How to connect ChEMBL MCP with Python

Search for chemical compounds in ChEMBL database by name, ChEMBL ID, or molecular structure. WHEN TO USE compound_search vs drug_search: • compound_search: Use for ANY molecule lookup by name, ID, or structure (may lack clinical data) • drug_search: Use ONLY when searching by therapeutic indication (e.g., "drugs for diabetes") For a simple drug name lookup like "find aspirin", use compound_search. SEARCH STRATEGIES: • By name: Use 'name' for drug names, synonyms, or trade names (case-insensitive, partial match) • By ID: Use 'chembl_id' for direct lookup when you know the exact identifier (e.g., 'CHEMBL25' for aspirin) • By structure: Use 'smiles' with 'similarity_threshold' (70-100%) for finding structurally similar compounds • Substructure: Use 'smiles' without threshold to find compounds containing that substructure SIMILARITY SEARCH (uses Morgan fingerprints, radius 2, 2048 bits via FPSim2): • 70%: Loose similarity (finds diverse analogs) • 80%: Good starting threshold (finds close analogs) • 90%+: Very strict (finds nearly identical compounds) MAX_PHASE VALUES (clinical development stage): • 4 = Approved (marketed drug, e.g., FDA/EMA approved) • 3 = Phase 3 Clinical Trials • 2 = Phase 2 Clinical Trials (includes INN applications) • 1 = Phase 1 Clinical Trials (includes USAN applications) • 0.5 = Early Phase 1 • -1 = Unknown clinical phase • NULL = Preclinical compound (bioactivity data only) RETURNED DATA INCLUDES: • Molecular properties: MW, ALogP (lipophilicity), PSA, HBA, HBD, rotatable bonds, aromatic rings • QED (Quantitative Estimate of Drug-likeness): 0-1 scale, higher = more drug-like • Chirality: 0=racemic, 1=single stereoisomer, 2=achiral, -1=unchecked • Rule of Five: MW<500, ALogP<5, HBD<5, HBA<10 (compounds with 0-1 violations are Ro5 compliant) • ATC classifications, cross-references to external databases, molecule hierarchy (parent/salt forms) TIPS: • For approved drugs, add max_phase=4 • ChEMBL uses compound 'families' - salts map to parent compounds • Use get_bioactivity after finding compounds to get their target activities • Properties are calculated on parent (salt-free) form EXAMPLES: • Find aspirin: name='aspirin' or chembl_id='CHEMBL25' • Find kinase inhibitors: name='nib', max_phase=4 • Find structural analogs: Use SMILES with similarity_threshold=80

Retrieve bioactivity measurements (IC50, Ki, EC50, etc.) for compound-target interactions. WORKFLOW: Use target_search or compound_search first to get ChEMBL IDs, then query bioactivity. ACTIVITY TYPES (standard_type field): • IC50: Half-maximal inhibitory concentration (most common for inhibitors) • Ki: Inhibition constant (binding affinity, independent of substrate concentration) • Kd: Dissociation constant (direct binding measurement) • EC50: Half-maximal effective concentration (for agonists) • AC50: Half-maximal activity concentration • Potency/ED50: Effective dose measurements pChEMBL VALUE (recommended for filtering - standardized potency): • Definition: -log10(molar IC50/Ki/Kd/EC50/AC50/Potency/ED50) • Only calculated when: standard_relation='=' AND standard_units='nM' AND value>0 • pChEMBL 9 = 1 nM (highly potent, drug-like) • pChEMBL 7 = 100 nM (potent) • pChEMBL 6 = 1 µM (moderate) • pChEMBL 5 = 10 µM (weak) • pChEMBL 3 = 1 mM (very weak) • Use min_pchembl >= 6 for µM or better activity • Use min_pchembl >= 7 for sub-100nM potency (drug-like) ASSAY TYPES (assay_type field): • B (Binding): Direct target binding measurements (Ki, Kd) • F (Functional): Biological effect in cells/tissues (EC50, IC50 in cellular context) • A (ADME): Absorption, distribution, metabolism, excretion assays (t1/2, bioavailability) • T (Toxicity): Cytotoxicity, hERG inhibition • P (Physicochemical): Solubility, stability (no biological material) • U (Unclassified): Cannot fit single category DATA VALIDITY FLAGS (data_validity_comment field): • 'Outside typical range': Value unusually high/low for activity type • 'Potential missing data': Incomplete data entry • 'Potential author error': Suspected error in publication • 'Manually validated': Curator confirmed accuracy • 'Potential transcription error': Values differ by 3 or 6 orders of magnitude (unit error) LIGAND EFFICIENCY METRICS (in results when available): • LE (Ligand Efficiency): Activity per heavy atom • BEI (Binding Efficiency Index): Activity per molecular weight • LLE (Lipophilic Ligand Efficiency): pActivity - LogP • SEI (Surface Efficiency Index): Activity per polar surface area TIPS: • Use with ADMET targets to get experimental ADMET data • Combine molecule_chembl_id + target_chembl_id for specific compound-target pairs • Check potential_duplicate flag - may indicate cited (not independent) measurements • activity_comment may indicate 'active'/'inactive' conclusions from depositor • document_chembl_id links to source publication for verification

Search for biological targets (proteins, enzymes, receptors, organisms) in ChEMBL database. SEARCH STRATEGIES: • By name: Use 'target_name' for protein names, families (e.g., 'kinase'), or receptors • By gene: Use 'gene_symbol' for exact gene symbol matches (e.g., 'EGFR', 'BRAF', 'TP53') • By ID: Use 'target_chembl_id' for direct lookup (e.g., 'CHEMBL203' for EGFR) • By organism: Filter results to specific species (e.g., 'Homo sapiens', 'Mus musculus') • By type: Filter by target_type to get specific categories TARGET TYPES (target_type field): • SINGLE PROTEIN: Individual protein (most common, highest confidence bioactivity data) • PROTEIN COMPLEX: Multi-subunit complex (e.g., ion channels, GPCRs with multiple subunits) • PROTEIN FAMILY: Homologous protein groups (broader search, lower specificity) • PROTEIN-PROTEIN INTERACTION: Two interacting proteins • CHIMERIC PROTEIN: Engineered fusion protein • SELECTIVITY GROUP: Panel of related targets for selectivity profiling • ORGANISM: Whole organism (bacteria, parasites, viruses, fungi) • TISSUE: Tissue-level target • CELL-LINE: Cell-based target (phenotypic screening) • NUCLEIC-ACID: DNA/RNA targets • SUBCELLULAR: Subcellular compartments • UNKNOWN: Unclassified targets TARGET CONFIDENCE SCORES (in bioactivity data): • 9: Direct single protein target (highest confidence, most reliable) • 8: Homologous single protein (inferred from related species) • 7: Direct protein complex subunits (multi-subunit target) • 6: Homologous protein complex • 5: Direct protein selectivity group • 4: Homologous selectivity group • 3: Protein not in target complex • 2: Non-protein organism target • 1: Non-molecular target (cell-line, organism, tissue) • 0: Default or uncurated TARGET RELATIONSHIPS (between targets): • EQUIVALENT TO: Same target in different contexts • OVERLAPS WITH: Partially shared components • SUBSET OF: Contains subset of components • SUPERSET OF: Contains additional components RETURNED DATA INCLUDES: • Target components: Protein subunits with accessions (UniProt) • GO annotations: Gene Ontology terms for molecular function, biological process, cellular component • Cross-references: Links to UniProt, PFAM, InterPro, IntAct, Reactome, etc. • Species information: Tax ID and organism name TIPS: • Use gene_symbol for exact matches - more precise than target_name • After finding a target, use get_bioactivity with target_chembl_id to find active compounds • For drug discovery, focus on SINGLE PROTEIN targets (confidence >= 7) • Filter by organism='Homo sapiens' for human targets • Check GO annotations to understand target function and localization

