中...]

3.3 Structural Tractability

3.3 结构可开发性

[Researching...]
[研究中...]

3.4 Chemical Probes & Enabling Packages

3.4 化学探针与靶点赋能包

[Researching...]
[研究中...]

4. Known Modulators & Chemical Matter

4. 已知调控剂与化学物质

4.1 Approved/Clinical Drugs

4.1 已获批/临床阶段药物

[Researching...]
[研究中...]

4.2 ChEMBL Bioactivity

4.2 ChEMBL生物活性

[Researching...]
[研究中...]

4.3 BindingDB Ligands

4.3 BindingDB配体

[Researching...]
[研究中...]

4.4 PubChem Bioassays

4.4 PubChem生物测定

[Researching...]
[研究中...]

4.5 Chemical Probes

4.5 化学探针

[Researching...]
[研究中...]

5. Clinical Precedent

5. 临床先例

5.1 FDA-Approved Drugs

5.1 FDA获批药物

[Researching...]
[研究中...]

5.2 Clinical Trial Landscape

5.2 临床试验现状

[Researching...]
[研究中...]

5.3 Failed Programs & Lessons

5.3 失败项目与经验教训

[Researching...]
[研究中...]

6. Safety & Toxicity Profile

6. 安全性与毒性概况

6.1 OpenTargets Safety Liabilities

6.1 OpenTargets安全隐患

[Researching...]
[研究中...]

6.2 Expression in Critical Tissues

6.2 关键组织中的表达

[Researching...]
[研究中...]

6.3 Knockout Phenotypes

6.3 敲除表型

[Researching...]
[研究中...]

6.4 Known Adverse Events

6.4 已知不良事件

[Researching...]
[研究中...]

6.5 Paralog & Off-Target Risks

6.5 旁系同源物与脱靶风险

[Researching...]
[研究中...]

7. Pathway Context & Network Analysis

7. 通路背景与网络分析

7.1 Biological Pathways

7.1 生物通路

[Researching...]
[研究中...]

7.2 Protein-Protein Interactions

7.2 蛋白质-蛋白质相互作用

[Researching...]
[研究中...]

7.3 Functional Enrichment

7.3 功能富集

[Researching...]
[研究中...]

7.4 Pathway Redundancy Assessment

7.4 通路冗余评估

[Researching...]
[研究中...]

8. Validation Evidence

8. 验证证据

8.1 Target Essentiality (DepMap)

8.1 靶点必需性（DepMap）

[Researching...]
[研究中...]

8.2 Functional Studies

8.2 功能研究

[Researching...]
[研究中...]

8.3 Animal Models

8.3 动物模型

[Researching...]
[研究中...]

8.4 Biomarker Potential

8.4 生物标志物潜力

[Researching...]
[研究中...]

9. Structural Insights

9. 结构见解

9.1 Experimental Structures (PDB)

9.1 实验结构（PDB）

[Researching...]
[研究中...]

9.2 AlphaFold Prediction

9.2 AlphaFold预测

[Researching...]
[研究中...]

9.3 Binding Pocket Analysis

9.3 结合口袋分析

[Researching...]
[研究中...]

9.4 Domain Architecture

9.4 结构域架构

[Researching...]
[研究中...]

10. Literature Landscape

10. 文献现状

10.1 Publication Metrics

10.1 出版物指标

[Researching...]
[研究中...]

10.2 Key Publications

10.2 关键出版物

[Researching...]
[研究中...]

10.3 Research Trend

10.3 研究趋势

[Researching...]
[研究中...]

11. Validation Roadmap

11. 验证路线图

11.1 Recommended Validation Experiments

11.1 推荐验证实验

[Researching...]
[研究中...]

11.2 Tool Compounds for Testing

11.2 测试用工具化合物

[Researching...]
[研究中...]

11.3 Biomarker Strategy

11.3 生物标志物策略

[Researching...]
[研究中...]

11.4 Clinical Biomarker Candidates

11.4 临床生物标志物候选

[Researching...]
[研究中...]

11.5 Disease Models to Test

11.5 待测试疾病模型

[Researching...]
[研究中...]

