In addition, the architecture must support multiset input, as the number of identical vector representations of atoms matters in the context of molecular sets. Given these requirements, specifically the need for permutation invariance, it is insufficient to simply pad the sets. Therefore, performing machine learning tasks on such molecular sets requires a Molecular Set Representation Learning neural network architecture capable of permutation invariant representation of variable-sized sets. Over the past five years, multiple architectures, including DeepSets, Set-Transformer, or RepSet, have become available that fulfil these requirements .