Drug Discovery Pipeline

Drug Discovery Pipeline (Meta Skill)

This meta-skill orchestrates a multi-stage drug discovery workflow by combining target validation, compound searching, property filtering, and lead optimization into a single coherent pipeline. It coordinates four specialized skills to move from a biological target to a ranked list of drug candidates.

Workflow

Step 1: Target Validation

Query UniProt for the target protein to gather functional annotations, known domains, post-translational modifications, and disease associations. Assess druggability by checking for known binding pockets, ligand-binding domains, and membership in established druggable protein families (kinases, GPCRs, ion channels, nuclear receptors).

Step 2: Known Drug and Compound Survey

Query ChEMBL for existing drugs, clinical candidates, and bioactive compounds reported against the target. Collect activity data (IC50, Ki, EC50) and note selectivity profiles. Identify chemical series and mechanism of action classes already explored in the literature.

Step 3: Lead Expansion via Similarity Search

Use PubChem similarity and substructure searches to find structural analogs of the most promising hits from Step 2. Expand the candidate pool by exploring nearby chemical space using Tanimoto similarity with ECFP4 fingerprints. Retrieve vendor availability and patent status where possible.

Step 4: Property Filtering and ADMET Prediction

Apply RDKit to compute molecular descriptors and filter candidates through established drug-likeness rules:

• Lipinski Rule of Five (MW, LogP, HBD, HBA)
• Veber rules (rotatable bonds, TPSA)
• PAINS filter to remove frequent hitters
• ADMET property estimation (solubility, permeability, CYP inhibition flags)

Remove compounds that violate multiple criteria or show structural alerts.

Step 5: Compound Ranking and Prioritization

Score remaining candidates using a weighted multi-parameter optimization:

• Potency (pIC50 or pKi against target)
• Selectivity (activity ratio vs. off-targets)
• Drug-likeness (QED score)
• Synthetic accessibility (SA score)
• Novelty (Tanimoto distance from known drugs)

Output a ranked table of top candidates with reasoning for each score.

Integration Points

• uniprot-protein -- Target protein annotation, domain architecture, druggability assessment
• chembl-drug -- Bioactivity data, existing drugs, SAR context for the target
• pubchem-compound -- Similarity searching, analog identification, vendor availability
• rdkit-chemistry -- Descriptor calculation, filtering rules, ADMET prediction, scoring

Output Formats

• Target summary: Protein name, function, druggability assessment, known ligands
• Compound table: SMILES, name, source, activity, drug-likeness scores
• Ranked list: Top 10-20 candidates with composite scores and rationale
• SAR notes: Observed structure-activity trends across chemical series

Best Practices

1. Always validate the target before searching for compounds to avoid wasted effort 2. Set activity thresholds early (e.g., IC50 < 1 uM) to keep the candidate pool manageable 3. Use multiple fingerprint types for similarity search to capture diverse analogs 4. Apply PAINS filters before investing effort in detailed ADMET analysis 5. Document the rationale for each filtering step to maintain reproducibility 6. Consider the therapeutic area when weighting ranking criteria 7. Flag compounds with known IP restrictions or limited synthetic routes 8. Cross-check top candidates against ChEMBL for any reported toxicity signals 9. Present results with confidence levels reflecting data quality and coverage 10. Iterate the pipeline if initial results are sparse by relaxing similarity thresholds