tooluniverse-binder-discovery

What the source does

• FilesystemNone observedNo reads or writes in the source
• NetworkNone observedNo outbound calls in the source
• ShellNone observedNo commands run in the source
• DependenciesPinnedNothing floats
• SecretsNone foundNo keys in source

Five audit layers

Gates applied: no behavioural pass.

Audit 0.4.1 · source sha 43be0fac747b · 29 Oct 2026Report an issue or request a re-scan

• Publishermims-harvard GitHub organizationOwner of the repository this skill is read from
• Source foldermims-harvard/tooluniverse/skills/tooluniverse-binder-discoveryThe exact folder we audited
• LicenceApache-2.0As the repository states it
• Commit pinned for this verdict43be0fac747bA new audit runs when the source changes
• Repository statusNot archivedLast push 1 day ago
• Sandboxed runNot runSkills are read, not executed; layer L4 is skipped

Its description: Discover novel small-molecule binders for protein targets using structure-based and ligand-based screening. Covers druggability assessment, known-ligand mining (ChEMBL, BindingDB), similarity expansion, ADMET filtering,…

---
name: tooluniverse-binder-discovery
description: Discover novel small-molecule binders for protein targets using structure-based and ligand-based screening. Covers druggability assessment, known-ligand mining (ChEMBL, BindingDB), similarity expansion, ADMET filtering, and synthesis feasibility. Use for hit identification, virtual screening, target-to-compounds workflows, and lead-finding before commit-to-medchem.
disable-model-invocation: true
---

# Small Molecule Binder Discovery Strategy

Systematic discovery of novel small molecule binders using 60+ ToolUniverse tools across druggability assessment, known ligand mining, similarity expansion, ADMET filtering, and synthesis feasibility.

**LOOK UP DON'T GUESS** - Always retrieve actual data from tools before drawing conclusions. Do not assume druggability, binding sites, or compound properties based on target class alone.

**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 and 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 with rationale
7. **English-first queries** - Always use English terms in tool calls. Respond in the user's language

---

## Binding Site Reasoning (Start Here)

Before any tool call, reason about the target's structural biology:

**Is the binding site a well-defined pocket (small molecule accessible) or a flat protein-protein interface (needs peptide/macrocycle)?** This determines your screening strategy.

- **Enzymes with active sites** (proteases, kinases, ATPases): deep, well-defined pockets. Classic small molecule territory. Prioritize co-crystal structure search and known inhibitor scaffold analysis.
- **GPCRs and ion channels**: transmembrane pockets. Structure often available; start with GPCRdb and GtoPdb for known pharmacology.
- **Nuclear receptors**: deep hydrophobic pockets. Excellent small molecule tractability; ligand-based methods are well-powered.
- **Protein-protein interfaces**: flat, large contact surface. Small molecules rarely compete effectively unless there is a "hot spot" cavity. Check whether any allosteric pockets exist before committing to small molecule strategy. Warn the user if no pocket is found. If the target is a known molecular-glue-degrader neosubstrate (or the goal is to induce a new PPI rather than block an existing pocket), check `MolGlueDB_search_compounds`/`MolGlueDB_get_compound` for known glue chemotypes before declaring the target undruggable by small molecules.
- **Intrinsically disordered regions**: essentially no direct small molecule approach. Redirect to peptide strategies, or check `MolGlueDB_search_compounds` in case the target is a documented molecular-glue neosubstrate.
- **Scaffolding / adaptor proteins**: assess co-crystal structures for unexpected pockets before declaring undruggable.

Use this reasoning to select phases and warn the user about challenges before executing a full workflow.

---

## Critical Workflow Requirements

### 1. Report-First Approach (MANDATORY)

**DO NOT** show search process or tool outputs to the user. Instead:

1. **Create the report file FIRST** - Before any data collection:
   - File name: `[TARGET]_binder_discovery_report.md`
   - Initialize with all section headers from the template (see REPORT_TEMPLATE.md)
   - Add placeholder text: `[Researching...]` in each section

2. **Progressively update the report** - As you gather data, update each section immediately.

3. **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:

Example: `*Source: ChEMBL via ChEMBL_get_target_activities (CHEMBL203)*`

---

## Workflow Overview

Phases in order:
- **Phase 0**: Tool verification (check parameter names with `get_tool_info`)
- **Phase 1**: Target validation — resolve IDs, assess druggability, identify binding sites, predict structure if needed
- **Phase 2**: Known ligand mining — ChEMBL, BindingDB, GtoPdb, PubChem BioAssay, chemical probes; SAR analysis
- **Phase 3**: Structure analysis — PDB co-crystals, EMDB (membrane targets), binding pocket characterization
- **Phase 3.5**: Docking validation — dock reference inhibitor to validate pocket geometry
- **Phase 4**: Compound expansion — similarity/substructure search (seeds: 3-5 diverse actives) + de novo generation
- **Phase 5**: ADMET filtering — physicochemical, bioavailability, toxicity, CYP, structural alerts
- **Phase 6**: Candidate docking and prioritization — score and rank top 20
- **Phase 6.5**: Literature evidence — PubMed, EuropePMC, OpenAlex
- **Phase 7**: Report synthesis and delivery

---

## Phase 0: Tool Verification

**CRITICAL**: Verify tool parameters before calling unfamiliar tools.

```python
tool_info = tu.tools.get_tool_info(tool_names="ChEMBL_get_target_activities")
```

Common parameter corrections (verify with `get_tool_info` if uncertain):
- `OpenTargets_*`: `ensemblId` (camelCase); `ADMETAI_*`: `smiles` must be a list
- `NvidiaNIM_alphafold2` *(requires NVIDIA_API_KEY env var; free key at build.nvidia.com)*: `sequence` not `seq`; `NvidiaNIM_genmol` *(requires NVIDIA_API_KEY env var; free key at build.nvidia.com)*: SMILES must contain `[*{min-max}]`
- `NvidiaNIM_boltz2` *(requires NVIDIA_API_KEY env var; free key at build.nvidia.com)*: `polymers=[{"molecule_type": "protein", "sequence": "..."}]`

---

## Phase 1: Target Validation

### 1.1 Identifier Resolution

Resolve all IDs upfront and store for downstream queries:

```
1. UniProt_search(query=target_name, organism="human") -> UniProt accession

Excerpt, first 6,000 characters of SKILL.md, verbatim from the audited commit. It is text from someone else’s repository, quoted so you can judge it before your agent reads it.

git clone https://github.com/mims-harvard/tooluniverse
git -C tooluniverse checkout 43be0fac747b

Works in

1. 2026-10-29
   ✓SAFE B 89 / 100

   first audit · source 43be0fac747b

Questions about tooluniverse-binder-discovery

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