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 Symbolic Regression methods. Our computational framework
demonstrates the potential for closed-form model discovery in pharmacometric
applications, addressing the labor-intensive nature of traditional model
derivation. With the increasing availability of large datasets, this framework
holds the potential to significantly enhance model-informed drug discovery.

本研究引入了 PKINNs，这是一种全新的纯数据驱动药代动力学信息网络模型，通过符号回归方法，它能高效地发现和建模基于多室的药代动力学结构，并可靠地预测其衍生物。我们的计算框架展示了在药物代谢动力学应用中发现闭合形式模型的潜力，从而解决了传统模型推导的劳动密集性问题，随着大型数据集的日益丰富，该框架有望显著增强基于模型的药物发现。