As antibiotic-resistant bacterial strains are rapidly spreading worldwide,
infections caused by these strains are emerging as a global crisis causing the
death of millions of people every year. Antimicrobial Peptides (AMPs) are one
of the candidates to tackle this problem because of their potential diversity,
and ability to favorably modulate the host immune response. However,
large-scale screening of new AMP candidates is expensive, time-consuming, and
now affordable in developing countries, which need the treatments the most. In
this work, we propose a novel active machine learning-based framework that
statistically minimizes the number of wet-lab experiments needed to design new
AMPs, while ensuring a high diversity and novelty of generated AMPs sequences,
in multi-rounds of wet-lab AMP screening settings. Combining recurrent neural
network models and a graph-based filter (GraphCC), our proposed approach
delivers novel and diverse candidates and demonstrates better performances
according to our defined metrics.

本篇论文旨在利用一种基于机器学习的框架快速有效地筛选出新的抗菌肽，其中利用了基于循环神经网络和基于图形的过滤器，从而显著减少湿实验的次数和时间成本。