Discovering intrinsic multi-compartment pharmacometric models using Physics Informed Neural Networks
<|reference_start|>Discovering intrinsic multi-compartment pharmacometric models using Physics Informed Neural Networks: 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.<|reference_end|>
@article{nasim2024discovering,
    title={Discovering intrinsic multi-compartment pharmacometric models using
  Physics Informed Neural Networks},
    author={Imran Nasim, Adam Nasim},
    journal={arXiv preprint arXiv:2405.00166},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.00166},
    primaryClass={cs.LG cs.AI q-bio.QM}
}