In addition, dynamic intermolecular interactions such as hydrogen bonds and π-stacking are common occurrences in ligand-protein binding, which is of high interest in medicinal chemistry . Furthermore, specific bonds are not well defined in conjugated systems such as aromatic rings, as electrons are delocalised over multiple atoms and bonds. Atomic number: {0,1} 101 Formal charge:{0,1} Hybridization: {0,1} Chiral tag: {0,1} In ring: {0,1} Total hyrogens:{0,1} Bond Type: {0,1} Stereo: {0,1} Is Aromatic: {0,1} Is Conjugated: {0,1} Given this often somewhat fuzzy notion of bonds in molecules, we hypothesize that representing a molecule as a set of atoms rather than a graph may capture the true nature of molecules better than explicit graph representations while preserving implicit information about molecular structure. In this set-based approach, each atom is represented as a vector of one-hot encoded atom invariants as defined by Rogers & Hahn [12] (see details in Section 4.2). While this representation may encode the local topology of a molecular graph through these invariants, e.g. the degree of an atom, it does not encode any explicit connectivity of the molecular graph. In the context of set representation learning, a molecule is therefore defined as a set of k-dimensional vectors ⃗ a where the set's cardinality is the number of non-hydrogen atoms in the molecule. However, this set-based representation introduces two properties that are not supported by typical neural network-based machine learning architectures: (i) The cardinality of the molecular sets differs depending on the number of non-hydrogen atoms in a molecule, and (ii) the molecular sets are unordered.