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

# Profile a molecule from SMILES
exec python scripts/chain_entry.py --input-json '{"smiles": "CC(=O)Oc1ccccc1C(=O)O", "context": "user"}'

# Chain from chemistry-query output
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},
    "lipinski": {"pass": true, "violations": 0, "details": {...}},
    "veber": {"pass": true, "tpsa": {...}, "rotatable_bonds": {...}},
    "qed": 0.5385,
    "sa_score": 2.3,
    "adme": {
      "bbb": {"prediction": "moderate", "confidence": "medium", "rationale": "..."},
      "solubility": {"logS_estimate": -1.87, "class": "high", "rationale": "..."},
      "gi_absorption": {"prediction": "high", "rationale": "..."},
      "cyp3a4_inhibition": {"risk": "low", "rationale": "..."},
      "pgp_substrate": {"prediction": "unlikely", "rationale": "..."},
      "plasma_protein_binding": {"prediction": "moderate-low", "rationale": "..."}
    },
    "pains": {"alert": false}
  },
  "risks": [],
  "recommend_next": ["toxicology", "ip-expansion"],
  "confidence": 0.85,
  "warnings": [],
  "timestamp": "ISO8601"
}

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