12. Risk Assessment

12. 风险评估

12.1 Key Risks

12.1 关键风险

[Researching...]
[研究中...]

12.2 Mitigation Strategies

12.2 缓解策略

[Researching...]
[研究中...]

12.3 Competitive Landscape

12.3 竞争格局

[Researching...]
[研究中...]

13. Completeness Checklist

13. 完整性检查清单

[To be populated post-audit...]
[审核后填充...]

14. Data Sources & Methodology

14. 数据来源与方法

[Will be populated as research progresses...]

---
[研究过程中填充...]

---

Completeness Checklist (MANDATORY)

完整性检查清单（必填）

Before finalizing, verify:
markdown
undefined
最终确定前，请验证：
markdown
undefined

13. Completeness Checklist

13. 完整性检查清单

Phase Coverage

阶段覆盖

  • Phase 0: Target disambiguation (all IDs resolved)
  • Phase 1: Disease association (OT + GWAS + gnomAD + literature)
  • Phase 2: Druggability (tractability + class + structure + probes)
  • Phase 3: Chemical matter (ChEMBL + BindingDB + PubChem + drugs)
  • Phase 4: Clinical precedent (FDA + trials + failures)
  • Phase 5: Safety (OT safety + expression + KO + ADRs + paralogs)
  • Phase 6: Pathway context (Reactome + STRING + GO)
  • Phase 7: Validation evidence (DepMap + literature + models)
  • Phase 8: Structural insights (PDB + AlphaFold + pockets + domains)
  • Phase 9: Literature (collision-aware + metrics + key papers)
  • Phase 10: Validation roadmap (score + recommendations)
  • 阶段0：靶点消歧（所有ID已解析）
  • 阶段1：疾病关联性（OT + GWAS + gnomAD + 文献）
  • 阶段2：成药性（可开发性 + 类别 + 结构 + 探针）
  • 阶段3：化学物质（ChEMBL + BindingDB + PubChem + 药物）
  • 阶段4：临床先例（FDA + 试验 + 失败项目）
  • 阶段5：安全性（OT安全 + 表达 + 敲除 + ADR + 旁系同源物）
  • 阶段6：通路背景（Reactome + STRING + GO）
  • 阶段7：验证证据（DepMap + 文献 + 模型）
  • 阶段8：结构见解（PDB + AlphaFold + 口袋 + 结构域）
  • 阶段9：文献（碰撞检测 + 指标 + 关键论文）
  • 阶段10：验证路线图（评分 + 建议）

Data Quality

数据质量

  • All scores justified with specific data
  • Evidence grades (T1-T4) assigned to key claims
  • Negative results documented (not left blank)
  • Failed tools with fallbacks documented
  • Source citations for all data points
  • 所有得分均有具体数据支撑
  • 关键结论已分配证据等级（T1-T4）
  • 阴性结果已记录（未留空）
  • 失败工具及替代方案已记录
  • 所有数据点均有来源引用

Scoring

评分

  • All 12 score components calculated
  • Total score summed correctly
  • Priority tier assigned
  • GO/NO-GO recommendation justified

---
  • 已计算所有12个评分构成部分
  • 总分计算正确
  • 已分配优先级层级
  • GO/NO-GO建议有充分依据

---

Fallback Chains

替代工具链

Primary ToolFallback 1Fallback 2If All Fail
OpenTargets_get_diseases_phenotypes_*
CTD_get_gene_diseases
PubMed searchNote in report
GTEx_get_median_gene_expression
(versioned)		GTEx (unversioned)
HPA_search_genes_by_query
Document gap
ChEMBL_get_target_activities
BindingDB_get_ligands_by_uniprot
DGIdb_get_gene_info
Note in report
gnomad_get_gene_constraints
OpenTargets_get_target_constraint_info_*
-Note as unavailable
Reactome_map_uniprot_to_pathways
OpenTargets_get_target_gene_ontology_*
-Use GO only
STRING_get_protein_interactions
intact_get_interactions
OpenTargets interactions
Note in report
ProteinsPlus_predict_binding_sites
alphafold_get_prediction
Literature pocketsNote as limited
主工具替代工具1替代工具2全部失败时
OpenTargets_get_diseases_phenotypes_*
CTD_get_gene_diseases
PubMed搜索在报告中注明
GTEx_get_median_gene_expression
（带版本）		GTEx（不带版本）
HPA_search_genes_by_query
记录数据缺口
ChEMBL_get_target_activities
BindingDB_get_ligands_by_uniprot
DGIdb_get_gene_info
在报告中注明
gnomad_get_gene_constraints
OpenTargets_get_target_constraint_info_*
-注明不可用
Reactome_map_uniprot_to_pathways
OpenTargets_get_target_gene_ontology_*
-仅使用GO数据
STRING_get_protein_interactions
intact_get_interactions
OpenTargets interactions
在报告中注明
ProteinsPlus_predict_binding_sites
alphafold_get_prediction
文献中的口袋信息注明数据有限

