Dose-time-concentration prediction method based on GRU-TCN with temporal-channel attention

This study proposes a GRU-TCN model with Temporal-Channel Attention (GT-TCA) for dose-time-concentration prediction under data scarcity and multicollinearity. TimeCVAE augments limited pharmacokinetic data with distribution-consistent sequences. GRU captures temporal dependencies, TCN extracts multi-scale features, and attention emphasizes informative time steps and analytes. Experiments on Buyang Huanwu Decoction (normal/inflammatory) and simulations (RG1678, RIF) show GT-TCA reduces MAE by 22.7% and improves R² by 4% versus baselines (p < 0.05). Ablation confirms attention lowers MAE and RMSE by 6% and 5%. The model demonstrates robustness and provides more precise quantitative evidence to support precision dosing.