Deep generative models typically use non-3D methods to represent molecules. Text-based methods and the use of SMILES strings is still the most prevalent representation. The reason for this is probably because SMILES can be massively expressive, and that it is trivial to manipulate and transform strings. However, there are some drawbacks with using SMILES strings. 11,64 A significant problem is that a conservative change can have a huge change in the 3D structure of a molecule. This is important since all molecules are 3D objects. Here, we have addressed this issue by extending the fragment based DeepFMPO method, where molecules are built from similar fragments, instead of sequences of letters (as is the case for SMILES based methods). Fragmentbased methods are considered intuitive and often mimic the way medicinal chemists think and design. The approach was recently described by Meyers et al. as a method "offer an appealing compromise between molecular expressivity and practicality" [64]. Hence, a common medicinal chemistry design strategy is to work on molecular series, swapping fragments and substituents in one part of the molecule, while keeping other parts of the compounds unaltered. This is often a challenge for generative methods working on SMILES strings, leading us to the next topic of discussion.