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title	task	lineage_type	upstream_source	upstream_sha	imported_at	prompt_class	upstream_changes	author	validated
Tools Reference for Small Molecule Binder Discovery		import	https://github.com/mims-harvard/ToolUniverse/blob/e2520a96/skills/tooluniverse-binder-discovery/TOOLS_REFERENCE.md	e2520a96	2026-06-26	prompt	accepted	upstream	false

Tools Reference for Small Molecule Binder Discovery

Complete tool reference with verified parameters and fallback chains.

Phase 1: Target Validation Tools

UniProt_search

Purpose: Resolve protein name to UniProt accession

result = tu.tools.UniProt_search(
    query="EGFR human",
    organism="human",
    limit=10
)
# Returns: list of UniProt entries with accession, protein name, gene name

MyGene_query_genes

Purpose: Get Ensembl and NCBI gene IDs

result = tu.tools.MyGene_query_genes(
    q="EGFR",
    species="human",
    fields="ensembl.gene,symbol,name"
)
# Returns: gene info with ensembl.gene, symbol, name

ChEMBL_search_targets

Purpose: Get ChEMBL target ID

result = tu.tools.ChEMBL_search_targets(
    query="EGFR",
    organism="Homo sapiens",
    limit=10
)
# Returns: targets with target_chembl_id, pref_name, target_type

OpenTargets_get_target_tractability_by_ensemblID

Purpose: Assess small molecule tractability

result = tu.tools.OpenTargets_get_target_tractability_by_ensemblID(
    ensemblId="ENSG00000146648"  # Note: camelCase
)
# Returns: tractability assessments by modality (small molecule, antibody, etc.)

⚠️ Parameter: Use ensemblId (camelCase), NOT ensembl_id

DGIdb_get_gene_druggability

Purpose: Get druggability categories

result = tu.tools.DGIdb_get_gene_druggability(
    genes=["EGFR"]  # List of gene symbols
)
# Returns: druggability info, drug count, categories

ChEMBL_search_binding_sites

Purpose: Find known binding sites

result = tu.tools.ChEMBL_search_binding_sites(
    target_chembl_id="CHEMBL203"
)
# Returns: binding site names, types

InterPro_get_protein_domains

Purpose: Get domain architecture

result = tu.tools.InterPro_get_protein_domains(
    accession="P00533"  # UniProt accession
)
# Returns: domains, families, sites with positions

Thera-SAbDab Tools (NEW - Therapeutic Antibody Landscape)

Check therapeutic antibody competition for your target.

TheraSAbDab_search_by_target

Purpose: Find antibodies targeting specific antigen

result = tu.tools.TheraSAbDab_search_by_target(
    target="PD-1"  # Target antigen name
)
# Returns: list of therapeutic antibodies, phase, format, PDB IDs

TheraSAbDab_search_therapeutics

Purpose: Search antibodies by name or keyword

result = tu.tools.TheraSAbDab_search_therapeutics(
    query="pembrolizumab"  # Antibody name or target
)
# Returns: matching therapeutics with target, phase, format

TheraSAbDab_get_all_therapeutics

Purpose: Get summary of all therapeutic antibodies

result = tu.tools.TheraSAbDab_get_all_therapeutics()
# Returns: total count, distribution by phase, distribution by format

Why Use Thera-SAbDab:

Competitive intelligence: See what antibodies already target your protein
Target validation: Approved antibodies = validated target
Structural data: Links to PDB structures of antibody-target complexes
Strategic differentiation: Identify where small molecules offer advantages

Example Competitive Analysis:

def assess_antibody_competition(tu, target_name):
    """Assess therapeutic antibody competitive landscape."""
    
    # Search by target
    results = tu.tools.TheraSAbDab_search_by_target(target=target_name)
    
    if results.get('status') == 'success':
        antibodies = results['data'].get('therapeutics', [])
        
        # Count by phase
        phases = {}
        for ab in antibodies:
            phase = ab.get('phase', 'Unknown')
            phases[phase] = phases.get(phase, 0) + 1
        
        return {
            'total': len(antibodies),
            'by_phase': phases,
            'has_approved': 'Approved' in phases,
            'top_antibodies': antibodies[:5]
        }
    return None

GPCRdb Tools (for GPCR Targets)

~35% of approved drugs target GPCRs. Use GPCRdb for specialized GPCR data.

