Admet Prediction

ADMET Prediction

Potency gets a compound into a programme; ADMET decides whether it survives one. ADMET-AI is a Chemprop-RDKit graph network trained on 41 Therapeutics Data Commons datasets, tops the TDC ADMET leaderboard, and runs thousands of molecules a minute on a CPU. This skill is about reading its output as a developability verdict rather than a wall of numbers.

Tool: ADMET-AI 2.0.1, MIT, pip install admet-ai (requires Python 3.11+). Weights download on first use. No GPU needed. Checked against: PyPI 2.0.1, February 2026.

Read [references/running-admet-ai.md](references/running-admet-ai.md) before your first run, [references/endpoints.md](references/endpoints.md) to know which endpoints actually stop programmes, and [references/interpreting-predictions.md](references/interpreting-predictions.md) before acting on a number — that one is judgement, not syntax.

The two scripts

| Script | Answers | |---|---| | admet_batch.py | How do I feed a library in without wasting the run? | | admet_report.py | Which of these compounds has a liability worth acting on? |

Rank within a series; do not trust absolute values

This is the thing to get right. A public model has systematic offsets against your assay — different protocol, different lab, different chemistry. Within a congeneric series those offsets are largely shared, so the ordering survives even where the values do not.

Use predictions to decide which twenty of these hundred to make and assay. Do not use them to decide whether this compound will pass. A predicted hERG of 0.7 versus 0.3 within a series is a real signal; 0.7 in absolute terms is not a measurement.

The percentile column is the point

ADMET-AI reports every prediction against the distribution of approved drugs in DrugBank, in _drugbank_approved_percentile. It is the most useful thing the tool adds over a bare model and the column most often ignored.

"Predicted clearance 12" is hard to act on. "More extreme than 92% of approved drugs" prompts the right question: drugs exist out here, but not many — what is the argument that this one works?

Flagging a set

python skills/admet-prediction/scripts/admet_report.py report --csv predictions.csv

smiles                              liabilities  flagged                                             out_of_domain
c1ccccc1CCNC(=O)c1ccc(Cl)cc1        5            hERG|DILI|Solubility_AqSolDB|Lipophilicity|Half_Life
CCO                                 0                                                                molecular_weight=46.07 outside [150, 700]

Each endpoint is flagged against its own direction — high solubility is good, high clearance is bad, high hERG is very bad — so a single summed "score" over the columns would be meaningless. admet_report.py endpoints prints the full registry with thresholds; they are this skill's conventions and are meant to be argued with.

Note the second row. Ethanol is flagged as out of domain, not clean. ADMET-AI reports no applicability domain, so a prediction on anything unlike its training data arrives with the same confident four decimal places as a reliable one.

BBB penetration has no liability direction — essential for a CNS target, a liability everywhere else. The script leaves it unflagged rather than guessing your programme.

Preparing input

python skills/admet-prediction/scripts/admet_batch.py prepare --smiles library.smi --out-dir admet_in

# 3 input, 2 unique (1 duplicates collapsed), 1 chunk(s)
# warning: 1 SMILES contain `.` -- a salt, mixture, or counterion.
admet_predict --smiles_path admet_in/chunk_0000.csv --save_path admet_in/chunk_0000_pred.csv --smiles_column smiles

Three things this prevents. ADMET-AI needs a CSV with a header — a bare .smi list silently loses its first molecule. Duplicates cost twice and add nothing, since the model is deterministic. And a . in a SMILES is a salt or mixture: the model predicts on the string as given, so the answer describes the wrong species. Desalt with datamol first.

Four ways predictions mislead

1. Classification outputs are probabilities, not classes. hERG at 0.55 is a coin flip. Move

the threshold with the cost of being wrong — screen hERG at 0.3, not 0.5.

1. Endpoints are not equally trustworthy. Lipophilicity and solubility are well predicted;

DILI, clearance, and Vd are barely better than a coin flip. The leaderboard's average rank hides that.

1. Real liabilities are simply absent. Time-dependent CYP inhibition, reactive metabolites,

transporters beyond Pgp, phospholipidosis, mitochondrial toxicity — none are covered.

1. Over-filtering early is the expensive mistake. Most ADMET liabilities are fixable by

medicinal chemistry; poor potency and a wrong target are not. Filtering a primary screen on predicted DILI discards real chemistry on the basis of noise.

When to stop using this

If your project has more than a few hundred measured compounds for an endpoint, train a Chemprop model on your own data — the applicability domain finally matches your chemistry, and it will beat any public model on it. For time-dependent CYP inhibition, transporters, or reactive metabolites, there is no model; run the assay.

Composing with the rest of the bundle

• medchem → before: structural alerts and PAINS cost nothing and catch much of this first.
• rdkit / datamol → before: desalt and standardise, or you predict on the wrong species.
• chemical-space → before: this is a good filter stage in an ultra-large cascade.
• pkpd-translation → after: predicted clearance, half-life, and PPB become dose projections.
• deepchem / pytdc → instead: when you want to train on your own data rather than use a

ready-made model.

Reporting results honestly

Give the percentile beside the value. Name the thresholds used and say they are conventions. State whether the molecule sits inside a drug-like property window. Never write "this compound is a hERG blocker" from a prediction — write "predicted hERG 0.82, above the 90th percentile of approved drugs; assay before progressing". Say what the predictions decided: they choose what to assay, they do not replace it.

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: K-Dense-AI
• Source: K-Dense-AI/drug-discovery-agent-skills
• License: MIT
• Homepage: www.k-dense.ai

Install and usage instructions live in the source repository linked above.