  Deep generative models of molecules have grown immensely in popularity,
trained on relevant datasets, these models are used to search through chemical
space. The downstream utility of generative models for the inverse design of
novel functional compounds depends on their ability to learn a training
distribution of molecules. The most simple example is a language model that
takes the form of a recurrent neural network and generates molecules using a
string representation. More sophisticated are graph generative models, which
sequentially construct molecular graphs and typically achieve state of the art
results. However, recent work has shown that language models are more capable
than once thought, particularly in the low data regime. In this work, we
investigate the capacity of simple language models to learn distributions of
molecules. For this purpose, we introduce several challenging generative
modeling tasks by compiling especially complex distributions of molecules. On
each task, we evaluate the ability of language models as compared with two
widely used graph generative models. The results demonstrate that language
models are powerful generative models, capable of adeptly learning complex
molecular distributions -- and yield better performance than the graph models.
Language models can accurately generate: distributions of the highest scoring
penalized LogP molecules in ZINC15, multi-modal molecular distributions as well
as the largest molecules in PubChem.
