Combining genetic algorithm with machine learning strategies for designing potent antimicrobial peptides.

ABSTRACT:

Background

Current methods in machine learning provide approaches for solving challenging, multiple constraint design problems. While deep learning and related neural networking methods have state-of-the-art performance, their vulnerability in decision making processes leading to irrational outcomes is a major concern for their implementation. With the rising antibiotic resistance, antimicrobial peptides (AMPs) have increasingly gained attention as novel therapeutic agents. This challenging design problem requires peptides which meet the multiple constraints of limiting drug-resistance in bacteria, preventing secondary infections from imbalanced microbial flora, and avoiding immune system suppression. AMPs offer a promising, bioinspired design space to targeting antimicrobial activ

SUBMITTER: Boone K 

PROVIDER: S-EPMC8111958 | biostudies-literature | 2021 May

REPOSITORIES: biostudies-literature