# Admet Prediction > Turn a set of structures into absorption, distribution, metabolism, excretion, and toxicity estimates with ADMET-AI, and read them as a developability verdict rather than a table of numbers. Use this skill to run batch prediction over a library, interpret each endpoint against its DrugBank-approved percentile, and flag the liabilities that stop a series — hERG blockade, CYP inhibition, poor Caco-2 permeability, high clearance, and plasma protein binding. Also trigger on ADMET-AI, admet_ai, Chemprop-RDKit, hERG liability, CYP3A4 inhibition, Caco-2, bioavailability prediction, or developability triage. - Skill: `k-dense-ai/admet-prediction` (Agent Skill, multi-file: 6 files) - Install (CLI): `npx skillmds@latest add k-dense-ai/admet-prediction` - Raw SKILL.md: https://api.skillmd.com/api/skills/k-dense-ai/admet-prediction/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Coding & Dev Tools - License: MIT - Author: K-Dense AI (https://skillmd.com/u/k-dense-ai) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/k-dense-ai/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](https://github.com/swansonk14/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 ```bash 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 ```bash 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. 2. **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. 3. **Real liabilities are simply absent.** Time-dependent CYP inhibition, reactive metabolites, transporters beyond Pgp, phospholipidosis, mitochondrial toxicity — none are covered. 4. **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.