/agentd-drug-discovery
<!--
$ npx -y skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill agentd-drug-discovery --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
- Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
- You can call itInvoke it directly when you want it.
- Slash command
/agentd-drug-discovery
Context preview
The summary Claude sees to decide when to auto-load this skill.
<!--
SKILL.md
agentd-drug-discovery.SKILL.md<!--
COPYRIGHT NOTICE
This file is part of the "Universal Biomedical Skills" project.
Copyright (c) 2026 MD BABU MIA, PhD <md.babu.mia@mssm.edu>
All Rights Reserved.
#
This code is proprietary and confidential.
Unauthorized copying of this file, via any medium is strictly prohibited.
#
Provenance: Authenticated by MD BABU MIA
-->
--- 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 -->
Read more
<!--
COPYRIGHT NOTICE
This file is part of the "Universal Biomedical Skills" project.
Copyright (c) 2026 MD BABU MIA, PhD <md.babu.mia@mssm.edu>
All Rights Reserved.
#
This code is proprietary and confidential.
Unauthorized copying of this file, via any medium is strictly prohibited.
#
Provenance: Authenticated by MD BABU MIA
-->
--- 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 -->
The largest open-source medical AI skill library for OpenClaw.
Other skills on openclaw-medical-skills.
adaptyv
Cloud laboratory platform for automated protein testing and validation. Use when designing proteins and needing experimental validation including binding…
adhd-daily-planner
Time-blind friendly planning, executive function support, and daily structure for ADHD brains. Specializes in realistic time estimation, dopamine-aware task…
aeon
This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection,…
agent-browser
Browse the web for any task — research topics, read articles, interact with web apps, fill forms, take screenshots, extract data, and test web pages. Use…
ai-analyzer
AI驱动的综合健康分析系统,整合多维度健康数据、识别异常模式、预测健康风险、提供个性化建议。支持智能问答和AI健康报告生成。

