Artificial Neural Networks in Drug Transport Modeling and Simulation-II

Sharmistha P. Chatterjee, University of North Florida
Abhijit S. Pandya, Florida Atlantic University


Artificial neural networks (ANNs) are widely applied for extracting patterns, machine learning, establishing functional relationships between inputs and outputs, and prediction of recurring phenomena based on time series. ANNs are used in pharmaceutical and pharmacokinetic areas to model complex relationships and to predict the nonlinear relationship between causal factors and response variables. The distinct features of the ANN, similar to learning and adapting, make this approach very useful in modeling drug transport mechanisms because the biological system itself dynamically changes because of external and internal parameters. In situations in which functional dependence of output is consistent, but the mathematical formulation of functional dependences between the inputs and outputs is difficult to establish, ANNs have proven to be an effective modeling paradigm.