drug-target-validation

name	drug-target-validation
description	Comprehensive computational validation of drug targets for due diligence. Evaluates targets across multiple dimensions — disease association, druggability, clinical precedent, safety, competitive landscape. Produces a quantitative Target Validation Score with GO/NO-GO recommendation. Use when users ask about target validation, druggability, or "is X a good drug target for Y?"
user-invocable	false

Drug Target Validation

Validate drug target hypotheses using Gosset data and web research. Produces a quantitative Target Validation Score (0-100) with GO/NO-GO recommendation.

Data Sources

Gosset MCP Tools

• find_drugs(query, limit, effort) — Find drugs targeting this target (clinical precedent)
• find_companies(query, limit) — Find companies pursuing this target
• find_trials(query, limit) — Find clinical trials for this target
• find_deals(query, limit) — Find deal activity around this target
• gosset_agent(prompt) — Deep research on target biology, safety, landscape

Open Targets Platform API (Free, no key)

import requests

# Target-disease associations
r = requests.post("https://api.platform.opentargets.org/api/v4/graphql", json={
    "query": """query($ensemblId: String!) {
        target(ensemblId: $ensemblId) {
            id
            approvedSymbol
            approvedName
            tractability { label modality value }
            safetyLiabilities { event biosample direction studies { name } }
        }
    }""",
    "variables": {"ensemblId": "ENSG00000169083"}
})

# Disease associations for a target
r = requests.post("https://api.platform.opentargets.org/api/v4/graphql", json={
    "query": """query($ensemblId: String!, $index: Int!, $size: Int!) {
        target(ensemblId: $ensemblId) {
            associatedDiseases(page: {index: $index, size: $size}) {
                rows { disease { id name } score datatypeScores { id score } }
            }
        }
    }""",
    "variables": {"ensemblId": "ENSG00000169083", "index": 0, "size": 25}
})

# Known drugs for a target
r = requests.post("https://api.platform.opentargets.org/api/v4/graphql", json={
    "query": """query($ensemblId: String!, $size: Int!) {
        target(ensemblId: $ensemblId) {
            knownDrugs(size: $size) {
                rows { drug { id name } phase mechanismOfAction status }
            }
        }
    }""",
    "variables": {"ensemblId": "ENSG00000169083", "size": 25}
})

ChEMBL API (Free, no key)

# Search targets
r = requests.get("https://www.ebi.ac.uk/chembl/api/data/target/search.json", params={
    "q": "EGFR", "limit": 5
})

# Get bioactivities for a target
r = requests.get("https://www.ebi.ac.uk/chembl/api/data/activity.json", params={
    "target_chembl_id": "CHEMBL203", "limit": 100
})

UniProt API (Free, no key)

# Protein function, subcellular location, structure cross-refs
r = requests.get("https://rest.uniprot.org/uniprotkb/P00533.json")

PubMed / Entrez (Free, via BioPython)

from Bio import Entrez
Entrez.email = "[e-mail]"
handle = Entrez.esearch(db="pubmed", term="EGFR AND lung cancer AND target validation", retmax=50)

openFDA API (Free)

# Adverse events for drugs hitting this target
r = requests.get("https://api.fda.gov/drug/event.json", params={
    "search": "patient.drug.medicinalproduct:erlotinib", "limit": 10
})

# Drug labeling (warnings, boxed warnings)
r = requests.get("https://api.fda.gov/drug/label.json", params={
    "search": 'openfda.generic_name:"erlotinib"', "limit": 1
})

gnomAD (Free, via web search)

• Genetic constraint scores (pLI, LOEUF) — indicates target essentiality
• Loss-of-function intolerance as safety signal

GWAS Catalog (Free API)

r = requests.get("https://www.ebi.ac.uk/gwas/rest/api/singleNucleotidePolymorphisms/search/findByGene", params={
    "geneName": "EGFR"
})

Validation Dimensions (10)

1. Target Disambiguation

Resolve gene symbol, protein name, UniProt ID. Confirm the exact molecular target.

2. Disease Association (0-10)

gosset_agent(prompt="What is the genetic and biological evidence linking [target] to [disease]?")

• Genetic associations (GWAS, Mendelian)
• Expression data (disease vs. normal)
• Animal model evidence

3. Druggability Assessment (0-10)

• Target class (kinase, GPCR, ion channel, etc.)
• Structural information (crystal structures, binding pockets)
• Small molecule vs. biologic tractability

4. Clinical Precedent (0-10)

find_drugs(query="[target] inhibitor", limit=50, effort="high")
find_trials(query="[target] [disease]", limit=30)

• Approved drugs hitting this target
• Clinical-stage assets and their results
• Failed programs and reasons for failure

5. Competitive Landscape (0-10)

find_drugs(query="[target]", limit=100, effort="high")
find_companies(query="[target] developers", limit=30)

• Number and phase distribution of competitors
• Differentiation opportunities
• Freedom to operate

6. Safety Assessment (0-10)

• Known on-target toxicities
• Expression in normal tissues
• Class-effect safety signals from clinical data
• Knockout phenotype data

7. Deal Activity (0-10)

find_deals(query="[target] licensing", limit=20)

• Recent deals validating the target
• Deal values as market signal
• Partnership patterns

8. Literature Evidence (0-10)

• Publication volume and trajectory
• Key opinion leader activity
• Conference presentation trends

9. Pathway Context (0-10)

• Pathway position and redundancy risk
• Biomarker availability for patient selection
• Combination rationale

10. Commercial Potential (0-10)

• Patient population size
• Current standard of care limitations
• Unmet medical need severity
• Pricing/reimbursement precedent

Scoring

Target Validation Score = weighted sum across dimensions:

Dimension	Weight
Disease Association	15%
Druggability	10%
Clinical Precedent	15%
Competitive Landscape	10%
Safety	15%
Deal Activity	5%
Literature Evidence	5%
Pathway Context	10%
Commercial Potential	15%

GO/NO-GO Thresholds

• 75-100: Strong GO — compelling target with strong validation
• 50-74: Conditional GO — promising but gaps exist
• 25-49: Caution — significant risks, needs more validation
• 0-24: NO-GO — insufficient evidence or critical red flags

Output Format

Target Validation Report: [Target] for [Disease]

1. Executive Summary — Score, recommendation, key findings
2. Dimension Scores — Table with score, evidence grade (T1-T4), and rationale per dimension
3. Competitive Landscape — Pipeline map of assets targeting this target
4. Risk Assessment — Critical risks and mitigation strategies
5. Recommendation — GO/NO-GO with conditions and next steps