| name | bio-admet-prediction |
| description | Predicts ADMET properties using ADMETlab 3.0 (119 endpoints with uncertainty), ADMET-AI, DeepChem MolNet, and chemprop D-MPNN with explicit handling of OECD QSAR principles, applicability domain assessment, calibration, hERG/CYP/AMES gold-standard endpoints, and PAINS / Lipinski / Ro5 / Veber / BBB druglikeness filters. Use when filtering compounds for drug-likeness, prioritizing leads by predicted safety, or building an in-house ADMET QSAR model. |
| tool_type | python |
| primary_tool | ADMETlab |
Version Compatibility
Reference examples tested with: RDKit 2024.09+, requests 2.31+, DeepChem 2.8+, chemprop 2.0+ (note major API change from 1.x), admet-ai 1.3+, pandas 2.2+.
Before using code patterns, verify installed versions match. If versions differ:
- Python:
pip show <package> then help(module.function) to check signatures
If code throws ImportError, AttributeError, or TypeError, introspect the installed
package and adapt the example to match the actual API rather than retrying.
ADMET Prediction
Predict absorption, distribution, metabolism, excretion, and toxicity properties of drug candidates. ADMET prediction underpins lead selection and de-risking; calibrated, applicability-domain-aware predictions distinguish a working filter from a costly false-confidence rejection. Modern best practice combines online services (ADMETlab 3.0 with uncertainty estimates), open-source models (chemprop D-MPNN), and rule-based filters (Lipinski / Veber / BBB heuristics) -- each with known failure modes.
For PAINS / Brenk / structural alerts, see chemoinformatics/substructure-search. For QSAR model building from in-house data, see chemoinformatics/qsar-modeling.
ADMET Model Taxonomy
| Tool | Endpoints | Architecture | Uncertainty | Access | Fails when |
|---|
| ADMETlab 3.0 | 119 (A,D,M,E,T + physchem + medchem) | Multi-task DMPNN + descriptors | Per-prediction | REST API (free, no auth) | Outside training distribution; metals; macrocycles |
| ADMET-AI (NVIDIA) | ~50 (focus on safety) | chemprop D-MPNN | Ensemble variance | Python package | Limited endpoints vs ADMETlab |
| DeepChem MolNet | ~30 (tox21, ToxCast, ClinTox) | Various GCN/GAT | Per-task variance | Python package | Models trained on small datasets |
| pkCSM | ~30 (absorption, distribution, toxicity) | Graph signatures + RF | None | Web service | Smaller training data |
| SwissADME | ~30 (filters + physchem) | Hand-curated rules | None | Web service (NO API) | Cannot batch programmatically |
| ProTox-3.0 | ~46 (toxicity end-points) | DT + descriptors | None | Web service | Toxicity only; LD50 categorical |
| ADMETpredictor (Simulations Plus) | ~140 | Proprietary | Per-prediction | Commercial | License cost |
| FAF-Drugs4 | filters | Rule-based | None | Web | Static rules |
| chemprop (in-house) | User-defined | D-MPNN ± descriptors | Bayesian ensemble | Python package | Requires training data |
Decision: For batch screening of <10k compounds with no in-house data, ADMETlab 3.0 (free API, 119 endpoints, calibrated uncertainty) is the modern standard. For in-house QSAR on a specific endpoint with >500 measurements, train a chemprop D-MPNN + Morgan + MOE descriptors model (Liu et al. 2024 achieved AUC 0.956 on hERG with this combo).
