r2ascension/skill_repo/agentd-drug-discovery

At-a-Glance

• description (10-20 chars): Hypothesis foundry
• keywords: ligand-design, SAR, ADMET, docking, ranking
• measurable_outcome: Generate ≥10 candidate molecules (or requested count) with SMILES, key properties, and rationales per run, all delivered within 15 minutes.

Inputs

• target_protein, optional reference_compound, disease indication.
• constraints dict (LogP, MW, TPSA, etc.) and num_candidates.

Outputs

1. Ranked candidate list with SMILES + property scores + novelty metrics.
2. ADMET/toxicity alerts and SAR rationale per molecule.
3. Reproducibility manifest (data source versions, model checkpoints).

Workflow

1. Evidence retrieval: Mine literature + databases for known ligands and liabilities.
2. Generate candidates: Run AgentD generative step (scaffold hopping/fragment growth) aligned to constraints.
3. Score & filter: Apply Lipinski/QED/ADMET heuristics; include docking setup when requested.
4. Rank & explain: Combine efficacy, developability, novelty; summarize SAR learnings.
5. Deliver outputs: Emit JSON/CSV plus narrative recommendations; mark as in silico.

Guardrails

• Clearly state outputs are hypothetical and need wet-lab validation.
• Flag PAINS/reactive motifs automatically.
• Record data/model versions for audit trails.

References

• Detailed parameter tables and dependencies listed in README.md.

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