(482j) Iterative Peptide Discovery with Active Learning and Meta-Learning of Deep Convolutional Classifiers

Often the development of novel materials is not amenable to high-throughput or purely computational screening methods. Instead, materials must be synthesized one at a time in a process and does not generate significant amounts of data. One way this method can be improved is by ensuring that each experiment provides the best improvement in both material properties and predictive modeling accuracy. In this work, we study the effectiveness of active learning, which optimizes the order of experiments, and meta learning, which transfers knowledge from one context to another, to reduce the number of experiments necessary to build a predictive model. We present a novel multi-task benchmark database of peptides designed to advance active, few-shot, and meta-learning methods for experimental design. We show results of standard active learning and meta-learning methods across these datasets to assess their ability to improve predictive models from the fewest number of experiments. We find both uncertainty minimization and query by committee to be effective active learning techniques. The meta-learning method Reptile was found to improve accuracy when only 3-5 experimental results are available, but trail normal gradient-descent based methods beyond 5 experiments.