GPCRdb_get_protein

Purpose: Get GPCR protein information

result = tu.tools.GPCRdb_get_protein(
    operation="get_protein",
    protein="adrb2_human"  # Entry name format: {gene}_human
)
# Returns: GPCR family, class, species, sequence info

GPCRdb_list_proteins

Purpose: List GPCR families or proteins in a family

# List all GPCR families
families = tu.tools.GPCRdb_list_proteins(
    operation="list_proteins"
)

# List proteins in specific family
proteins = tu.tools.GPCRdb_list_proteins(
    operation="list_proteins",
    family="001"  # Class A
)

GPCRdb_get_structures

Purpose: Get GPCR structures with receptor state (active/inactive)

result = tu.tools.GPCRdb_get_structures(
    operation="get_structures",
    protein="adrb2_human",
    state="active"  # Optional: "active", "inactive", "intermediate"
)
# Returns: PDB IDs with receptor state, resolution, ligand info

GPCRdb_get_ligands

Purpose: Get known GPCR ligands

result = tu.tools.GPCRdb_get_ligands(
    operation="get_ligands",
    protein="adrb2_human"
)
# Returns: Ligands with binding affinity, type (agonist/antagonist)

GPCRdb_get_mutations

Purpose: Get experimental mutation data for GPCR

result = tu.tools.GPCRdb_get_mutations(
    operation="get_mutations",
    protein="adrb2_human"
)
# Returns: Mutations with effect on ligand binding/signaling

Why GPCRdb is Essential for GPCR Binder Discovery:

Receptor state structures: Active vs. inactive conformations
Ballesteros-Weinstein numbering: Standard residue numbering across GPCRs
Curated ligand data: Agonists, antagonists, allosteric modulators
Mutation effects: Direct effects on ligand binding

Phase 1.4: Structure Prediction (NVIDIA NIM)

Requires: NVIDIA_API_KEY environment variable

NvidiaNIM_alphafold2

Purpose: High-accuracy structure prediction with pLDDT confidence scores

result = tu.tools.NvidiaNIM_alphafold2(
    sequence="MRPSGTAGAALLALL...",  # Protein sequence
    algorithm="mmseqs2",             # MSA algorithm: "mmseqs2" or "jackhmmer"
    relax_prediction=False           # Optional: Run relaxation
)
# Returns: PDB structure string with pLDDT scores per residue
# pLDDT interpretation:
#   ≥90: Very high confidence
#   70-90: Confident
#   50-70: Low confidence
#   <50: Very low confidence

⚠️ Note: Async operation, may take 5-15 minutes. Max sequence length ~2000 AA.

NvidiaNIM_esmfold

Purpose: Fast structure prediction (synchronous)

result = tu.tools.NvidiaNIM_esmfold(
    sequence="MRPSGTAGAALLALL..."  # Protein sequence (max 1024 AA)
)
# Returns: PDB structure string

⚠️ Note: Fast (~30 sec) but limited to 1024 residues. No MSA, slightly lower accuracy.

NvidiaNIM_msa_search

Purpose: Generate multiple sequence alignment for structure prediction

result = tu.tools.NvidiaNIM_msa_search(
    sequence="MRPSGTAGAALLALL...",
    database="uniref90"  # "uniref90", "bfd", etc.
)
# Returns: MSA in A3M format

Use: Pre-generate MSA for repeated AlphaFold2 predictions.

Phase 2: Known Ligand Mining Tools

ChEMBL_get_target_activities

Purpose: Get all bioactivity data for target

result = tu.tools.ChEMBL_get_target_activities(
    target_chembl_id="CHEMBL203",
    limit=500
)
# Returns: activities with molecule_chembl_id, standard_type, standard_value

⚠️ Parameter: Use target_chembl_id, NOT chembl_target_id

ChEMBL_get_molecule

Purpose: Get detailed molecule information

result = tu.tools.ChEMBL_get_molecule(
    molecule_chembl_id="CHEMBL553"
)
# Returns: molecule data including SMILES, max_phase, properties

GtoPdb_get_interactions

Purpose: Get pharmacological interactions

result = tu.tools.GtoPdb_get_interactions(
    target_id="1797"  # GtoPdb target ID
)
# Returns: ligands with pKi, pIC50, action type

