  DNA-encoded libraries (DELs) are a powerful approach for rapidly screening
large numbers of diverse compounds. One of the key challenges in using DELs is
library design, which involves choosing the building blocks that will be
combinatorially combined to produce the final library. In this paper we
consider the task of protein-protein interaction (PPI) biased DEL design. To
this end, we evaluate several machine learning algorithms on the PPI modulation
task and use them as a reward for the proposed GFlowNet-based generative
approach. We additionally investigate the possibility of using structural
information about building blocks to design a hierarchical action space for the
GFlowNet. The observed results indicate that GFlowNets are a promising approach
for generating diverse combinatorial library candidates.
