Predicting stochastic cellular dynamics emerging from chemical reaction networks (CRNs) is a long-standing challenge in systems biology. Deep learning was recently used to abstract the CRN by mixture density neural network, trained with traces of original process. Such abstraction dramatically cheaper execute, yet it preserves statistical features training data. However, practice, modeller has ...