A key component of the model is its ability to evaluate individual reactions along a route and deal with variant length or reaction input order. Since each input represents the properties of individual reactions, the network treats the reactions as a set, and thus must be invariant to input permutations to prove consistent scores. To achieve permutation invariance, we adapt DeepSets architecture proposed by Zaheer et al. Formally, let X = {x 1 , x 2 , . . . , x n } represent the set of reaction features of n reactions in one route, where each