Retrieve mechanism of action (MoA) data for approved drugs and clinical candidates. PURPOSE: Understand how drugs interact with their targets - essential for drug repurposing, understanding polypharmacology, and target validation. ACTION TYPES (action_type field): • INHIBITOR: Blocks target activity (most common for small molecule drugs) • ANTAGONIST: Blocks receptor activation (prevents agonist binding) • AGONIST: Activates receptor (mimics natural ligand) • BLOCKER: Blocks ion channels or transporters • MODULATOR: Alters target activity (often allosteric mechanism) • POSITIVE ALLOSTERIC MODULATOR: Enhances agonist response without binding orthosteric site • NEGATIVE ALLOSTERIC MODULATOR: Reduces agonist response • OPENER: Opens ion channels (increases conductance) • ACTIVATOR: Increases enzyme activity • PARTIAL AGONIST: Partially activates receptor (submaximal efficacy) • INVERSE AGONIST: Reduces constitutive (basal) receptor activity • SUBSTRATE: Acts as substrate for enzyme (e.g., prodrugs) • RELEASING AGENT: Causes release of neurotransmitters • SEQUESTERING AGENT: Binds and removes target (e.g., antibodies) KEY RESULT FIELDS: • direct_interaction: true = drug binds directly to target; false = indirect effect • disease_efficacy: true = target is directly relevant to therapeutic effect • molecular_mechanism: Specific molecular action (e.g., 'Cyclooxygenase inhibitor') • binding_site_name/comment: Where drug binds on target (e.g., 'ATP binding site') • selectivity_comment: Notes on target selectivity vs related proteins • mechanism_refs: Literature references supporting the mechanism BINDING SITE INFORMATION: Results may include binding site details when known: • site_name: Named binding pocket (e.g., 'Colchicine site', 'ATP binding domain') • site_id: ChEMBL binding site identifier for further lookup WORKFLOW: 1. Find compound: compound_search(name='imatinib') 2. Get mechanisms: get_mechanism(molecule_chembl_id='CHEMBL941') 3. Validate with bioactivity: get_bioactivity(molecule_chembl_id='CHEMBL941', target_chembl_id='CHEMBL1862') TIPS: • MoA data is manually curated for approved drugs and advanced clinical candidates • Use target_chembl_id to find all drugs acting on a specific target (drug repurposing) • Check mechanism_refs for original publications supporting the mechanism • Combine with get_bioactivity to see quantitative potency data for mechanism targets • Parent compound IDs work even when mechanism is stored under salt form

Search for approved drugs and clinical candidates by therapeutic indication. PURPOSE: Find drugs used for specific diseases, identify approved treatments, explore drug repurposing opportunities. DRUG VS COMPOUND: • Drug: Compound with assigned INN/USAN name + clinical data (max_phase ≥ 1) • Compound: Any molecule in ChEMBL (may only have bioactivity data) • Use compound_search for broader chemical searches, drug_search for therapeutic applications MAX_PHASE VALUES (clinical development stage): • 4 = Approved (marketed drug, e.g., FDA/EMA approved) • 3 = Phase III Clinical Trials (large-scale efficacy trials) • 2 = Phase II Clinical Trials (proof of concept, includes INN applications) • 1 = Phase I Clinical Trials (safety, includes USAN applications) • 0.5 = Early Phase 1 (exploratory studies) • -1 = Unknown clinical phase (status uncertain) • NULL = Preclinical only (no human trials, compound_search more appropriate) SAFETY FLAGS IN RESULTS: • black_box_warning: 1 = has FDA black box warning (serious safety concern) • withdrawn_flag: true = withdrawn from one or more markets • withdrawn_reason: Why drug was withdrawn (e.g., hepatotoxicity, cardiac effects) • withdrawn_country/year/class: Details about market withdrawal INDICATION SEARCH: • Uses MeSH (Medical Subject Headings) disease terminology • Also searches EFO (Experimental Factor Ontology) terms • Partial matching supported (e.g., 'cancer' matches 'breast cancer', 'lung cancer') • Common indications: hypertension, diabetes, cancer, asthma, depression, arthritis INDICATION DETAILS IN RESULTS: • mesh_id/mesh_heading: MeSH disease classification • efo_id/efo_term: EFO disease ontology • max_phase_for_ind: Highest phase achieved for this specific indication TIPS: • Use only_approved=True for marketed drugs only • Check black_box_warning and withdrawn_flag for safety information • After finding drugs, use get_mechanism to understand their molecular targets • Use max_phase=3 to include drugs in late-stage trials (potential future approvals) WORKFLOW EXAMPLE: 1. drug_search(indication='hypertension', only_approved=True) → Find approved antihypertensives 2. get_mechanism(molecule_chembl_id='CHEMBL1200749') → Get targets for amlodipine 3. get_bioactivity(target_chembl_id='CHEMBL1940') → Find other L-type calcium channel blockers

Retrieve ADMET-related molecular properties for drug-likeness assessment. IMPORTANT: ChEMBL provides CALCULATED molecular properties (from structure), not experimental ADMET data. For experimental ADMET measurements, use get_bioactivity with specific ADMET target ChEMBL IDs. CALCULATED PROPERTIES (what this tool returns): • ALogP: Calculated lipophilicity (Wildman-Crippen LogP) - Optimal for oral drugs: 1-3 - < 0: Poor membrane permeability - > 5: Poor solubility, potential accumulation • Molecular Weight (full_mwt): Total molecular weight including salts • Molecular Weight (mw_freebase): Parent compound molecular weight only • H-bond Donors (HBD): Rule-of-5 limit < 5 • H-bond Acceptors (HBA): Rule-of-5 limit < 10 • Polar Surface Area (PSA): Topological PSA - < 140 Ų: Good oral absorption - < 90 Ų: Better CNS penetration (crosses BBB) • Rotatable Bonds (RTB): < 10 for good oral bioavailability • Heavy Atoms: Non-hydrogen atom count • Aromatic Rings: < 4 recommended for drug-likeness • Rule-of-5 Violations (num_ro5_violations): 0-1 preferred • Rule-of-3 Pass (ro3_pass): Y/N for fragment-like properties • QED Weighted: Quantitative Estimate of Drug-likeness (0-1 scale) - Higher = more drug-like profile - Based on MW, ALogP, HBD, HBA, PSA, RTB, aromatic rings, alerts DRUG-LIKENESS GUIDELINES: • Lipinski Rule-of-5: MW < 500, ALogP < 5, HBD ≤ 5, HBA ≤ 10 • Veber Rules: PSA ≤ 140 Ų, RTB ≤ 10 • Lead-like: MW < 450, ALogP -4 to 4.2, RTB ≤ 10 FOR EXPERIMENTAL ADMET DATA (use get_bioactivity with these targets): • hERG (CHEMBL240): Cardiac safety (K+ channel, IC50 > 10µM preferred) • CYP3A4 (CHEMBL340): Major metabolizing enzyme (avoid strong inhibition) • CYP2D6 (CHEMBL289): Polymorphic CYP (genetic variability concerns) • CYP2C9 (CHEMBL3397): Warfarin metabolism (drug interactions) • P-glycoprotein (CHEMBL4302): Drug efflux transporter (affects BBB, gut) WORKFLOW EXAMPLE: 1. Get calculated properties: get_admet(molecule_chembl_id='CHEMBL941') 2. Check hERG liability: get_bioactivity(molecule_chembl_id='CHEMBL941', target_chembl_id='CHEMBL240') 3. Check CYP inhibition: get_bioactivity(molecule_chembl_id='CHEMBL941', target_chembl_id='CHEMBL340')