ADMET Prediction & Drug Candidate Profiling

ADMET Prediction & Drug Candidate Profiling

ADMET reasoning: a drug fails if it can't be absorbed, distributes to wrong tissues, isn't metabolized safely, or isn't excreted. Evaluate each property independently — good absorption doesn't compensate for liver toxicity. The ADME properties determine whether a compound reaches its target at therapeutic concentrations; toxicity determines whether it's safe to do so. Prioritize experimental data (T2) over computational predictions (T3) — ADMETAI predictions are screening tools, not definitive verdicts. When a FAIL is flagged in any toxicity category (hERG, AMES, DILI), treat it as program-limiting until wet-lab data refutes it.

LOOK UP DON'T GUESS: never assume SMILES, CID, or experimental LD50 values — always call PubChem to resolve compound identity before any ADMETAI or PubChemTox call.

Comprehensive pharmacokinetic and toxicity profiling integrating AI-based ADMET predictions, rule-based drug-likeness filters, and experimental benchmarks from curated databases.

When to Use This Skill

Triggers:

• "What are the ADMET properties of [compound]?"
• "Is [drug] likely to cross the blood-brain barrier?"
• "Predict the toxicity of this SMILES: ..."
• "Does [compound] violate Lipinski's rule of five?"
• "Assess the drug-likeness of [molecule]"
• "What are the CYP interactions for [drug]?"
• "Pharmacokinetic profile of [compound]"
• "Is [compound] orally bioavailable?"
• "What is the LD50 / hERG liability of [molecule]?"

Input: Drug name (e.g., "ibuprofen") OR SMILES string (e.g., "CC(C)Cc1ccc(cc1)C(C)C(=O)O")

Before You Run

ADMETAI tools run a local model, so they need the ml extra:

uv pip install 'tooluniverse[ml]'

Without it the tools still appear in tu list (the config loads) but fail at call time with ADMETModel requires 'admet-ai' package. Run tooluniverse-doctor to confirm which optional groups are installed.

Expected console noise — not errors. The first ADMETAI call loads PyTorch and prints warnings such as missing-GPU / Trainer messages from PyTorch Lightning, and TypedStorage is deprecated from PyTorch. These are emitted by the underlying libraries during normal CPU inference. Predictions are unaffected — do not report them to the user as failures and do not retry