Modality-Specific Considerations

治疗模态特异性考量

Small Molecule Focus

小分子聚焦

  • Emphasize: binding pockets, ChEMBL compounds, Lipinski compliance
  • Key tractability: OpenTargets SM tractability bucket
  • Structure: co-crystal structures with small molecule ligands
  • Chemical matter: IC50/Ki/Kd data from ChEMBL/BindingDB
  • 重点：结合口袋、ChEMBL化合物、Lipinski规则合规性
  • 关键可开发性：OpenTargets SM可开发性分类
  • 结构：与小分子配体的共晶结构
  • 化学物质：ChEMBL/BindingDB中的IC50/Ki/Kd数据

Antibody Focus

抗体聚焦

  • Emphasize: extracellular domains, cell surface expression, glycosylation
  • Key tractability: OpenTargets AB tractability bucket
  • Structure: ectodomain structures, epitope mapping
  • Expression: surface expression in disease vs normal tissue
  • 重点：细胞外结构域、细胞表面表达、糖基化
  • 关键可开发性：OpenTargets AB可开发性分类
  • 结构：胞外域结构、表位定位
  • 表达：疾病组织与正常组织中的表面表达差异

PROTAC Focus

PROTAC聚焦

  • Emphasize: intracellular targets, surface lysines, E3 ligase proximity
  • Key tractability: OpenTargets PROTAC tractability
  • Structure: full-length structures for linker design
  • Chemical matter: known binders + E3 ligase binders
  • 重点：细胞内靶点、表面赖氨酸、E3连接酶 proximity
  • 关键可开发性：OpenTargets PROTAC可开发性
  • 结构：用于 linker 设计的全长结构
  • 化学物质：已知结合剂 + E3连接酶结合剂

