---
name: AgentD Drug Discovery
slug: agentd-drug-discovery
category: Data
description: AgentD Drug Discovery mines literature and databases for known ligands, generates candidate molecules, and ranks them with SAR and ADMET annotations for early drug discovery tasks. It delivers SMILES, property scores, and reproducibility manifests for in silico hypothesis generation.
github: "https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/agentd-drug-discovery"
language: Python
stars: 2944
forks: 410
install: "npx degit https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/agentd-drug-discovery ~/.claude/skills/agentd-drug-discovery"
installs_to: ~/.claude/skills/agentd-drug-discovery
source_path: skills/agentd-drug-discovery/SKILL.md
collection_size: 25
category_size: 668
collection_url: "https://dirskills.com/collections/FreedomIntelligence/OpenClaw-Medical-Skills"
added: 2026-08-17T07:09:39.145Z
last_synced: 2026-08-17T07:09:39.145Z
canonical_url: "https://dirskills.com/skills/agentd-drug-discovery"
---

# AgentD Drug Discovery

AgentD Drug Discovery mines literature and databases for known ligands, generates candidate molecules, and ranks them with SAR and ADMET annotations for early drug discovery tasks. It delivers SMILES, property scores, and reproducibility manifests for in silico hypothesis generation.

**Install:**

```bash
npx degit https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/agentd-drug-discovery ~/.claude/skills/agentd-drug-discovery
```

## README

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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`.


<!-- AUTHOR_SIGNATURE: 9a7f3c2e-MD-BABU-MIA-2026-MSSM-SECURE -->