OpenTargets_get_chemical_probes_by_target_ensemblID

Purpose: Find validated chemical probes

result = tu.tools.OpenTargets_get_chemical_probes_by_target_ensemblID(
    ensemblId="ENSG00000146648"
)
# Returns: probes with ratings, use recommendations

OpenTargets_get_associated_drugs_by_target_ensemblID

Purpose: Get approved/clinical drugs

result = tu.tools.OpenTargets_get_associated_drugs_by_target_ensemblID(
    ensemblId="ENSG00000146648"
)
# Returns: drugs with phase, mechanism of action

BindingDB_get_ligands_by_uniprot (NEW)

Purpose: Get ligands with measured binding affinities

result = tu.tools.BindingDB_get_ligands_by_uniprot(
    uniprot="P00533",  # UniProt accession
    affinity_cutoff=10000  # Max affinity in nM
)
# Returns: SMILES, affinity_type (Ki/IC50/Kd), affinity value, PMID

Advantages: Direct affinity measurements, literature links, compounds not in ChEMBL

BindingDB_get_ligands_by_pdb (NEW)

Purpose: Get ligands for a PDB structure

result = tu.tools.BindingDB_get_ligands_by_pdb(
    pdb_ids="1M17",  # PDB ID
    affinity_cutoff=10000,
    sequence_identity=100
)
# Returns: ligands binding to the structure's protein

BindingDB_get_targets_by_compound (NEW)

Purpose: Find off-targets for selectivity analysis

result = tu.tools.BindingDB_get_targets_by_compound(
    smiles="CC(=O)Nc1ccc(cc1)O",  # Compound SMILES
    similarity_cutoff=0.85
)
# Returns: other proteins binding similar compounds

PubChem_search_assays_by_target_gene (NEW)

Purpose: Find HTS screening data for target

result = tu.tools.PubChem_search_assays_by_target_gene(
    gene_symbol="EGFR"
)
# Returns: list of AIDs (assay IDs) for this target

PubChem_get_assay_summary (NEW)

Purpose: Get assay statistics

result = tu.tools.PubChem_get_assay_summary(aid=504526)
# Returns: active/inactive counts, target info, assay metadata

PubChem_get_assay_active_compounds (NEW)

Purpose: Get hits from HTS screen

result = tu.tools.PubChem_get_assay_active_compounds(aid=504526)
# Returns: CIDs of active compounds

PubChem_get_assay_dose_response (NEW)

Purpose: Get IC50/EC50 curves

result = tu.tools.PubChem_get_assay_dose_response(aid=1053104)
# Returns: concentration-response data

PubChem_get_compound_bioactivity (NEW)

Purpose: Get all bioactivity for a compound

result = tu.tools.PubChem_get_compound_bioactivity(cid=2244)
# Returns: all assays, targets, activity outcomes

When to Use Each Source:

Source	Strengths	Primary Use
ChEMBL	Curated, SAR-ready	Main ligand source
GtoPdb	Pharmacology focus	GPCRs, channels
BindingDB	Direct Ki/Kd values	Affinity data
PubChem BioAssay	HTS screens	Novel scaffolds

Phase 3: Structure Analysis Tools

PDB_search_similar_structures

Purpose: Find PDB structures by sequence

result = tu.tools.PDB_search_similar_structures(
    query="P00533",  # UniProt or PDB ID
    type="sequence"  # or "structure"
)
# Returns: PDB IDs with similarity scores

get_protein_metadata_by_pdb_id

Purpose: Get structure metadata

result = tu.tools.get_protein_metadata_by_pdb_id(
    pdb_id="1M17"
)
# Returns: resolution, method, deposition date, title

get_binding_affinity_by_pdb_id

Purpose: Get co-crystallized ligand affinities

result = tu.tools.get_binding_affinity_by_pdb_id(
    pdb_id="1M17"
)
# Returns: ligand codes with Kd, Ki, IC50 values

alphafold_get_prediction

Purpose: Get AlphaFold predicted structure

result = tu.tools.alphafold_get_prediction(
    accession="P00533"  # UniProt accession
)
# Returns: structure prediction with pLDDT scores

⚠️ Parameter: Use accession, NOT uniprot

get_ligand_smiles_by_chem_comp_id

Purpose: Get ligand structure from PDB

result = tu.tools.get_ligand_smiles_by_chem_comp_id(
    chem_comp_id="AQ4"  # 3-letter ligand code from PDB
)
# Returns: SMILES, name, formula

EMDB_search_structures (NEW)

Purpose: Search EMDB for cryo-EM structures

result = tu.tools.EMDB_search_structures(
    query="EGFR membrane receptor"
)
# Returns: EMDB entries with emdb_id, resolution, title

When to use: Membrane proteins (GPCRs, ion channels), large complexes, targets where conformational states matter.

