Prediction of antimicrobial peptides toxicity based on their physico-chemical properties using machine learning techniques.

ABSTRACT:

Background

Antimicrobial peptides are promising tools to fight against ever-growing antibiotic resistance. However, despite many advantages, their toxicity to mammalian cells is a critical obstacle in clinical application and needs to be addressed.

Results

In this study, by using an up-to-date dataset, a machine learning model has been trained successfully to predict the toxicity of antimicrobial peptides. The comprehensive set of features of both physico-chemical and linguistic-based with local and global essences have undergone feature selection to identify key properties behind toxicity of antimicrobial peptides. After feature selection, the hybrid model showed the best performance with a recall of 0. 876 and a F1 score of 0. 849.

Conclusions

The obtained model can

SUBMITTER: Khabbaz H 

PROVIDER: S-EPMC8582201 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature