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 | 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) | DT + descriptors | None | Web service | Toxicity only | | 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.

ADMETlab 3.0 API

The current standard for free ADMET prediction. 119 endpoints across 6 categories; per-prediction uncertainty.

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 endpoints: Absorption (Caco-2, HIA, Pgp), Distribution (BBB+, PPB, VDss), Metabolism (CYP1A2/2C9/2C19/2D6/3A4), Excretion (CL, T1/2), Toxicity (hERG, AMES, hepatotoxicity), Drug-likeness (Lipinski, Veber, QED).

hERG Cardiotoxicity (Gold Standard Endpoint)

hERG blockade causes QT prolongation and is the #1 reason for late-stage drug attrition.

Triangulation: For hERG, use ADMETlab + ProTox + literature. A single-model probability > 0.5 is NOT a kill signal.

Lipinski / Veber / Drug-Likeness Rules

from rdkit.Chem import Descriptors, Lipinski, QED

def druglike_score(mol):
    return {
        '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': round(QED.qed(mol), 2),
    }

References

• Fu et al., Nucleic Acids Res. 52:W422 -- ADMETlab 3.0.
• Liu et al., 2024 -- hERG ML benchmarks.
• Lipinski et al., Adv. Drug Deliv. Rev. -- Rule of 5.
• Bickerton et al., Nat. Chem. 4:90 -- QED.

Related Skills

• chemoinformatics/molecular-descriptors - Physicochemical descriptors
• chemoinformatics/substructure-search - PAINS / BRENK / REOS
• chemoinformatics/qsar-modeling - In-house ADMET model training"