EMDB_get_structure (NEW)

Purpose: Get details for EMDB entry including associated PDB models

result = tu.tools.EMDB_get_structure(
    entry_id="EMD-12345"
)
# Returns: entry details including pdb_ids (associated atomic models)

Cryo-EM vs X-ray Decision:

Target Type	Prefer	Reason
GPCR	Cryo-EM	Native membrane conformation
Ion channel	Cryo-EM	Multiple functional states
Kinase	X-ray	Higher resolution typically
Large complex	Cryo-EM	Better for macromolecular assembly

Phase 3.5: Docking Validation (NVIDIA NIM)

Requires: NVIDIA_API_KEY environment variable

NvidiaNIM_diffdock

Purpose: Blind molecular docking (no predefined binding site needed)

result = tu.tools.NvidiaNIM_diffdock(
    protein=pdb_content,      # PDB file content as string
    ligand=ligand_sdf,        # Ligand in SDF or MOL2 format
    num_poses=10              # Number of poses to generate
)
# Returns: Docked poses with confidence scores
# Confidence interpretation:
#   >0.9: Excellent, high-confidence pose
#   0.7-0.9: Good, reliable binding mode
#   0.5-0.7: Moderate, consider alternatives
#   <0.5: Low confidence, may not bind

Use: When you have PDB structure and ligand SDF file.

NvidiaNIM_boltz2

Purpose: Protein-ligand complex from sequence + SMILES

result = tu.tools.NvidiaNIM_boltz2(
    polymers=[{
        "molecule_type": "protein",
        "sequence": "MRPSGTAGAALLALL..."
    }],
    ligands=[{
        "smiles": "COc1cc2ncnc(Nc3ccc(C#C)cc3)c2cc1OCCOC"
    }],
    sampling_steps=50,       # Default: 50, higher = better quality
    diffusion_samples=1      # Number of complex samples
)
# Returns: Protein-ligand complex structure
# Metrics: aggregate_score, pTM, ipTM
#   pTM: Protein structure confidence
#   ipTM: Interface prediction confidence (protein-ligand contact)

Use: When starting from SMILES only, no SDF file needed.

Docking Method Selection

Situation	Use	Reason
Have PDB + SDF file	NvidiaNIM_diffdock	Faster, validated inputs
Have sequence + SMILES only	NvidiaNIM_boltz2	End-to-end prediction
Quick screening many compounds	NvidiaNIM_diffdock	Faster per ligand
Need high-confidence complex	NvidiaNIM_boltz2	Better at interface prediction

Phase 4: Compound Expansion Tools

ChEMBL_search_similar_molecules

Purpose: Similarity search in ChEMBL

result = tu.tools.ChEMBL_search_similar_molecules(
    molecule="CC(C)Cc1ccc(cc1)C(C)C(O)=O",  # SMILES, ChEMBL ID, or name
    similarity=70  # Tanimoto threshold (0-100)
)
# Returns: similar molecules with similarity score

⚠️ Parameter: Use molecule, NOT smiles

PubChem_search_compounds_by_similarity

Purpose: Similarity search in PubChem

result = tu.tools.PubChem_search_compounds_by_similarity(
    smiles="CC(C)Cc1ccc(cc1)C(C)C(O)=O",
    threshold=0.7  # Tanimoto (0-1)
)
# Returns: CIDs with similarity scores

ChEMBL_search_substructure

Purpose: Substructure search in ChEMBL

result = tu.tools.ChEMBL_search_substructure(
    smiles="c1ccc2ncncc2c1"  # Quinazoline core
)
# Returns: molecules containing substructure

PubChem_search_compounds_by_substructure

Purpose: Substructure search in PubChem

result = tu.tools.PubChem_search_compounds_by_substructure(
    smiles="c1ccc2ncncc2c1"
)
# Returns: CIDs containing substructure

