How can artificial intelligence be used for peptidomics?

• Type: Article in International Scientific Journal
• Date: 2021

Authors

Perpetuo, L (Author) Other; Klein, J (Author) Other; Ferreira, R (Author) Other; Guedes, S (Author) Other; Amado, F (Author) Other; Leite-Moreira AF (Author) FMUP; Silva, AMS (Author) Other; Thongboonkerd, V (Author) Other; Vitorino, R (Author) FMUP

Journal

Title: Expert Review of Proteomics

Vol. 18

Pages: 527-556

ISSN: 1478-9450

Publisher: Taylor & Francis

Indexing

ISI Web of Knowledge - 4 Citations

Scopus - 8 Citations

Other information

Authenticus ID: P-00V-A8V

DOI: 10.1080/14789450.2021.1962303

Abstract (EN): Introduction Peptidomics is an emerging field of omics sciences using advanced isolation, analysis, and computational techniques that enable qualitative and quantitative analyses of various peptides in biological samples. Peptides can act as useful biomarkers and as therapeutic molecules for diseases Areas covered The use of therapeutic peptides can be predicted quickly and efficiently using data-driven computational methods, particularly artificial intelligence (AI) approach. Various AI approaches are useful for peptide-based drug discovery, such as support vector machine, random forest, extremely randomized trees, and other more recently developed deep learning methods. AI methods are relatively new to the development of peptide-based therapies, but these techniques already become essential tools in protein science by dissecting novel therapeutic peptides and their functions (Figure 1). Expert opinion Researchers have shown that AI models can facilitate the development of peptidomics and selective peptide therapies in the field of peptide science. Biopeptide prediction is important for the discovery and development of successful peptide-based drugs. Due to their ability to predict therapeutic roles based on sequence details, many AI-dependent prediction tools have been developed (Figure 1).

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 30