How can artificial intelligence be used for peptidomics?

Autores da FMUP

• Joaquim Adelino Correia Ferreira Leite Moreira

  Autor

Participantes de fora da FMUP

• Perpetuo, L
• Klein, J
• Ferreira, R
• Guedes, S
• Amado, F
• Silva, AMS
• Thongboonkerd, V
• Vitorino, R.

Unidades de investigação

• Cirurgia e Fisiologia

• Laboratório Associado RISE- Rede de Investigação em Saúde

  

  Investigador

  Fernando Carlos De Landér Schmitt

• RISE-Health

  

  Investigador

  Fernando Carlos De Landér Schmitt

Abstract

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).

Keywords

• Artificial intelligence; peptides; computational; proteomics; software