STITCH_get_chemical_protein_interactions

Purpose: Cross-database chemical-protein links

result = tu.tools.STITCH_get_chemical_protein_interactions(
    identifier="EGFR",
    species=9606  # Human
)
# Returns: chemicals with confidence scores

Phase 4.4: De Novo Molecule Generation (NVIDIA NIM)

Requires: NVIDIA_API_KEY environment variable

NvidiaNIM_genmol

Purpose: Scaffold hopping with masked regions

result = tu.tools.NvidiaNIM_genmol(
    smiles="COc1cc2ncnc(Nc3ccc([*{3-8}])c([*{1-3}])c3)c2cc1OCCCN1CCOCC1",
    num_molecules=100,       # Number to generate
    temperature=2.0,         # Diversity: 0.5=conservative, 2.0=diverse
    scoring="QED"            # "QED" or "logP"
)
# Returns: Generated molecules with QED/LogP scores
# Mask syntax: [*{min-max}] specifies atom count range

Mask Design Guidelines:

Position Type	Mask Example	Typical Use
Small substituent	`[*{1-3}]`	Halogen, methyl, hydroxyl
Medium group	`[*{3-6}]`	Linkers, small rings
Solubilizing tail	`[*{5-12}]`	Morpholine, piperazine
Core modification	`[*{6-10}]`	Ring replacements

Temperature Selection:

Temperature	Effect	When to Use
0.5-1.0	Conservative, close analogs	Early optimization
1.5-2.0	Balanced diversity	General exploration
2.5-3.0	High diversity, more novelty	Scaffold hopping

NvidiaNIM_molmim

Purpose: Controlled generation from reference molecule

result = tu.tools.NvidiaNIM_molmim(
    smi="COc1cc2ncnc(Nc3ccc(Cl)cc3)c2cc1OCCN1CCOCC1",
    num_molecules=50,        # Number to generate
    algorithm="CMA-ES"       # Optimization algorithm
)
# Returns: Optimized analogs with property scores
# Generates molecules similar to reference but with optimized properties

Use: Generate close analogs of top actives with improved properties.

Generation Strategy

1. Identify seeds: Top 3-5 actives from Phase 2
         ↓
2. Design approach:
   ├─ Know specific positions to vary? → NvidiaNIM_genmol (with masks)
   └─ Want general optimization? → NvidiaNIM_molmim
         ↓
3. Generate: 50-100 molecules per seed
         ↓
4. Filter: Pass to Phase 5 (ADMET)
         ↓
5. Dock: Score survivors in Phase 6

Phase 5: ADMET Tools

ADMETAI_predict_physicochemical_properties

Purpose: Drug-likeness assessment

result = tu.tools.ADMETAI_predict_physicochemical_properties(
    smiles=["CC(C)Cc1ccc(cc1)C(C)C(O)=O"]  # List of SMILES
)
# Returns: MW, logP, HBD, HBA, Lipinski violations, QED, TPSA

⚠️ Parameter: smiles must be a LIST, even for single compound

ADMETAI_predict_bioavailability

Purpose: Oral absorption prediction

result = tu.tools.ADMETAI_predict_bioavailability(
    smiles=["CC(C)Cc1ccc(cc1)C(C)C(O)=O"]
)
# Returns: Bioavailability_Ma, HIA_Hou, PAMPA, Caco2, Pgp_substrate

ADMETAI_predict_toxicity

Purpose: Toxicity endpoint predictions

result = tu.tools.ADMETAI_predict_toxicity(
    smiles=["CC(C)Cc1ccc(cc1)C(C)C(O)=O"]
)
# Returns: AMES, hERG, DILI, ClinTox, LD50, Carcinogens

ADMETAI_predict_CYP_interactions

Purpose: CYP enzyme interactions

result = tu.tools.ADMETAI_predict_CYP_interactions(
    smiles=["CC(C)Cc1ccc(cc1)C(C)C(O)=O"]
)
# Returns: CYP1A2, CYP2C9, CYP2C19, CYP2D6, CYP3A4 substrate/inhibitor

