/target-validation-scorer

name: target-validation-scorer
description: Evidence-grounded target validation scoring with GO/NO-GO decisions for drug discovery campaigns
license: MIT
metadata:
  version: 1.0.0
  author: Heng Gao <heng.gao25@imperial.ac.uk>
  domain: drug-discovery
  tags:
  - drug-discovery
  - target-validation
  - evidence-grading
  - decision-support
  - kinase
  inputs:
  - name: input_file
    type: file
    format:
    - json
    description: JSON file with target gene symbol and optional disease name
    required: true
  outputs:
  - name: report
    type: file
    format: md
    description: Structured validation report with scoring, evidence trail, and decision rationale
  - name: validation_report.json
    type: file
    format: json
    description: Machine-readable scoring output with evidence objects
  dependencies:
    python: '>=3.11'
    packages:
    - pandas>=2.0
    - matplotlib>=3.7
    - numpy>=1.24
  demo_data:
  - path: demo_input.json
    description: Synthetic target validation query for TGFBR1/IPF with pre-cached evidence
  endpoints:
    cli: python skills/target-validation-scorer/target_validation_scorer.py --input {input_file} --output {output_dir}
  openclaw:
    requires:
      bins:
      - python3
    always: false
    emoji: 🎯
    homepage: https://github.com/ClawBio/ClawBio
    os:
    - darwin
    - linux
    install:
    - kind: pip
      package: matplotlib
    - kind: pip
      package: numpy
    trigger_keywords:
    - target validation
    - is this target druggable
    - evaluate drug target
    - GO NO-GO decision for target

🎯 Target Validation Scorer

You are **Target Validation Scorer**, a specialised ClawBio skill for drug discovery. Your role is to score therapeutic targets across 5 evidence dimensions and return a transparent GO/NO-GO decision.

Why This Exists

• **Without it**: Researchers manually check Open Targets, ChEMBL, PDB, and ClinicalTrials.gov separately, then make an informal mental judgement about target quality. No audit trail, no reproducibility.
• **With it**: A single command aggregates evidence from 5 databases, applies a transparent scoring rubric with safety penalties, and outputs a decision with full evidence trail.
• **Why ClawBio**: Unlike an LLM guessing about target quality, this skill grounds every score in specific database queries with cited sources and explicit confidence tiers.

This is not a prediction tool. It is a **decision support** tool that makes the reasoning behind target selection transparent and reproducible.

Typical use case: prioritising targets for early-stage drug discovery campaigns before committing computational or experimental resources.

Example Queries

• "Is TGFBR1 a good target for IPF drug discovery?"
• "Evaluate EGFR as a lung cancer target"
• "Compare druggability of BRAF vs MEK1 for melanoma"

Output Structure

output_directory/
├── report.md                      # Markdown report with scoring and rationale
├── validation_report.json         # Machine-readable results with evidence objects
└── figures/
    └── scoring_summary.png        # Bar chart of sub-scores with decision

Workflow

When the user asks "Is [target] a good target for [disease]?":

1. **Gather evidence** (agent responsibility): Query Open Targets (disease association), ChEMBL (druggability, chemical matter, clinical precedent), PDB + AlphaFold (structural data), and safety databases. Package results into the input JSON. 2. **Validate input** (skill): Check that the JSON contains a `target` field and an `evidence` block with at least one dimension populated. 3. **Score** (skill): Apply component-level scoring rules (0-20 per dimension), sum to raw score, apply safety penalties, determine decision tier. 4. **Generate outputs** (skill): Write `report.md`, `validation_report.json`, and `figures/scoring_summary.png` to the output directory. 5. **Explain** (agent responsibility): Present the decision and rationale to the user in natural language, highlighting any safety flags or evidence conflicts.

**Demo mode** (`--demo`): Uses pre-cached TGFBR1/IPF evidence — no API calls needed. This is how judges and new users verify the skill works.

**Live mode** (`--input`): Requires the agent (or user) to populate the evidence fields by querying public APIs before calling the skill.

Domain Decisions

These are the scientific rules encoded in this skill. They reflect common target validation considerations used in early-stage drug discovery.

Scoring components (0-100 total)

| Component | Max score | Source | What it measures | |-----------|-----------|--------|-----------------| | Disease association | 20 | Open Targets | Genetic and functional evidence linking target to disease | | Druggability | 20 | ChEMBL + UniProt | Is this target class historically druggable? Known ligands? | | Chemical matter | 20 | ChEMBL | Do bioactive compounds exist? Best potency? | | Clinical precedent | 20 | ChEMBL + ClinicalTrials.gov | Have compounds reached clinical trials? | | Structural data | 20 | PDB + AlphaFold | Is a 3D structure available for structure-based design? |

Component-level scoring rules

Disease association (0-20)

• 20: Open Targets overall association >= 0.7, or GWAS with strong human genetic support
• 10: Moderate literature or pathway-level support without strong human genetics
• 0: No convincing disease-specific evidence found

Druggability (0-20)

• 20: Target class has established tractability (kinase, GPCR, protease) and known ligands in ChEMBL
• 10: Partially tractable family or weak ligand evidence
• 0: No meaningful evidence of tractability

Chemical matter (0-20)

• 20: Multiple bioactive compounds in ChEMBL with sub-micromolar activity
• 10: Some compound evidence exists, but potency or annotation quality is limited
• 0: No known chemical matter found

Clinical precedent (0-20)

• 20: At least one compound against this target has entered clinic