tooluniverse-binder-discovery

What this skill does

# 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.
- **Intrinsically disordered regions**: essentially no small molecule approach. Redirect to peptide or degrader strategies.
- **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_name="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
2. MyGene_query_genes(q=gene_symbol, species="human") -> Ensembl gene ID
3. ChEMBL_search_targets(query=target_name, organism="Homo sapiens") -> ChEMBL target ID
4. GtoPdb_search_targets(query=target_name) -> GtoPdb ID (if GPCR/channel/enzyme)
```

### 1.2 Druggability Assessment

Use multi-source triangulation:
- `OpenTargets_get_target_tractability_by_ensemblID(ensemblId)` - tractability bucket
- `DGIdb_get_gene_druggability(genes=[gene_symbol])` - druggability categories
- `OpenTargets_get_target_classes_by_ensemblID(ensemblId)` - target class
- For GPCRs: `GPCRdb_get_protein` + `GPCRdb_get_ligands` + `GPCRdb_get_structures`
- For antibody landscape: `TheraSAbDab_search_by_target(target=target_name)`

**Decision Point**: If no tractability data and binding site reasoning suggests PPI or disordered region, explicitly warn the user before proceeding.

### 1.3 Binding Site Analysis

- `ChEMBL_search_binding_sites(target_chembl_id)`
- `get_binding_affinity_by_pdb_id(pdb_id)` for co-crystallized ligands
- `InterPro_get_protein_domains(accession)` for domain architecture

### 1.4 Structure Prediction (NVIDIA NIM)

Requires `NVIDIA_API_KEY`. Two options:
- **AlphaFold2**: `NvidiaNIM_alphafold2(sequence, algorithm="mmseqs2")` - high accuracy, 5-15 min
- **ESMFold**: `ESMFold_predict_structure(sequence)` - fast (~30s), max 1024 AA

pLDDT guidance: >=90 very high confidence, 70-90 confident, <70 use with caution. Low pLDDT in the putative binding region undermines docking reliability.

---

## Phase 2: Known Ligand Mining

Priority order for bioactivity data:
1. `ChEMBL_get_target_activities` - curated, SAR-ready
2. `BindingDB_get_ligands_by_uniprot` - direct Ki/Kd with literature links
3. `GtoPdb_search_ligands` - pharmacology focus (GPCRs, channels)
4. `PubChem_search_assays_by_target_gene` - HTS screens, novel scaffolds
5. `OpenTargets_get_chemical_probes_by_target_ensemblID` - validated probes

Key steps:
1. Filter to IC50/Ki/Kd < 10 uM; retrieve molecule details for top actives
2. Identify chemical probes and approved drugs
3. Analyze SAR: common scaffolds, key modifications
4. Check off-target selectivity: `BindingDB_get_targets_by_compound`

---

## Phase 3: Structure Analysis

Tools:
- `PDB_search_similar_structures(query=uniprot, type="sequence")` - find PDB entries
- `get_protein_metadata_by_pdb_id(pdb_id)` - resolution, method
- `get_binding_affinity_by_pdb_id(pdb_id)` - co-crystal ligand affinities
- `get_ligand_smiles_by_chem_comp_id(chem_comp_id)` - ligand SMILES from PDB
- `EMDB_search_structures(query)` - cryo-EM structures (prefer for GPCRs, ion channels)
- `alphafold_get_prediction(qualifier)` - AlphaFold DB fallback

### Phase 3.5: Docking Validation (NVIDIA NIM)

If PDB + SDF available: use `get_diffdock_info(protein=PDB, ligand=SDF, num_poses=10)`.
If only sequence + SMIL