Computational peptide discovery with a genetic programming approach

Created by W.Langdon from gp-bibliography.bib Revision:1.8194

@Article{scalzitti:2024:CAMD,

• author = "Nicolas Scalzitti and Iliya Miralavy and David E. Korenchan and Christian T. Farrar and Assaf A. Gilad and Wolfgang Banzhaf",
• title = "Computational peptide discovery with a genetic programming approach",
• journal = "Journal of Computer-Aided Molecular Design",
• year = "2024",
• volume = "38",
• number = "1",
• pages = "Article no 17",
• month = dec,
• keywords = "genetic algorithms, genetic programming, Peptide discovery, CEST MRI, Contrast agent, Regular expressions, Evolutionary algorithm",
• ISSN = "0920-654X",
• URL = "https://rdcu.be/d5f5T",
• URL = "http://link.springer.com/article/10.1007/s10822-024-00558-0",
• DOI = "doi:10.1007/s10822-024-00558-0",
• size = "22 pages",
• abstract = "In silico methods can accelerate research and substantially reduce costs. Evolutionary algorithms are a promising approach for exploring large search spaces and can facilitate the discovery of new peptides. This study presents the development and use of a new variant of the genetic-programming-based POET algorithm, called POETRegex, where individuals are represented by a list of regular expressions. This algorithm was trained on a small curated dataset and employed to generate new peptides improving the sensitivity of peptides in magnetic resonance imaging with chemical exchange saturation transfer (CEST). The resulting model achieves a performance gain of 20 percent over the initial POET models and is able to predict a candidate peptide with a 58 percent performance increase compared to the gold-standard peptide. By combining the power of genetic programming with the flexibility of regular expressions, new peptide targets were identified that improve the sensitivity of detection by CEST.",

}

Genetic Programming entries for Nicolas Scalzitti Iliya Miralavy David E Korenchan Christian T Farrar Assaf A Gilad Wolfgang Banzhaf

Citations