🎁 Get the FREE AI Skills Starter Guide β€” Subscribe β†’
BytesAgainBytesAgain
BytesAgain β€Ί Skills β€Ί Pharmaclaw Pharmacology Agent
πŸ¦€ ClawHub

Pharmaclaw Pharmacology Agent

by @cheminem

Pharmacology agent for ADME/PK profiling of drug candidates from SMILES. Computes drug-likeness (Lipinski Ro5, Veber rules), QED, SA Score, ADME predictions...

Versionv1.1.0
Downloads1,704
Stars⭐ 2
#legal
TERMINAL
clawhub install pharma-pharmacology-agent

πŸ“– About This Skill

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 β€” 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": "", "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": "",
  "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 | βœ… Pass (0 violations) | High solubility, moderate BBB, QED 0.54 | | Aspirin | 180.04 | 1.31 | βœ… Pass (0 violations) | Moderate solubility, SA 1.58 (easy), QED 0.55 | | Sotorasib | 560.23 | 4.48 | βœ… Pass (1 violation: MW) | Low solubility, CYP3A4 risk, high PPB | | Metformin | 129.10 | -1.03 | βœ… Pass (0 violations) | High solubility, low BBB, QED 0.25 | | Invalid SMILES | β€” | β€” | β€” | Graceful JSON error | | Empty input | β€” | β€” | β€” | 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 β€” 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

    • πŸ’‘ Examples

    # 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": "", "context": "from_chemistry"}'