To implement this set-based methodology, we adapted the DeepSets architecture proposed by Zaheer et al. , which is specifically designed to process sets with permutation-invariance. This allowed the model to robustly handle varying numbers of reactions and unordered inputs, ensuring flexibility and scalability in route evaluation. Formally, let X = {x 1 , x 2 , . . . , x n } represent the set of reaction features of n reactions in one route. Each reaction i is represented by a feature vector