Benchmarking protein structure predictors to assist machine learning-guided peptide discovery

2023/01/01 by Victor Daniel Aldas-Bulos, Fabien Plisson · 1 voice · 2 citations
Immunology and Microbiology · Biochemistry, Genetics and Molecular Biology · #Antimicrobial Peptides and Activities #Machine Learning in Bioinformatics #Chemical Synthesis and Analysis

paper · pdf · doi:10.1039/d3dd00045a

openalex publication_date 2023/01/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/04

Abstract

Machine learning models provide an informed and efficient strategy to create novel peptide and protein sequences with the desired profiles.

Citations

• Divergence measures based on the Shannon entropy
• Highly accurate protein structure prediction with AlphaFold
• ColabFold: making protein folding accessible to all
• Benchmarking AlphaFold2 on peptide structure prediction

Cited by

• Structure-aware machine learning strategies for antimicrobial peptide discovery
• Accelerating the Discovery and Design of Antimicrobial Peptides with Artificial Intelligence

Discussions

• #proteindesign #AlphaFold #structuralbias doi.org/10.1039/D3DD00… [bsky, 0 points, 0 comments]