The proposed model can be solved by a simulation-based optimization algorithm that contains three major steps: characterization of the UE solution set, random sampling over the solution set, and a two-phase simulation optimization step.
In the optimization step, we model the target area based on the pre-measured location and allocate multiple particles into the area to mine confident candidates.
The addition of reactions to the model is guided by an optimization step minimizing the number of reactions.
We show that experiments with good candidate compounds would out-select them in the rodent model during preclinical optimization steps.
The full models including stepwise covariate elimination steps and model optimization transformation can be found in the supplementary files.
Hence, the most significant factors were subjected to further optimization step using Box Behnken model.
A raloxifene-guided optimization step of the model previously developed in our laboratory produced significant changes at the level of volume and area of the binding pocket, which were attributed to the different size and shape of raloxifene compared with the planar and symmetric TCDD scaffold.
The final values of the model states and parameters of the current optimization step are used as initial values for the next optimization step.
Fleck et al. [13] have modified the PFAAM model by adding two different dynamic models to deal with occlusions and a local optimization step.
