target-validation-scorer

name	target-validation-scorer
version	1.0.0
author	Heng Gao <[e-mail]>
domain	drug-discovery
description	Evidence-grounded target validation scoring with GO/NO-GO decisions for drug discovery campaigns
license	MIT
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"]}
tags	["drug-discovery","target-validation","evidence-grading","decision-support","kinase"]
metadata	{"openclaw":{"requires":{"bins":["python3"],"env":[],"config":[]},"always":false,"emoji":"🎯","homepage":"https://github.com/ClawBio/ClawBio","os":["darwin","linux"],"install":[{"kind":"pip","package":"matplotlib","bins":[]},{"kind":"pip","package":"numpy","bins":[]}],"trigger_keywords":["target validation","is this target druggable","evaluate drug target","GO NO-GO decision for target"]}}
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}"}

🎯 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.