tooluniverse-binder-discovery

# 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_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 6. `MolGlueDB_search_compounds` - molecular glue degraders specifically (small molecules that induce a novel protein-protein interaction rather than occupy a conventional pocket); check this when the target has no tractable pocket but is a known or candidate neosubstrate 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 + SMILES: use `NvidiaNIM_boltz2(polymers=[...], ligands=[...])`. Dock a known reference inhibitor first to validate the binding pocket geometry before running candidates. --- ## Phase 4: Compound Expansion ### 4.1-4.3 Search-Based Expansion Use 3-5 diverse actives as seeds, similarity threshold 70-85%: - `ChEMBL_search_similar_molecules(molecule=SMILES, similarity=70)` - `PubChem_search_compounds_by_similarity(smiles, threshold=0.7)` - `ChEMBL_search_substructure(smiles=core_scaffold)` - `STITCH_get_chemical_protein_interactions(identifier=gene, species=9606)` ### 4.4 De Novo Generation (NVIDIA NIM) **GenMol** - scaffold hopping with masked regions: ``` NvidiaNIM_genmol(smiles="...core...[*{3-8}]...tail...[*{1-3}]...", num_molecules=100, temperature=2.0, scoring="QED") ``` **MolMIM** - controlled analog generation: ``` NvidiaNIM_molmim(smi=reference_smiles, num_molecules=50, algorithm="CMA-ES") ``` --- ## Phase 5: ADMET Filtering Apply sequentially (all tools accept `smiles=[list]`): 1. **Physicochemical**: `ADMETAI_predict_physicochemical_properties` - Lipinski violations <= 1, QED > 0.3, MW 200-600 2. **Bioavailability**: `ADMETAI_predict_bioavailability` - oral bioavailability > 0.3 3. **Toxicity**: `ADMETAI_predict_toxicity` - AMES < 0.5, hERG < 0.5, DILI < 0.5 4. **CYP**: `ADMETAI_predict_CYP_interactions` - flag CYP3A4 inhibitors 5. **Alerts**: `ChEMBL_search_compound_structural_alerts` - no PAINS Include a filter funnel summary in the report showing pass/fail counts at each stage. --- ## Phase 6: Candidate Docking & Prioritization Composite score: docking confidence (40%) + ADMET score (30%) + similarity to known active (20%) + novelty (10%, not in ChEMBL + novel scaffold bonus). Evidence tiers for candidates: - T1 (3 stars): Experimental IC50/Ki < 100 nM - T2 (2 stars): Docking within 5% of reference OR IC50 100-1000 nM - T3 (1 star): >80% similarity to T1 compound - T4 (0 stars): 70-80% similarity, scaffold match only - T5 (no stars): Generated molecule, ADMET-passed, no docking Deliver top 20 candidates with: Rank, ID, SMILES, docking score, ADMET score, overall score, source, evidence tier. --- ## Phase 6.5: Literature Evidence - `PubMed_search_articles(query="[TARGET] inhibitor SAR")` - peer-reviewed - `EuropePMC_search_articles(query, source="PPR")` - preprints (not peer-reviewed) - `openalex_search_works(query)` - citation analysis --- ## Fallback Chains ``` Target ID: ChEMBL_search_targets -> GtoPdb_search_targets -> "Not in databases" Druggability: OpenTargets tractability -> DGIdb druggability -> target class proxy Bioactivity: ChEMBL -> BindingDB -> GtoPdb -> PubChem BioAssay -> "No data" Structure: PDB -> EMDB (membrane) -> alphafold_get_prediction -> NvidiaNIM_esmfold -> AlphaFold DB -> "None" Similarity: ChEMBL similar -> PubChem similar -> "Search failed" Docking: get_diffdock_info -> NvidiaNIM_boltz2 -> similarity-based scoring Generation: NvidiaNIM_genmol -> NvidiaNIM_molmim -> similarity search only Literature: PubMed -> EuropePMC (preprints) -> OpenAlex GPCR data: GPCRdb_get_protein -> GtoPdb_search_targets ``` --- ## Programmatic Access (Beyond Tools) When ToolUniverse tools return limited compound sets, access chemical databases directly: ```python import requests, pandas as pd # PubChem batch property retrieval (up to 100 CIDs per call) cids = "2244,5988,3672" url = f"https://pubchem.ncbi.nlm.nih.gov/rest/pug/compound/cid/{cids}/property/MolecularWeight,XLogP,TPSA,HBondDonorCount,HBondAcceptorCount/JSON" props = pd.DataFrame(requests.get(url).json()["PropertyTable"]["Properties"]) # ChEMBL bioactivity bulk download for a target target_id = "CHEMBL203" # EGFR url = f"https://www.ebi.ac.uk/chembl/api/data/activity.json?target_chembl_id={target_id}&pchembl_value__gte=5&limit=1000" activities = requests.get(url).json()["activities"] df = pd.DataFrame(activities)[["molecule_chembl_id", "canonical_smiles", "pchembl_value", "standard_type"]] # Lipinski Rule of 5 filtering (no RDKit needed) lipinski = props[(props["MolecularWeight"] <= 500) & (props["XLogP"] <= 5) & (props["HBondDonorCount"] <= 5) & (props["HBondAcceptorCount"] <= 10)] # SDF download from PubChem (for docking input) sdf_url = f"https://pubchem.ncbi.nlm.nih.gov/rest/pug/compound/cid/{cids}/SDF" sdf_content = requests.get(sdf_url).text ``` See `tooluniverse-data-wrangling` skill for format cookbook and pagination patterns. --- ## NVIDIA NIM Runtime Notes AlphaFold2: 5-15 min (async, max ~2000 AA). ESMFold: ~30 sec (max 1024 AA). DiffDock: ~1-2 min/ligand. Boltz2: ~2-5 min. GenMol/MolMIM: ~1-3 min. Always check: `import os; nvidia_available = bool(os.environ.get("NVIDIA_API_KEY"))` For large expansions (>500 compounds): batch in chunks of 100, prioritize top candidates for docking. --- ## Reference Files - [WORKFLOW_DETAILS.md](./WORKFLOW_DETAILS.md) - Phase-by-phase procedures, code patterns, screening protocols - [TOOLS_REFERENCE.md](./TOOLS_REFERENCE.md) - Complete tool reference with parameters and fallback chains