Model-ready data, curated end to end
Models can be trained and validated using curated public, licensed, and proprietary datasets — standardized and quality-controlled before a single parameter is fit.
Data standardization
Structures, units, and assay conditions are normalized to a consistent schema before modeling.
Assay harmonization
Results from heterogeneous assay formats and protocols are reconciled onto comparable scales.
Quality control
Automated and expert review filters outliers, duplicates, and low-confidence measurements.
Train / validation / test separation
Scaffold-aware splitting prevents structural leakage and overstated performance.
Model-ready datasets
Curated datasets are versioned and packaged for direct use in training and benchmarking.
Working with your own data
Organizations with proprietary assay data can work with DMPK.AI to harmonize internal measurements onto the same standardized schema used across our public and licensed datasets, then fine-tune or train new endpoint-specific models.
All training data is split by chemical scaffold — not by random row — so reported validation performance reflects generalization to new chemical matter, not memorization of near-duplicate structures.