Graph-Based Active Machine Learning Method for Diverse and Novel Antimicrobial Peptides Generation and Selection
This work addresses the global crisis of antibiotic-resistant bacterial infections by enabling more efficient AMP discovery, particularly benefiting developing countries, though it appears incremental as it combines existing methods like recurrent neural networks with a graph-based filter.
The authors tackled the problem of expensive and time-consuming large-scale screening for new antimicrobial peptides (AMPs) by proposing a novel active machine learning framework that statistically minimizes wet-lab experiments while ensuring high diversity and novelty in generated AMP sequences, demonstrating better performance according to their defined metrics.
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.