AI/ML Pharmacokinetic Modeling
Upload your PK data and compare traditional compartmental models against modern AI/ML approaches — Neural ODEs, XGBoost, and Gaussian Process regression — all in one analysis. See which approach best captures your drug's pharmacokinetics.
Traditional Compartmental
1-CMT and 2-CMT models with first-order absorption. The gold standard.
scipy.optimizeNeural ODE
A neural network learns the ODE system governing PK dynamics directly from data.
PyTorch + torchdiffeqML Ensemble
XGBoost for feature-driven prediction + Gaussian Process for uncertainty quantification.
XGBoost + scikit-learnUpload PK Data
How It Works
Upload Your Data
Upload a CSV with time and concentration columns. Additional columns (dose, subject ID, covariates) improve the analysis. Standard NONMEM-style datasets work directly.
Automated Model Fitting
We fit 4 models simultaneously: 1-compartment, 2-compartment, Neural ODE (learning PK dynamics from scratch), and an XGBoost + Gaussian Process ensemble.
Compare & Interpret
Side-by-side comparison of all approaches with RMSE, R², AIC, diagnostic plots, and model-specific insights. Understand when AI/ML adds value for your data.
Expected CSV Format
ID,time,conc,dose 1,0.5,2.341,500 1,1.0,5.672,500 1,2.0,8.123,500 ...
Column names are flexible — we auto-detect time (t, time, hours), concentration (conc, dv, cp, c), dose (amt, dose), and subject ID (id, subject). Case-insensitive.