How do I install the Agentd Drug Discovery skill?

Run npx skillmds@latest add fridrichmethod/agentd-drug-discovery in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Agentd Drug Discovery safe to use?

This skill has not completed SkillMD's automated safety review yet. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Agentd Drug Discovery?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Agentd Drug Discovery free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Agentd Drug Discovery?

FridrichMethod (@fridrichmethod) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Agentd Drug Discovery by fridrichmethod

Name: Agentd Drug Discovery
Author: FridrichMethod

File contents

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

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

Workflow

Evidence retrieval: Mine literature + databases for known ligands and liabilities.
Generate candidates: Run AgentD generative step (scaffold hopping/fragment growth) aligned to constraints.
Score & filter: Apply Lipinski/QED/ADMET heuristics; include docking setup when requested.
Rank & explain: Combine efficacy, developability, novelty; summarize SAR learnings.
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.

---name: agentd-drug-discoverydescription: Use the AgentD workflow to mine evidence, design molecules, and rank candidates with SAR plus ADMET annotations for early drug discovery tasks.---## 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`.## Outputs1. 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).## Workflow1. **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`.

Frequently asked questions

Agentd Drug Discovery