使用物理信息神经网络发现内在多房室药动学模型
Discovering intrinsic multi-compartment pharmacometric models using Physics Informed Neural Networks
AI总结：
本文提出纯数据驱动的药动学信息神经网络PKINNs，用于自动发现多房室药动学模型结构并预测导数，结合符号回归实现可解释的闭式模型发现，有望提升模型指导的药物发现效率。
摘要：
药动学模型在药物发现与开发中至关重要，对候选分子的推进起着决定性作用。然而，推导控制系统行为的数学方程是一个劳动密集型的试错过程，且常受紧迫时间限制。本研究提出PKINNs，一种全新的纯数据驱动、融合药动学信息的神经网络模型。PKINNs能高效发现并建模基于多房室的内在药动学结构，并可靠预测其导数。所得模型通过符号回归方法兼具可解释性与可说明性。我们的计算框架展示了在药动学应用中进行闭式模型发现的潜力，解决了传统模型推导劳动密集的问题。随着大规模数据集日益增多，该框架有望显著增强模型指导的药物发现。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