Quick Reference: Verified Tool Parameters

快速参考：已验证工具参数

ToolParametersNotes
ensembl_lookup_gene
gene_id
, 
species
species="homo_sapiens" REQUIRED; response wrapped in 
{status, data, url, content_type}
OpenTargets_get_*_by_ensemblID
ensemblId
camelCase, NOT ensemblID
OpenTargets_get_publications_by_target_ensemblID
entityId
NOT ensemblId
OpenTargets_get_associated_drugs_by_target_ensemblID
ensemblId
, 
size
size is REQUIRED
OpenTargets_target_disease_evidence
efoId
, 
ensemblId
Both REQUIRED
GTEx_get_median_gene_expression
operation
, 
gencode_id
operation="median" REQUIRED
HPA_get_rna_expression_by_source
gene_name
, 
source_type
, 
source_name
ALL 3 required
PubMed_search_articles
query
, 
limit
Returns plain list, NOT {articles:[]}
UniProt_get_function_by_accession
accession
Returns list of strings
alphafold_get_prediction
qualifier
NOT uniprot_accession
drugbank_get_safety_*
query
, 
case_sensitive
, 
exact_match
, 
limit
ALL required
STRING_get_protein_interactions
protein_ids
, 
species
protein_ids is array; species=9606
Reactome_map_uniprot_to_pathways
id
NOT uniprot_id
ChEMBL_get_target_activities
target_chembl_id__exact
Note double underscore
search_clinical_trials
query_term
REQUIRED parameter
gnomad_get_gene_constraints
gene_symbol
NOT gene_id
DepMap_get_gene_dependencies
gene_symbol
NOT gene_id
BindingDB_get_ligands_by_uniprot
uniprot
, 
affinity_cutoff
affinity in nM
Pharos_get_target
gene
or 
uniprot
Both optional but need one
工具参数说明
ensembl_lookup_gene
gene_id
, 
species
必须添加
species="homo_sapiens"
；响应包裹在
{status, data, url, content_type}
中
OpenTargets_get_*_by_ensemblID
ensemblId
驼峰式，非
ensemblID
OpenTargets_get_publications_by_target_ensemblID
entityId
非
ensemblId
OpenTargets_get_associated_drugs_by_target_ensemblID
ensemblId
, 
size
size
必填
OpenTargets_target_disease_evidence
efoId
, 
ensemblId
两者均必填
GTEx_get_median_gene_expression
operation
, 
gencode_id
必须添加
operation="median"
HPA_get_rna_expression_by_source
gene_name
, 
source_type
, 
source_name
全部3个参数必填
PubMed_search_articles
query
, 
limit
返回纯列表，非
{articles:[]}
UniProt_get_function_by_accession
accession
返回字符串列表
alphafold_get_prediction
qualifier
非
uniprot_accession
drugbank_get_safety_*
query
, 
case_sensitive
, 
exact_match
, 
limit
全部必填
STRING_get_protein_interactions
protein_ids
, 
species
protein_ids
为数组；
species=9606
Reactome_map_uniprot_to_pathways
id
非
uniprot_id
ChEMBL_get_target_activities
target_chembl_id__exact
注意双下划线
search_clinical_trials
query_term
必填参数
gnomad_get_gene_constraints
gene_symbol
非
gene_id
DepMap_get_gene_dependencies
gene_symbol
非
gene_id
BindingDB_get_ligands_by_uniprot
uniprot
, 
affinity_cutoff
亲和力单位为nM
Pharos_get_target
gene
或
uniprot
两者可选但需至少提供一个

Example Execution: EGFR for NSCLC

示例执行：EGFR用于NSCLC

Phase 0 Result

阶段0结果

  • Symbol: EGFR, Ensembl: ENSG00000146648, UniProt: P00533, ChEMBL: CHEMBL203
  • 符号: EGFR, Ensembl: ENSG00000146648, UniProt: P00533, ChEMBL: CHEMBL203

Expected Scores (EGFR for NSCLC)

预期评分（EGFR用于NSCLC）

  • Disease Association: ~28/30 (strong genetic + pathway + literature)
  • Druggability: ~24/25 (kinase, many structures, abundant compounds)
  • Safety: ~14/20 (widely expressed but manageable toxicity)
  • Clinical Precedent: 15/15 (multiple approved drugs)
  • Validation Evidence: ~9/10 (extensive functional data)
  • Total: ~90/100 = Tier 1
  • 疾病关联性: ~28/30（强遗传+通路+文献证据）
  • 成药性: ~24/25（激酶家族，大量结构，丰富化合物）
  • 安全性: ~14/20（广泛表达但毒性可管理）
  • 临床先例: 15/15（多种获批药物）
  • 验证证据: ~9/10（大量功能数据）
  • 总分: ~90/100 = 层级1

Example for Novel Target (e.g., understudied kinase)

全新靶点示例（如未充分研究的激酶）

  • Disease Association: ~8/30 (limited GWAS, few publications)
  • Druggability: ~15/25 (kinase family bonus, AlphaFold structure)
  • Safety: ~12/20 (limited data, unknown KO phenotype)
  • Clinical Precedent: 0/15 (no clinical development)
  • Validation Evidence: ~2/10 (minimal functional data)
  • Total: ~37/100 = Tier 4
  • 疾病关联性: ~8/30（有限GWAS数据，少量出版物）
  • 成药性: ~15/25（激酶家族加分，AlphaFold结构）
  • 安全性: ~12/20（数据有限，未知敲除表型）
  • 临床先例: 0/15（无临床开发）
  • 验证证据: ~2/10（少量功能数据）
  • 总分: ~37/100 = 层级4