Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub · AI
Pharmaclaw Pharmacology Agent
Pharmacology agent for ADME/PK profiling of drug candidates from SMILES. Computes drug-likeness (Lipinski Ro5, Veber rules), QED, SA Score, ADME predictions...
skill openclawclawhub Free
Pharmacology agent for ADME/PK profiling of drug candidates from SMILES. Computes drug-likeness (Lipinski Ro5, Veber rules), QED, SA Score, ADME predictions...
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Install for OpenClaw
Quick setup
- Download the package from Yavira.
- Extract the archive and review SKILL.md first.
- Import or place the package into your OpenClaw setup.
Package facts
- Download mode
- Yavira redirect
- Package format
- ZIP package
- Source platform
- Tencent SkillHub
- What's included
- scripts/chain_entry.py, references/api_reference.md, SKILL.md
Validation
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- Review SKILL.md after the package is downloaded.
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Install with your agent
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Trust & source
Release facts
- Source
- Tencent SkillHub
- Verification
- Indexed source record
- Version
- 1.1.0
Provenance
- Publisher
- Cheminem
- Source page
- View original listing
- Canonical URL
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Documentation
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/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
PropertyMethodThresholdsBBB permeabilityClark's rules (TPSA/logP)TPSA<60+logP 1-3 = high; TPSA<90 = moderateSolubilityESOL approximationlogS > -2 high; > -4 moderate; else lowGI absorptionEgan egg modellogP<5.6 and TPSA<131.6 = highCYP3A4 inhibitionRule-basedlogP>3 and MW>300 = high riskP-gp substrateRule-basedMW>400 and HBD>2 = likelyPlasma protein bindinglogP correlationlogP>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+: MoleculeMWlogPLipinskiKey FindingsCaffeine194.08-1.03✅ Pass (0 violations)High solubility, moderate BBB, QED 0.54Aspirin180.041.31✅ Pass (0 violations)Moderate solubility, SA 1.58 (easy), QED 0.55Sotorasib560.234.48✅ Pass (1 violation: MW)Low solubility, CYP3A4 risk, high PPBMetformin129.10-1.03✅ Pass (0 violations)High solubility, low BBB, QED 0.25Invalid SMILES———Graceful JSON errorEmpty 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
Category context
Agent frameworks, memory systems, reasoning layers, and model-native orchestration.
Source: Tencent SkillHub
Largest current source with strong distribution and engagement signals.
Package contents
Included in package
2 Docs1 Scripts
- SKILL.md
- references/api_reference.md
- scripts/chain_entry.py