AD
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数据与复现
TO
tooluniverse-binder-discovery
维护者 FreedomIntelligence · 最近更新 2026年4月1日
Discover novel small molecule binders for protein targets using structure-based and ligand-based approaches. Creates actionable reports with candidate compounds, ADMET profiles, and synthesis feasibility. Use when users ask to find small molecules for a target, identify novel binders, perform virtual screening, or need hit-to-lead compound identification.
原始来源
FreedomIntelligence/OpenClaw-Medical-Skills
https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/tooluniverse-binder-discovery
- 维护者
- FreedomIntelligence
- 许可
- MIT
- 最近更新
- 2026年4月1日
技能摘要
来自 SKILL.md 的关键信息
核心说明
- Systematic discovery of novel small molecule binders ，使用 60+ ToolUniverse tools across druggability assessment，known ligand mining，similarity expansion，ADMET filtering，、 synthesis feasibility。
- KEY PRINCIPLES：1. Report-first approach - Create report file FIRST，then populate progressively 2. Target validation FIRST - Confirm druggability before compound searching 3. Multi-strategy approach - Combine structure-based 、 ligand-based methods 4. ADMET-aware filtering - Eliminate poor compounds early 5. Evidence grading - Grade candidates by supporting evidence 6. Actionable output - Provide prioritized candidates ，支持 rationale 7. English-first queries - Always use English terms in tool calls，even if user writes in another language. Only try original-language terms as fallback. Respond in user's language。
- tool_info = tu.tools.get_tool_info(tool_name="ChEMBL_get_target_activities")。
原始文档
SKILL.md 摘录
1. Report-First Approach (MANDATORY)
DO NOT show search process or tool outputs to the user. Instead:
-
Create the report file FIRST - Before any data collection:
- File name:
[TARGET]_binder_discovery_report.md - Initialize with all section headers from the template
- Add placeholder text:
[Researching...]in each section
- File name:
-
Progressively update the report - As you gather data:
- Update each section with findings immediately
- The user sees the report growing, not the search process
-
Output separate data files:
[TARGET]_candidate_compounds.csv- Prioritized compounds with SMILES, scores[TARGET]_bibliography.json- Literature references (optional)
2. Citation Requirements (MANDATORY)
Every piece of information MUST include its source:
## 3.2 Known Inhibitors
| Compound | ChEMBL ID | IC50 (nM) | Selectivity | Source |
|----------|-----------|-----------|-------------|--------|
| Imatinib | CHEMBL941 | 38 | ABL-selective | ChEMBL |
| Dasatinib | CHEMBL1421 | 0.5 | Multi-kinase | ChEMBL |
*Source: ChEMBL via `ChEMBL_get_target_activities` (CHEMBL1862)*
适用场景
- 适合在users ask to find small molecules ，用于 target，identify novel binders，perform virtual screening，或 need hit-to-lead compound identification时使用。
不适用场景
- Do not rely on this catalog entry alone ，用于 installation 或 maintenance details。
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