Artificial Intelligence Tools Addressing Challenges of Cancer Progression Due to Antimicrobial Resistance in Pathogenic Biofilm Systems

Infections, inflammation, and progression of multifactorial diseases are found to be integratively linked, including most Cancers. Dysfunctional microbiomes are also associated with several cancers in their tumor microenvironments. Antimicrobial peptides (AMPs) are short, positively charged peptides found in a diverse range of species, including bacteria and humans. As host defense peptides, they can destroy pathogenic infections, particularly those that are multidrug resistant. AMPs have raised hopes in the biomedical and pharmaceutical industries as fresh non-antibiotic strategies for combating infectious diseases. However, in vitro and in vivo verification of AMPs is problematic and may miss new antimicrobial drugs. Creating computational methods for quick and precise identification of AMPs and their functional forms is critical for developing new and more effective antimicrobial drugs. Machine learning techniques were recently discovered effective at mining, predicting, and producing efficient antimicrobial peptides from a large AMP database. We reviewed 76 articles, after following literature search rubrics to come to the following conclusions. Distance metric-constant K-based nearest neighbor algorithms (KNN), hidden Markov models (HMMs), support vector machine models (SVMs), random forest models (RFs), decision tree models, and deep neural network (DNN)-based models are some of the most popular AI tools for detecting antimicrobial activity in peptide sequence-derived structure and function. Knowledge graphs can further assist in identifying hub genes and antimicrobial peptides that target and block quorum sensing (QS) signals within the microbial networks. In conclusion, we state that currently no single AI method has been found appropriate for AMP discovery and accurately capable of predicting high-efficacy AMPs. Our current literature review and analysis identify cutting-edge algorithms or innovations that might be included in hybrid machine-learning approaches for the most effective AMP identification, creation, and prediction. Non-peptide, natural molecule-based approaches to AMR reduction are also being studied for development, with natural peptide scaffolds serving as the foundation.