  Often the development of novel functional peptides is not amenable to high
throughput or purely computational screening methods. Peptides must be
synthesized one at a time in a process that does not generate large amounts of
data. One way this method can be improved is by ensuring that each experiment
provides the best improvement in both peptide properties and predictive
modeling accuracy. Here, we study the effectiveness of active learning,
optimizing experiment order, and meta-learning, transferring knowledge between
contexts, to reduce the number of experiments necessary to build a predictive
model. We present a multi-task benchmark database of peptides designed to
advance these methods for experimental design. Each task is binary
classification of peptides represented as a sequence string. We find neither
active learning method tested to be better than random choice. The
meta-learning method Reptile was found to improve average accuracy across
datasets. Combining meta-learning with active learning offers inconsistent
benefits.
