Pharma Pharmacology Agent v2.0.0

---

name: pharmaclaw-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 v2.0.0

Overview

Predictive pharmacology profiling for drug candidates. Combines ADMETlab 3.0 ML predictions (when available) with comprehensive RDKit descriptor-based models. Provides full ADME assessment, toxicity risk, druglikeness scoring, and risk flagging — all from a SMILES string.

**Key capabilities:**

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

`scripts/admetlab3.py`

Enhanced ADME/Tox predictor. Attempts ADMETlab 3.0 API first, falls back to comprehensive RDKit models.

# Full ADME profile
python scripts/admetlab3.py --smiles "CC(=O)Oc1ccccc1C(=O)O"

# Specific categories
python scripts/admetlab3.py --smiles "CN1C=NC2=C1C(=O)N(C(=O)N2C)C" --categories absorption,toxicity

**Output includes:**

Resources

Changelog

**v2.0.0** (2026-02-18)

**v1.1.0** (2026-02-14)