/agentd-drug-discovery

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--- name: agentd-drug-discovery description: Use the AgentD workflow to mine evidence, design molecules, and rank candidates with SAR plus ADMET annotations for early drug discovery tasks. allowed-tools:

• read_file
• run_shell_command

---

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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