  Generative Flow Networks (GFlowNets) have demonstrated significant
performance improvements for generating diverse discrete objects $x$ given a
reward function $R(x)$, indicating the utility of the object and trained
independently from the GFlowNet by supervised learning to predict a desirable
property $y$ given $x$. We hypothesize that this can lead to incompatibility
between the inductive optimization biases in training $R$ and in training the
GFlowNet, potentially leading to worse samples and slow adaptation to changes
in the distribution. In this work, we build upon recent work on jointly
learning energy-based models with GFlowNets and extend it to learn the joint
over multiple variables, which we call Joint Energy-Based GFlowNets (JEBGFNs),
such as peptide sequences and their antimicrobial activity. Joint learning of
the energy-based model, used as a reward for the GFlowNet, can resolve the
issues of incompatibility since both the reward function $R$ and the GFlowNet
sampler are trained jointly. We find that this joint training or joint
energy-based formulation leads to significant improvements in generating
anti-microbial peptides. As the training sequences arose out of evolutionary or
artificial selection for high antibiotic activity, there is presumably some
structure in the distribution of sequences that reveals information about the
antibiotic activity. This results in an advantage to modeling their joint
generatively vs. pure discriminative modeling. We also evaluate JEBGFN in an
active learning setting for discovering anti-microbial peptides.
