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Compartment models of cell culture are widely used in cytology, pharmacology,toxicology and other fields. Numerical simulation, data modeling and predictionof compartment models can be realized by traditional differential equationmodeling methods. At the same time, with the development of software andhardware, Physical Informed Neural Network (PINN) is widely used to solvedifferential equation models. This work models, simulates and predicts the cellculture compartment model based on the machine learning framework PyTorch withan 16 hidden layers neural network, including 8 linear layers and 8 feedbackactive layers. The results showed a loss value of 0.0004853 for three-componentfour-parameter quantitative pharmacodynamic model predictions in this way,which is evaluated by Mean Square Error (MSE). In summary, Physical InformedNeural Network can serve as an effective tool to deal with cell culturecompartment models and may perform better in dealing with big datasets.

Simulation, Modeling and Prediction of a Pharmacodynamic Animal Tissue Culture Compartment Model by Physical Informed Neural Network