  Recent breakthroughs in generative modelling have led to a number of works
proposing molecular generation models for drug discovery. While these models
perform well at capturing drug-like motifs, they are known to often produce
synthetically inaccessible molecules. This is because they are trained to
compose atoms or fragments in a way that approximates the training
distribution, but they are not explicitly aware of the synthesis constraints
that come with making molecules in the lab. To address this issue, we introduce
SynFlowNet, a GFlowNet model whose action space uses chemically validated
reactions and reactants to sequentially build new molecules. We evaluate our
approach using synthetic accessibility scores and an independent retrosynthesis
tool. SynFlowNet consistently samples synthetically feasible molecules, while
still being able to find diverse and high-utility candidates. Furthermore, we
compare molecules designed with SynFlowNet to experimentally validated actives,
and find that they show comparable properties of interest, such as molecular
weight, SA score and predicted protein binding affinity.
