| name | pharma-pharmacology-agent |
| description | Pharmacology agent for ADME/PK profiling of drug candidates from SMILES. Computes drug-likeness (Lipinski Ro5, Veber rules), QED, SA Score, ADME predictions (BBB permeability, aqueous solubility, GI absorption, CYP3A4 inhibition, P-gp substrate, plasma protein binding), and PAINS alerts. Chains from chemistry-query for SMILES input. Triggers on pharmacology, ADME, PK/PD, drug likeness, Lipinski, absorption, distribution, metabolism, excretion, BBB, solubility, bioavailability, lead optimization, drug profiling. |
Pharma Pharmacology Agent v1.1.0
Overview
Predictive pharmacology profiling for drug candidates using RDKit descriptors and validated rule-based heuristics. Provides comprehensive ADME assessment, drug-likeness scoring, and risk flagging — all from a SMILES string.
Key capabilities:
- Drug-likeness: Lipinski Rule of Five, Veber oral bioavailability rules
- Scores: QED (Quantitative Estimate of Drug-likeness), SA Score (Synthetic Accessibility)
- ADME predictions: BBB permeability, aqueous solubility (ESOL), GI absorption (Egan), CYP3A4 inhibition risk, P-glycoprotein substrate, plasma protein binding
- Safety: PAINS (Pan-Assay Interference) filter alerts
- Risk assessment: Automated flagging of pharmacological concerns
- Standard chain output: JSON schema compatible with all downstream agents
Quick Start
exec python scripts/chain_entry.py --input-json '{"smiles": "CC(=O)Oc1ccccc1C(=O)O", "context": "user"}'
exec python scripts/chain_entry.py --input-json '{"smiles": "<canonical_smiles>", "context": "from_chemistry"}'
Scripts
scripts/chain_entry.py
Main entry point. Accepts JSON with smiles field, returns full pharmacology profile.
Input:
{"smiles": "CN1C=NC2=C1C(=O)N(C(=O)N2C)C", "context": "user"}
Output schema:
{
"agent": "pharma-pharmacology",
"version": "1.1.0",
"smiles": "<canonical>",
"status": "success|error",
"report": {
"descriptors": {"mw": 194.08, "logp": -1.03, "tpsa": 61.82, "hbd": 0, "hba": 6, "rotb": 0, "arom_rings": 2, "heavy_atoms": 14, "mr": 51.2},
...
... ...
ADME Prediction Rules
| Property | Method | Thresholds |
|---|
| BBB permeability | Clark's rules (TPSA/logP) | TPSA<60+logP 1-3 = high; TPSA<90 = moderate |
| Solubility | ESOL approximation | logS > -2 high; > -4 moderate; else low |
| GI absorption | Egan egg model | logP<5.6 and TPSA<131.6 = high |
| CYP3A4 inhibition | Rule-based | logP>3 and MW>300 = high risk |
| P-gp substrate | Rule-based | MW>400 and HBD>2 = likely |
| Plasma protein binding | logP correlation | logP>3 = high (>90%) |
Chaining
This agent is designed to receive output from chemistry-query:
chemistry-query (name→SMILES+props) → pharma-pharmacology (ADME profile) → toxicology / ip-expansion
The recommend_next field always includes ["toxicology", "ip-expansion"] for pipeline continuation.
Tested With
All features verified end-to-end with RDKit 2024.03+:
| Molecule | MW | logP | Lipinski | Key Findings |
|---|
| Caffeine | 194.08 | -1.03 | ✅ Pass (0 violations) | High solubility, moderate BBB, QED 0.54 |
| Aspirin | 180.04 | 1.31 | ✅ Pass (0 violations) | Moderate solubility, SA 1.58 (easy), QED 0.55 |
| Sotorasib | 560.23 | 4.48 | ✅ Pass (1 violation: MW) | Low solubility, CYP3A4 risk, high PPB |
| Metformin | 129.10 | -1.03 | ✅ Pass (0 violations) | High solubility, low BBB, QED 0.25 |
| Invalid SMILES | — | — | — | Graceful JSON error |
| Empty input | — | — | — | Graceful JSON error |
Error Handling
- Invalid SMILES: Returns
status: "error" with descriptive warning
- Missing input: Clear error message requesting
smiles or name
- All errors produce valid JSON (never crashes)
Resources
references/api_reference.md — API and methodology references
Changelog
v1.1.0 (2026-02-14)
- Initial production release with full ADME profiling
- Lipinski, Veber, QED, SA Score, PAINS
- BBB, solubility, GI absorption, CYP3A4, P-gp, PPB predictions
- Automated risk assessment
- Standard chain output schema
- Comprehensive error handling
- End-to-end tested with diverse molecules