ADMETAI_predict_clearance_distribution

Purpose: PK predictions

result = tu.tools.ADMETAI_predict_clearance_distribution(
    smiles=["CC(C)Cc1ccc(cc1)C(C)C(O)=O"]
)
# Returns: Clearance, Half_Life, VDss, PPB

ChEMBL_search_compound_structural_alerts

Purpose: PAINS and toxicophore detection

result = tu.tools.ChEMBL_search_compound_structural_alerts(
    smiles="CC(C)Cc1ccc(cc1)C(C)C(O)=O"
)
# Returns: structural alerts, PAINS flags

Phase 6: Candidate Docking (NVIDIA NIM)

Requires: NVIDIA_API_KEY environment variable

Batch Docking Strategy

After ADMET filtering, dock all candidates against the target structure.

# Batch docking workflow
candidates = admet_passed_compounds  # From Phase 5

# Get reference score first
reference_result = tu.tools.NvidiaNIM_diffdock(
    protein=pdb_content,
    ligand=reference_ligand_sdf,
    num_poses=10
)
reference_confidence = reference_result['best_pose_confidence']

# Dock all candidates
docking_results = []
for compound in candidates:
    result = tu.tools.NvidiaNIM_diffdock(
        protein=pdb_content,
        ligand=compound['sdf'],
        num_poses=5
    )
    docking_results.append({
        'id': compound['id'],
        'confidence': result['best_pose_confidence'],
        'vs_reference': (result['best_pose_confidence'] / reference_confidence - 1) * 100
    })

# Sort by confidence
ranked = sorted(docking_results, key=lambda x: x['confidence'], reverse=True)

Scoring Integration

Score Component	Weight	Source
Docking confidence	40%	NvidiaNIM_diffdock
ADMET score	30%	ADMETAI_predict_*
Similarity to known active	20%	ChEMBL_search_similar_molecules
Novelty bonus	10%	Structural uniqueness

Phase 6.5: Literature Evidence (NEW)

PubMed_search_articles

Purpose: Search published SAR studies

result = tu.tools.PubMed_search_articles(
    query="EGFR inhibitor SAR structure-activity",
    limit=30
)
# Returns: articles with pmid, title, abstract, publication date

EuropePMC_search_articles (for Preprints)

Purpose: Search preprints from bioRxiv, medRxiv, and other sources (latest findings, not peer-reviewed)

# Search preprints using EuropePMC (bioRxiv/medRxiv don't have search APIs)
result = tu.tools.EuropePMC_search_articles(
    query="EGFR small molecule discovery",
    source="PPR",  # PPR = Preprints only
    pageSize=15
)

# If you have a DOI, get full bioRxiv metadata:
full_metadata = tu.tools.BioRxiv_get_preprint(doi="10.1101/2023.12.01.569554")
# Returns: preprints with doi, title, posted date

⚠️ Note: Preprints NOT peer-reviewed. Use for emerging compounds/methods.

MedRxiv_get_preprint

Purpose: Get medRxiv preprint by DOI (for search, use EuropePMC with source='PPR')

# Get preprint by DOI
result = tu.tools.MedRxiv_get_preprint(doi="10.1101/2021.04.29.21256344")

# For searching clinical preprints, use EuropePMC:
search = tu.tools.EuropePMC_search_articles(
    query="EGFR inhibitor clinical trial",
    source="PPR",
    pageSize=10
)
# Returns: preprints with doi, title, abstract, etc.

openalex_search_works

Purpose: Search with citation analysis

result = tu.tools.openalex_search_works(
    query="EGFR kinase inhibitor structure",
    limit=20
)
# Returns: works with cited_by_count, publication_year

Use: Identify high-impact papers and validate compound importance.

SemanticScholar_search_papers

Purpose: AI-ranked paper search

result = tu.tools.SemanticScholar_search_papers(
    query="EGFR small molecule binder",
    limit=20
)
# Returns: papers with relevance ranking, citations

Fallback Chains

Target ID Resolution

Primary: ChEMBL_search_targets
├─ Success → Use target_chembl_id
└─ Fail → GtoPdb_search_targets (for GPCR/ion channel/enzyme)
         └─ Fail → Document "Target not in databases"

GPCR-Specific Data (NEW)

