pharmaclaw-pharmacology-agent - SKILL.md Agent Skill

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:

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+:

|----------|-----|------|----------|-------------|

| 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)

`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:

Physicochemical: MW, LogP, TPSA, LogS (ESOL), solubility class, fraction CSP3, molar refractivity
Absorption: Lipinski, Veber, Egan, HIA, Caco-2 permeability, P-gp substrate, oral bioavailability
Distribution: BBB penetration (Clark model), plasma protein binding
Metabolism: CYP3A4 inhibition risk
Toxicity: hERG risk, Ames mutagenicity, DILI, structural alerts (nitro, aromatic amine)
Druglikeness: QED, SA Score, lead-like, drug-like classifications

Resources

references/api_reference.md — API and methodology references

Changelog

v2.0.0 (2026-02-18)

ADMETlab 3.0 integration (ML-based predictions, auto-fallback to RDKit)
Enhanced RDKit ADME: Caco-2 permeability, Egan model, HIA, hERG, Ames, DILI
Solubility via ESOL model
Lead-like / drug-like classification
Structural alerts: nitro groups, aromatic amines

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