Decision Tree by Scenario
| Scenario | Workflow | Reasoning |
|---|
| Library triage, no in-house data | ADMETlab 3.0 API batch | Calibrated 119 endpoints |
| Single target, large in-house data (>500 datapoints) | chemprop D-MPNN ensemble | Beats generic models on target-specific data |
| Need calibrated probabilities | chemprop with ensemble + Platt | Native deep learning rarely calibrated |
| FDA / regulatory submission | OECD-compliant QSAR with AD | See OECD principles below |
| Quick filter for VS | Lipinski + Veber + QED >= 0.5 | Rule-based, no model needed |
| BBB penetration | TPSA <= 90, MW <= 500, HBD <= 3 (Pfizer CNS) | Wager 2010 |
| Cardiotox liability | hERG model (ADMETlab + ProTox + literature lit-check) | Triangulate; hERG critical |
| Drug-drug interaction (CYP) | CYP1A2/2C9/2C19/2D6/3A4 inhibitor + substrate | Standard set of 5 CYPs |
OECD QSAR Principles (5 Pillars)
For regulatory-grade ADMET QSAR (REACH, ECHA, FDA submissions), models must satisfy:
- Defined endpoint -- specific bioassay, units, conditions
- Unambiguous algorithm -- reproducible model + code
- Defined applicability domain (AD) -- where the model is valid
- Appropriate statistical validation -- external test set, cross-validation
- Mechanistic interpretation -- biological / chemical rationale
For non-regulatory work, AD assessment is still critical. The OECD's applicability domain is the workhorse: predictions outside the AD are unreliable, but operational AD measures (leverage, kNN, conformal prediction) often disagree.
Applicability Domain Methods
| Method | Definition | Flags out-of-AD when |
|---|
| kNN distance | Mean distance to k nearest neighbors in training set | > training-set distribution P95 |
| Leverage (Williams) | Hat-matrix diagonal | > 3p/n (p = features, n = compounds) |
| Density (KDE on PCA) | Density in feature space | < density of training set P5 |
| Conformal prediction | Per-prediction confidence interval | Interval > tolerance |
| Bayesian variance | Ensemble or MC-dropout variance | > training-set variance P95 |
For deep-learning ADMET, conformal prediction (Bostrom et al. 2024) is becoming the standard.
ADMETlab 3.0 API
The current standard for free ADMET prediction. 119 endpoints across 6 categories; per-prediction uncertainty.
Goal: Predict 119 ADMET endpoints with uncertainty for a batch of SMILES using a hosted API.
Approach: POST batches of <=500 SMILES to ADMETlab 3.0 REST endpoint and parse the returned JSON into a per-compound endpoint DataFrame.
import requests
import pandas as pd
def admetlab_predict(smiles_list, endpoint='admet'):
url = f'https://admetlab3.scbdd.com/api/{endpoint}'
payload = {'smiles': smiles_list}
response = requests.post(url, json=payload, timeout=120)
response.raise_for_status()
return pd.DataFrame(response.json())
smiles = ['CCO', 'c1ccc(C(=O)O)cc1', 'CC(=O)Oc1ccccc1C(=O)O']
results = admetlab_predict(smiles)
ADMETlab 3.0 endpoints (sample):
- Absorption: Caco-2 permeability (logPapp), HIA (%), Pgp inhibitor/substrate, MDCK
- Distribution: BBB+, PPB (%), VDss (L/kg), Fu (fraction unbound)
- Metabolism: CYP1A2/2C9/2C19/2D6/3A4 inhibitor / substrate
- Excretion: CL (mL/min/kg), T1/2 (h)
- Toxicity: hERG, AMES, hepatotoxicity (DILI), carcinogenicity, immunotoxicity, mutagenicity, respiratory, skin, eye, cardiotoxicity, mitochondrial, NR-AR, NR-ER, SR-MMP
- Drug-likeness: Lipinski, Veber, Ghose, Egan, Muegge, QED, SAscore
chemprop D-MPNN for Custom Endpoints
When in-house data is available, train a target-specific model. chemprop's D-MPNN architecture + atom/bond features + optional Morgan / MOE descriptors is the modern open-source SOTA.
Goal: Train a target-specific ADMET classifier or regressor on in-house bioassay data.
Approach: Use chemprop 2.x CLI with rdkit_2d_normalized descriptor features, 5-fold scaffold-balanced split, and 5-model ensemble for uncertainty estimation.