If target is GPCR:
    Primary: GPCRdb_get_protein
    ├─ Success → Get GPCR family, class
    │   ├─ GPCRdb_get_structures → Active/inactive state structures
    │   ├─ GPCRdb_get_ligands → Known agonists/antagonists
    │   └─ GPCRdb_get_mutations → Mutation effects on binding
    └─ Fail (not in GPCRdb) → Use GtoPdb_search_targets

Druggability Assessment

Primary: OpenTargets_get_target_tractability_by_ensemblID
├─ Success → Use tractability data
└─ Fail → DGIdb_get_gene_druggability
         └─ Fail → Use target class as proxy

Bioactivity Data

Primary: ChEMBL_get_target_activities
├─ Success → Use ChEMBL data
└─ Fail → BindingDB_get_ligands_by_uniprot (NEW)
         ├─ Success → Use BindingDB data
         └─ Fail → GtoPdb_get_interactions
                  ├─ Success → Use GtoPdb data
                  └─ Fail → PubChem_search_assays_by_target_gene (NEW)
                           └─ Fail → Document "No bioactivity data"

Similarity Search

Primary: ChEMBL_search_similar_molecules
├─ Success → Process results
└─ Fail → PubChem_search_compounds_by_similarity
         └─ Fail → Document "Similarity search failed"

Structure Retrieval

Primary: get_protein_metadata_by_pdb_id (for each PDB)
├─ Success → Use experimental structure
└─ Fail (no PDB) → EMDB_search_structures (for membrane proteins)
         ├─ Success → Get PDB model via EMDB_get_structure
         └─ Fail → NvidiaNIM_alphafold2
                 └─ Fail (API error) → NvidiaNIM_esmfold
                         └─ Fail → alphafold_get_prediction (AlphaFold DB)
                                 └─ Fail → Document "No structural information"

Literature Search (NEW)

Primary: PubMed_search_articles (peer-reviewed)
├─ Success → Use published literature
└─ Supplement with:
         ├─ EuropePMC_search_articles (source='PPR' for preprints)
         └─ openalex_search_works (citation analysis)

Docking

Primary: NvidiaNIM_diffdock (have PDB + SDF)
├─ Success → Use docking scores
└─ Fail → NvidiaNIM_boltz2 (from sequence + SMILES)
         └─ Fail → Skip docking, use similarity-based scoring

De Novo Generation

Primary: NvidiaNIM_genmol (specific position variation)
├─ Success → Process generated molecules
└─ Fail → NvidiaNIM_molmim (general analog generation)
         └─ Fail → Use similarity search only (no generation)

ADMET Prediction

Primary: ADMETAI_predict_* (all endpoints)
├─ Success → Use predictions
└─ Fail (invalid SMILES) → Skip compound, document reason
└─ Fail (API error) → Document "ADMET unavailable"

Common Parameter Errors

Tool	Wrong	Correct	Notes
`OpenTargets_*`	`ensembl_id`	`ensemblId`	CamelCase for OpenTargets
`ChEMBL_get_target_activities`	`chembl_target_id`	`target_chembl_id`	Underscore style
`ChEMBL_search_similar_molecules`	`smiles`	`molecule`	Accepts SMILES, ID, or name
`alphafold_get_prediction`	`uniprot`	`accession`	Just the accession
`ADMETAI_*`	`smiles="..."`	`smiles=["..."]`	Must be list
`NvidiaNIM_alphafold2`	`seq`	`sequence`	Full parameter name
`NvidiaNIM_genmol`	`smiles="C..."`	`smiles="C...[*{1-3}]..."`	Must have mask regions
`NvidiaNIM_boltz2`	`sequence="..."`	`polymers=[{"molecule_type": "protein", "sequence": "..."}]`	Use polymers list

Batch Processing Pattern

For efficiency, batch similar operations:

# Define calls
calls = [
    {"name": "ChEMBL_get_molecule", "arguments": {"molecule_chembl_id": id}}
    for id in chembl_ids[:50]  # Batch of 50
]

# Execute in parallel
results = tu.run_batch(calls)

# Process results
for result in results:
    if result and 'molecule_structures' in result:
        process_molecule(result)

Rate Limiting Awareness

Database	Rate Limit	Recommendation
ChEMBL	~10 req/sec	Batch queries when possible
PubChem	~5 req/sec	Use batch endpoints
ADMET-AI	No strict limit	Batch SMILES in lists
OpenTargets	GraphQL, lenient	Single complex queries preferred
UniProt	~10 req/sec	Batch search preferred
NVIDIA NIM	API key quota	Check quota, cache results

