  Generative models hold great promise for small molecule discovery,
significantly increasing the size of search space compared to traditional in
silico screening libraries. However, most existing machine learning methods for
small molecule generation suffer from poor synthesizability of candidate
compounds, making experimental validation difficult. In this paper we propose
Reaction-GFlowNet (RGFN), an extension of the GFlowNet framework that operates
directly in the space of chemical reactions, thereby allowing out-of-the-box
synthesizability while maintaining comparable quality of generated candidates.
We demonstrate that with the proposed set of reactions and building blocks, it
is possible to obtain a search space of molecules orders of magnitude larger
than existing screening libraries coupled with low cost of synthesis. We also
show that the approach scales to very large fragment libraries, further
increasing the number of potential molecules. We demonstrate the effectiveness
of the proposed approach across a range of oracle models, including pretrained
proxy models and GPU-accelerated docking.