Key: 5-fold ensemble for calibrated uncertainty; D-MPNN + rdkit_2d_normalized typically outperforms either alone.
hERG Cardiotoxicity (Gold Standard Endpoint)
hERG (KCNH2) blockade causes QT prolongation, Torsades de Pointes, and is the #1 reason for late-stage drug attrition. ICH S7B and FDA require non-clinical assessment.
| Model | Architecture | Training data | AUC | Reference |
|---|
| Cai et al. D-MPNN + MOE | chemprop + 206 MOE descriptors | 7,889 compounds | 0.956 | Liu 2024 |
| CardioTox-net | ECFP + RF stacking | ChEMBL hERG | 0.93 | Aniketh 2021 |
| ADMETlab 3.0 hERG | DMPNN multi-task | Internal | 0.92 (reported) | Fu 2024 |
| ProTox-3.0 | DT | ProTox training | 0.86 | Banerjee 2024 |
Postdoc-grade interpretation: A single-model probability > 0.5 is NOT a kill signal. Triangulate ADMETlab + ProTox + literature search for cardiotox liability. Consider the active concentration vs predicted hERG IC50 (10 uM threshold for "concern"); a hERG-positive compound at 100 nM active concentration may still be safe with sufficient hERG selectivity.
CYP Inhibition (DDI Risk)
5 CYP isoforms cover most clinically relevant DDIs:
| CYP | Substrates (drugs) | Inhibitor flag if predicted prob | Action |
|---|
| CYP3A4 | ~50% of drugs | > 0.5 | Often acceptable if substrate; flag if inhibitor |
| CYP2D6 | beta-blockers, antidepressants | > 0.5 | Higher concern (polymorphism) |
| CYP2C9 | warfarin, NSAIDs | > 0.5 | Caution if patient on warfarin |
| CYP2C19 | PPIs, clopidogrel | > 0.5 | Variable (polymorphism) |
| CYP1A2 | caffeine, theophylline | > 0.5 | Diet/smoking-influenced |
PAINS, BRENK, REOS Filters
ADMET prediction is separate from structural alerts; combine. See chemoinformatics/substructure-search for PAINS/BRENK/REOS pattern catalogs.
from rdkit.Chem.FilterCatalog import FilterCatalog, FilterCatalogParams
def alerts(mol, catalogs=('PAINS_A', 'BRENK', 'ZINC')):
params = FilterCatalogParams()
for cat in catalogs:
params.AddCatalog(getattr(FilterCatalogParams.FilterCatalogs, cat))
catalog = FilterCatalog(params)
hits = catalog.GetMatches(mol)
return [h.GetDescription() for h in hits]
Lipinski / Veber / Drug-Likeness
See chemoinformatics/molecular-descriptors for full physchem table. Quick filter:
from rdkit.Chem import Descriptors, Lipinski, QED
def druglike_score(mol):
mw = Descriptors.MolWt(mol)
logp = Descriptors.MolLogP(mol)
hbd = Lipinski.NumHDonors(mol)
hba = Lipinski.NumHAcceptors(mol)
tpsa = Descriptors.TPSA(mol)
rotbonds = Lipinski.NumRotatableBonds(mol)
qed = QED.qed(mol)
lipinski_violations = sum([mw > 500, logp > 5, hbd > 5, hba > 10])
veber_pass = rotbonds <= 10 and tpsa <= 140
bbb_pfizer = tpsa <= 90 and mw <= 500 and hbd <= 3
return {'MW': mw, 'LogP': logp, 'HBD': hbd, 'HBA': hba,
'TPSA': tpsa, 'RotBonds': rotbonds, 'QED': qed,
'Lipinski_violations': lipinski_violations,
'Veber_pass': veber_pass, 'BBB_pfizer': bbb_pfizer}
Per-Tool Failure Modes
ADMETlab 3.0 -- out-of-distribution prediction
Trigger: Macrocycle, metal-coordinated, oligomer (peptide >5 AA), or PROTAC submitted.
Mechanism: ADMETlab training set is drug-like organic molecules. Predictions on PROTACs, macrocycles, peptides extrapolate.
Symptom: Uncertainty estimates near max; ambiguous probabilities (0.4-0.6).
Fix: Check uncertainty band; if interval is broad, do not trust point estimate. For PROTACs / macrocycles, prefer literature-derived experimental data.
hERG D-MPNN -- training data bias
Trigger: Compound is novel chemotype not in training set (drug-like but in unexplored region).
Mechanism: D-MPNN learns local chemical features; for genuinely new scaffolds, extrapolation is unreliable.
Symptom: Model predicts hERG- (false negative) for compound that experimentally inhibits.