NVIDIA NIM Specific Notes

Tool	Typical Runtime	Notes
`NvidiaNIM_alphafold2`	5-15 min	Async, check status
`NvidiaNIM_esmfold`	~30 sec	Fast, max 1024 AA
`NvidiaNIM_diffdock`	~1-2 min	Per ligand
`NvidiaNIM_boltz2`	~2-5 min	Includes structure prediction
`NvidiaNIM_genmol`	~1-3 min	Depends on num_molecules
`NvidiaNIM_molmim`	~1-2 min	Fast analog generation

API Key Check:

import os
if not os.environ.get("NVIDIA_API_KEY"):
    print("Warning: NVIDIA_API_KEY not set. NvidiaNIM tools unavailable.")
    # Fall back to non-NIM alternatives

For large expansions (>500 compounds):

Use batch endpoints
Add small delays between batches
Cache results for reuse
For docking: prioritize top 50-100 candidates only

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Tools Reference for Small Molecule Binder Discovery

Phase 1: Target Validation Tools

UniProt_search

MyGene_query_genes

ChEMBL_search_targets

OpenTargets_get_target_tractability_by_ensemblID

DGIdb_get_gene_druggability

ChEMBL_search_binding_sites

InterPro_get_protein_domains

Thera-SAbDab Tools (NEW - Therapeutic Antibody Landscape)

TheraSAbDab_search_by_target

TheraSAbDab_search_therapeutics

TheraSAbDab_get_all_therapeutics

GPCRdb Tools (for GPCR Targets)

GPCRdb_get_protein

GPCRdb_list_proteins

GPCRdb_get_structures

GPCRdb_get_ligands

GPCRdb_get_mutations

Phase 1.4: Structure Prediction (NVIDIA NIM)

NvidiaNIM_alphafold2

NvidiaNIM_esmfold

NvidiaNIM_msa_search

Phase 2: Known Ligand Mining Tools

ChEMBL_get_target_activities

ChEMBL_get_molecule

GtoPdb_get_interactions

OpenTargets_get_chemical_probes_by_target_ensemblID

OpenTargets_get_associated_drugs_by_target_ensemblID

BindingDB_get_ligands_by_uniprot (NEW)

BindingDB_get_ligands_by_pdb (NEW)

BindingDB_get_targets_by_compound (NEW)

PubChem_search_assays_by_target_gene (NEW)

PubChem_get_assay_summary (NEW)

PubChem_get_assay_active_compounds (NEW)

PubChem_get_assay_dose_response (NEW)

PubChem_get_compound_bioactivity (NEW)

Phase 3: Structure Analysis Tools

PDB_search_similar_structures

get_protein_metadata_by_pdb_id

get_binding_affinity_by_pdb_id

alphafold_get_prediction

get_ligand_smiles_by_chem_comp_id

EMDB_search_structures (NEW)

EMDB_get_structure (NEW)

Phase 3.5: Docking Validation (NVIDIA NIM)

NvidiaNIM_diffdock

NvidiaNIM_boltz2

Docking Method Selection

Phase 4: Compound Expansion Tools

ChEMBL_search_similar_molecules

PubChem_search_compounds_by_similarity

ChEMBL_search_substructure

PubChem_search_compounds_by_substructure

STITCH_get_chemical_protein_interactions

Phase 4.4: De Novo Molecule Generation (NVIDIA NIM)

NvidiaNIM_genmol

NvidiaNIM_molmim

Generation Strategy

Phase 5: ADMET Tools

ADMETAI_predict_physicochemical_properties

ADMETAI_predict_bioavailability

ADMETAI_predict_toxicity

ADMETAI_predict_CYP_interactions

ADMETAI_predict_clearance_distribution

ChEMBL_search_compound_structural_alerts

Phase 6: Candidate Docking (NVIDIA NIM)

Batch Docking Strategy

Scoring Integration

Phase 6.5: Literature Evidence (NEW)

PubMed_search_articles

EuropePMC_search_articles (for Preprints)

MedRxiv_get_preprint

openalex_search_works

SemanticScholar_search_papers

Fallback Chains

Target ID Resolution

GPCR-Specific Data (NEW)

Druggability Assessment

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