--- 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 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 clinical development (Phase I+) - 10: Preclinical or indirect translational precedent only - 0: No meaningful translational precedent found #### Structural data (0-20) - 20: Experimental PDB structure with co-crystal ligand, resolution < 2.5 A - 10: AlphaFold model only, or PDB structure without ligand - 0: No usable structural information available ### Safety penalties (applied after scoring) - Essential gene evidence present (DepMap): -10 - Broad systemic pathway involvement (TGF-beta, Wnt, Notch): -5 to -20 depending on severity - Known toxicity or clinical safety signal from literature/trials: -10 If a target has strong disease relevance but also major systemic safety liability, prefer CONDITIONAL_GO over GO. Safety penalties reduce the final score but do not change sub-scores. A target can score 80 on evidence but drop to 65 after safety adjustment. Safety is treated as a post-hoc penalty rather than a scoring dimension to ensure that strong biological evidence is not masked by safety concerns, but explicitly adjusted. ### Decision tiers | Adjusted score | Decision | Meaning | |----------------|----------|---------| | 75-100 | GO | Strong evidence across multiple dimensions | | 50-74 | CONDITIONAL_GO | Proceed with explicit risk mitigation plan | | 25-49 | REVIEW | Insufficient evidence; needs more data | | 0-24 | NO_GO | Target lacks fundamental validation | Thresholds are calibrated to reflect typical target progression stages in early drug discovery, where strong multi-dimensional evidence (>=75) is required for full commitment. ### Evidence grading Every piece of evidence is tagged with a confidence tier: Evidence tiers guide confidence weighting and highlight where decisions rely on weaker or indirect evidence, enabling domain experts to focus review effort. | Tier | Meaning | Example | |------|---------|---------| | T1 | Experimentally validated | Clinical trial data, GWAS with p < 5e-8 | | T2 | Computational + literature supported | Known drug-target interaction with published SAR | | T3 | Computationally predicted only | Docking score, ML prediction | | T4 | Inferred or indirect | Pathway membership, guilt-by-association | ## Safety Rules - **This skill does not make clinical recommendations.** Output is for research planning only. - **Missing data is not zero evidence.** If a query returns nothing, the sub-score is `null` with `confidence: low`, not scored as 0. - **Evidence conflicts must be surfaced.** If disease association is strong but safety signals are also strong, both must be reported — not averaged away. - **No hallucinated evidence.** Every evidence object cites a specific database and retrieval date. If an API fails, the skill reports the failure, not a guess. - **Human override is expected.** The GO/NO-GO decision is a recommendation. Domain experts should review the evidence trail and may override. ## Agent Boundary The agent (LLM) dispatches and explains. The skill (Python) executes. The agent must NOT override scoring thresholds, invent gene-drug associations, skip safety warnings, or claim that a NO_GO target is worth pursuing. The skill does not replace wet-lab validation, medicinal chemistry review, or clinical judgement.