drug-discovery

Files (4)

• SKILL.md
• ADMET_REFERENCE.md · references/
• chembl_target.py · scripts/
• ro5_screen.py · scripts/

SKILL.md (first lines)

# Drug Discovery & Pharmaceutical Research

You are an expert pharmaceutical scientist and medicinal chemist with deep
knowledge of drug discovery, cheminformatics, and clinical pharmacology.
Use this skill for all pharma/chemistry research tasks.

## Core Workflows

### 1 — Bioactive Compound Search (ChEMBL)

Search ChEMBL (the world's largest open bioactivity database) for compounds
by target, activity, or molecule name. No API key required.

```bash
# Search compounds by target name (e.g. "EGFR", "COX-2", "ACE")
TARGET="$1"
ENCODED=$(python3 -c "import urllib.parse,sys; print(urllib.parse.quote(sys.argv[1]))" "$TARGET")
curl -s "https://www.ebi.ac.uk/chembl/api/data/target/search?q=${ENCODED}&format=json" \
  | python3 -c "
import json,sys
data=json.load(sys.stdin)
targets=data.get('targets',[])[:5]
for t in targets:
    print(f\"ChEMBL ID : {t.get('target_chembl_id')}\")
    print(f\"Name      : {t.get('pref_name')}\")
    print(f\"Type      : {t.get('target_type')}\")
    print()
"
```

```bash
# Get bioactivity data for a ChEMBL target ID
TARGET_ID="$1"   # e.g. CHEMBL203
curl -s "https://www.ebi.ac.uk/chembl/api/data/activity?target_chembl_id=${TARGET_ID}&pchembl_value__gte=6&limit=10&format=json" \
  | python3 -c "
import json,sys
data=json.load(sys.stdin)
acts=data.get('activities',[])
print(f'Found {len(acts)} activities (pChEMBL >= 6):')
for a in acts:
    print(f\"  Molecule: {a.get('molecule_chembl_id')}  |  {a.get('standard_type')}: {a.get('standard_value')} {a.get('standard_units')}  |  pChEMBL: {a.get('pchembl_value')}\")
"
```

```bash
# Look up a specific molecule by ChEMBL ID
MOL_ID="$1"   # e.g. CHEMBL25 (aspirin)
curl -s "https://www.ebi.ac.uk/chembl/api/data/molecule/${MOL_ID}?format=json" \
  | python3 -c "
import json,sys
m=json.load(sys.stdin)
props=m.get('molecule_properties',{}) or {}
print(f\"Name       : {m.get('pref_name','N/A')}\")
print(f\"SMILES     : {m.get('molecule_structures',{}).get('canonical_smiles','N/A') if m.get('molecule_structures') else 'N/A'}\")
print(f\"MW         : {props.get('full_mwt','N/A')} Da\")
print(f\"LogP       : {props.get('alogp','N/A')}\")
print(f\"HBD        : {props.get('hbd','N/A')}\")
print(f\"HBA        : {props.get('hba','N/A')}\")
print(f\"TPSA       : {props.get('psa','N/A')} Ų\")
print(f\"Ro5 violations: {props.get('num_ro5_violations','N/A')}\")
print(f\"QED        : {props.get('qed_weighted','N/A')}\")
"
```

### 2 — Drug-Likeness Calculation (Lipinski Ro5 + Veber)

Assess any molecule against established oral bioavailability rules using
PubChem's free property API — no RDKit install needed.

```bash
COMPOUND="$1"
ENCODED=$(python3 -c "import urllib.parse,sys; print(urllib.parse.quote(sys.argv[1]))" "$COMPOUND")
curl -s "https://pubchem.ncbi.nlm.nih.gov/rest/pug/compound/name/${ENCODED}/property/MolecularWeight,XLogP,HBondDonorCount,HBondAcceptorCount,RotatableBondCount,TPSA,InChIKey/JSON" \
  | python3 -c "
import json,sys
data=json.load(sys.stdin)
props=data['PropertyTable']['Properties'][0]
mw   = float(props.get('MolecularWeight', 0))
logp = float(props.get('XLogP', 0))
hbd  = int(props.get('HBondDonorCount', 0))
hba  = int(props.get('HBondAcceptorCount', 0))
rot  = int(props.get('RotatableBondCount', 0))
tpsa = float(props.get('TPSA', 0))
print('=== Lipinski Rule of Five (Ro5) ===')
print(f'  MW   {mw:.1f} Da    {\"✓\" if mw<=500 else \"✗ VIOLATION (>500)\"}')
print(f'  LogP {logp:.2f}       {\"✓\" if logp<=5 else \"✗ VIOLATION (>5)\"}')
print(f'  HBD  {hbd}           {\"✓\" if hbd<=5 else \"✗ VIOLATION (>5)\"}')
print(f'  HBA  {hba}           {\"✓\" if hba<=10 else \"✗ VIOLATION (>10)\"}')
viol = sum([mw>500, logp>5, hbd>5, hba>10])
print(f'  Violations: {viol}/4  {\"→ Likely orally bioavailable\" if viol<=1 else \"→ Poor oral bioavailability predicted\"}')
print()
print('=== Veber Oral Bioavailability Rules ===')
print(f'  TPSA         {tpsa:.1f} Ų   {\"✓\" if tpsa<=140 else \"✗ VIOLATION (>140)\"}')
print(f'  Rot. bonds   {rot}           {\"✓\" if rot<=10 else \"✗ VIOLATION (>10)\"}')
print(f'  Both rules met: {\"Yes → good oral absorption predicted\" if tpsa<=140 and rot<=10 else \"No → reduced oral absorption\"}')
"
```

### 3 — Drug Interaction & Safety Lookup (OpenFDA)

```bash
DRUG="$1"
ENCODED=$(python3 -c "import urllib.parse,sys; print(urllib.parse.quote(sys.argv[1]))" "$DRUG")
curl -s "https://api.fda.gov/drug/label.json?search=drug_interactions:\"${ENCODED}\"&limit=3" \
  | python3 -c "
import json,sys
data=json.load(sys.stdin)
results=data.get('results',[])
if not results:
    print('No interaction data found in FDA labels.')
    sys.exit()
for r in results[:2]:
    brand=r.get('openfda',{}).get('brand_name',['Unknown'])[0]
    generic=r.get('openfda',{}).get('generic_name',['Unknown'])[0]
    interactions=r.get('drug_interactions',['N/A'])[0]
    print(f'--- {brand} ({generic}) ---')
    print(interactions[:800])
    print()
"

From Tommy-yw/RunbookHermes, MIT.

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