Half of drug candidates fail due to ADMET issues—toxicity, undesirable metabolism, poor absorption. Each such molecule consumes millions of dollars in preclinical stages. We develop AI systems that predict ADMET with accuracy sufficient for molecule selection before synthesis. According to Nature Reviews Drug Discovery, about 50% of failures are linked to ADMET.
Why Predicting ADMET Early Is Critical
Late detection of ADMET problems is a major cause of clinical trial failures, so early ADMET prediction is crucial. For instance, hERG channel blockade leads to lethal arrhythmias and drug withdrawal from the market. Our ADMET prediction model, trained on thousands of compounds, identifies such risks in seconds. The prediction accuracy for hERG inhibition exceeds 85% ROC-AUC. A typical ADMET model implementation project saves significant preclinical research costs and reduces the development cycle by 12–18 months. This translates to cost savings of $500,000–$2 million.
Which Models Perform Best for ADMET?
Graph Neural Networks (MPNN, D-MPNN) are the current standard. GNNs surpass fingerprint+ML by 15–20% in ROC-AUC on Therapeutics Data Commons (TDC) benchmarks. For small datasets, XGBoost on ECFP4 fingerprints works well—faster and more interpretable. We combine both approaches in an ensemble.
| Approach | Accuracy (ROC-AUC) | Interpretability | Inference Speed |
|---|---|---|---|
| ECFP + XGBoost | 0.75–0.82 | High (SHAP) | <1 ms per molecule |
| GNN | 0.83–0.92 | Medium (attention) | 2–5 ms per molecule |
| Multitask GNN | 0.85–0.94 | Medium | 2–5 ms for all tasks |
Multitask learning combines 20+ ADMET tasks in one model. Shared representations improve prediction for properties with limited data. For example, the model trains on solubility, logP, and hERG simultaneously.
from chemprop import args, data, featurizers, models, train
# Chemprop — state-of-the-art for molecular ADMET
arguments = [
'--data_path', 'admet_train.csv',
'--dataset_type', 'regression',
'--target_columns', 'solubility logP hERG_inhibition caco2_permeability',
'--smiles_columns', 'smiles',
'--epochs', '50',
'--batch_size', '64',
'--ffn_num_layers', '3',
'--dropout', '0.1',
'--save_dir', 'admet_model',
]
args.parse_train_args(arguments)
train.cross_validate(...)
Beyond ROC-AUC, we use PR-AUC, F1-score, and calibration coefficient (Expected Calibration Error). For regression tasks, we use RMSE and R².
Improving CYP450 Metabolism Prediction Accuracy
Predicting metabolism by CYP450 enzyme family—one of the hardest ADMET tasks. Isoforms CYP3A4, CYP2D6, CYP2C9 metabolize most drugs. To boost accuracy, we use multitask learning with molecular docking descriptors. This model achieves F1-score 0.88 on the test set, 8% better than single-task alternative.
| Uncertainty Estimation Method | Interval Coverage | Computational Cost |
|---|---|---|
| Deep Ensembles | 95% coverage | High (5 models) |
| Conformal Prediction | 90% coverage | Low (after calibration) |
| MC Dropout | 85% coverage | Medium (50 forward passes) |
Prediction Uncertainty Assessment
A model may be unreliable for molecules far from the training distribution. We use Conformal Prediction—a method giving statistically rigorous prediction intervals without distribution assumptions. When outside the applicability domain, the system issues a clear "low confidence prediction" warning. We apply several applicability domain methods: Tanimoto similarity to nearest neighbors, leverage (Williams plot), and distance to k-NN in embedding space.
What's Included (Deliverables)
- Trained model (ONNX or TorchScript format)
- REST API with OpenAPI documentation
- Comprehensive documentation (user guide, API reference)
- API access credentials
- Report with metrics (ROC-AUC, PR-AUC, calibration)
- Analysis of applicability domain and uncertainty
- Team training on model usage
- 3 months of warranty support
How We Work
- Requirements analysis: define target ADMET properties, collect and clean datasets.
- Modeling: experiment with architectures, tune hyperparameters (Weights & Biases). We use 5-fold cross-validation and external test sets.
- Validation: cross-validation, testing on held-out sets, external benchmark verification.
- Integration: deploy on your infrastructure (SageMaker, Vertex AI) or on-premise.
- Handover: code, model, documentation, training.
Timelines: from 3 weeks for a single task to 3 months for a full multitask system. Pricing is determined individually—contact us for a commercial proposal.
Our Expertise
5+ years of experience in AI for drug discovery, over 50 completed projects in molecular modeling. We guarantee quality and on-time delivery. We use a modern stack: PyTorch, Hugging Face Transformers, Chemprop, RDKit, Weights & Biases. Request a free analysis of your data—we will check its suitability for ADMET modeling and propose an optimal architecture. Get a consultation right now by sending a request through the form on our website.