  Recent advances in machine learning for molecules exhibit great potential for
facilitating drug discovery from in silico predictions. Most models for
molecule generation rely on the decomposition of molecules into frequently
occurring substructures (motifs), from which they generate novel compounds.
While motif representations greatly aid in learning molecular distributions,
such methods struggle to represent substructures beyond their known motif set.
To alleviate this issue and increase flexibility across datasets, we propose
MAGNet, a graph-based model that generates abstract shapes before allocating
atom and bond types. To this end, we introduce a novel factorisation of the
molecules' data distribution that accounts for the molecules' global context
and facilitates learning adequate assignments of atoms and bonds onto shapes.
Despite the added complexity of shape abstractions, MAGNet outperforms most
other graph-based approaches on standard benchmarks. Importantly, we
demonstrate that MAGNet's improved expressivity leads to molecules with more
topologically distinct structures and, at the same time, diverse atom and bond
assignments.
