All these studies represent an important background on how accurate, and fast surrogate models can be built from training ML architectures using PBMs datasets as inputs. However, the choice of algorithm and the fidelity of the training data will affect the interpolation capabilities of the surrogate model, and it is important to mention that all of the previously mentioned works have different tradeoffs between accuracy, training times and prediction abilities.