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