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.
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.
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:
Report-first approach - Create report file FIRST, then populate progressively
Target validation FIRST - Confirm druggability before compound searching
Multi-strategy approach - Combine structure-based and ligand-based methods
ADMET-aware filtering - Eliminate poor compounds early
Evidence grading - Grade candidates by supporting evidence
Actionable output - Provide prioritized candidates with rationale
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:
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
Progressively update the report - As you gather data, update each section immediately.
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)
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:
ChEMBL_get_target_activities - curated, SAR-ready
BindingDB_get_ligands_by_uniprot - direct Ki/Kd with literature links
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%:
CYP: ADMETAI_predict_CYP_interactions - flag CYP3A4 inhibitors
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