  Design of de novo biological sequences with desired properties, like protein
and DNA sequences, often involves an active loop with several rounds of
molecule ideation and expensive wet-lab evaluations. These experiments can
consist of multiple stages, with increasing levels of precision and cost of
evaluation, where candidates are filtered. This makes the diversity of proposed
candidates a key consideration in the ideation phase. In this work, we propose
an active learning algorithm leveraging epistemic uncertainty estimation and
the recently proposed GFlowNets as a generator of diverse candidate solutions,
with the objective to obtain a diverse batch of useful (as defined by some
utility function, for example, the predicted anti-microbial activity of a
peptide) and informative candidates after each round. We also propose a scheme
to incorporate existing labeled datasets of candidates, in addition to a reward
function, to speed up learning in GFlowNets. We present empirical results on
several biological sequence design tasks, and we find that our method generates
more diverse and novel batches with high scoring candidates compared to
existing approaches.
