pharma-pharmacology-agent

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

# 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},
          ...
        ...  ...
     
     
     
            
            
          
          
          
          
    
      
  
   
    
   
   
   

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