Fix: Use ensemble + applicability-domain assessment (kNN distance, ensemble variance). If kNN distance to training set > P95, treat prediction as low-confidence.
CYP3A4 inhibitor + substrate ambiguity
Trigger: Model trained on either inhibitor OR substrate; predictions confused.
Mechanism: CYP3A4 inhibitors and substrates have similar SAR; many compounds are both.
Symptom: Both classes report > 0.5.
Fix: Two separate models (inhibitor model, substrate model); compounds that score high in both are flagged for in vitro confirmation.
SwissADME -- no API
Trigger: Wanting to batch programmatically.
Mechanism: SwissADME explicitly forbids automated access (TOS).
Symptom: No programmatic access; manual web upload only.
Fix: Use ADMETlab 3.0 API for programmatic batch (free, no TOS restrictions).
PAINS as a kill filter
Trigger: Treating PAINS_A match as a categorical exclusion.
Mechanism: PAINS is calibrated against HTS assay-interference; matches do NOT predict failed drug development.
Symptom: Library purged of valid leads (curcumin analogs, polyphenol natural products).
Fix: Flag PAINS for orthogonal-assay confirmation; do not exclude pre-emptively. See substructure-search for details.
Class-imbalanced AMES dataset
Trigger: Training/predicting AMES mutagenicity.
Mechanism: AMES public datasets have ~3:1 negative:positive imbalance; baseline F1 misleading.
Symptom: Model reports high accuracy but predicts negative for all.
Fix: Use AUC-ROC or balanced accuracy. Apply SMOTE / class-weighted loss in chemprop.
Reconciliation Across Models
When ADMETlab, ProTox, and chemprop disagree on hERG:
- All three predict hERG+ → high confidence, deprioritize
- Two predict hERG+, one hERG- → moderate confidence, plan in vitro patch-clamp
- Single positive (other two negative) → likely false positive of disagreeing model; verify chemotype is in distribution
- All three hERG- → low confidence (especially novel chemotype); still consider in vitro screen for clinical candidates
Common Errors
| Symptom | Cause | Fix |
|---|
| ADMETlab API 504 timeout | Batch too large | Submit <500 SMILES per request |
| chemprop training overfits | Random split | Use scaffold split (--split scaffold_balanced) |
| hERG prediction 50/50 | Out-of-distribution | Check applicability domain |
| QED returns nan | Stereo undefined or invalid | Standardize first |
| ProTox endpoints missing | Web scrape uses CSS selector | Use formal API |
| BBB+ true but TPSA > 90 | Different BBB model | Use Pfizer CNS heuristic for orthogonal check |
| Predictions inconsistent across runs | Random seed for chemprop ensemble | --seed 42 and reuse model |
| Calibration mismatch | DL native probabilities not calibrated | Apply Platt scaling on validation set |
References
- Fu et al., Nucleic Acids Res. 52:W422 (2024) -- ADMETlab 3.0.
- Shan W, Jiang Y, Chen J, Qin H, Qin H, Cheng F. 2022. RSC Adv 12(7):3935-3942 -- hERG ML benchmarks (DOI 10.1039/D1RA07956E).
- Heid et al., J. Chem. Inf. Model. 64:9 (2024) -- chemprop 2.0 redesign.
- OECD, "OECD Principles for the Validation of QSAR Models" (2007).
- OECD, "(Q)SAR Assessment Framework" (2023).
- Capuzzi et al., J. Chem. Inf. Model. 57:417 (2017) -- PAINS reality check.
- Wager et al., ACS Chem. Neurosci. 1:435 (2010) -- Pfizer CNS MPO.
- Bickerton et al., Nat. Chem. 4:90 (2012) -- QED.
Related Skills
- chemoinformatics/molecular-descriptors - Compute drug-likeness physchem
- chemoinformatics/substructure-search - PAINS / BRENK / REOS filter
- chemoinformatics/qsar-modeling - Build custom QSAR for in-house data
- chemoinformatics/molecular-standardization - Canonicalize before prediction
- machine-learning/biomarker-discovery - Adjacent ML approaches
- clinical-databases/pharmacogenomics - Patient genotype overlay
