Discovering intrinsic multi-compartment pharmacometric models using Physics Informed Neural Networks

arXiv:2405.00166v1 Announce Type: new
Abstract: Pharmacometric models are pivotal across drug discovery and development, playing a decisive role in determining the progression of candidate molecules. However, the derivation of mathematical equations governing the system is a labor-intensive trial-and-error process, often constrained by tight timelines. In this study, we introduce PKINNs, a novel purely data-driven pharmacokinetic-informed neural network model. PKINNs efficiently discovers and models intrinsic multi-compartment-based pharmacometric structures, reliably forecasting their derivatives. The resulting models are both interpretable and explainable through …

abstract arxiv cs.ai cs.lg derivation development discovery drug discovery drug discovery and development error however intrinsic labor mathematical equations molecules networks neural networks physics pivotal playing process q-bio.qm role study type