ArticleJournal of computer-aided molecular design2024
Computational peptide discovery with a genetic programming approach.
Article in Journal of computer-aided molecular design, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed, 6 citations in OpenAlex.
- Machine Learning-Based Development of Gadolinium Binding Peptides.NMR in biomedicine · 2026Article
- Optimization of CEST MRI Reporter Protein Design Using Cation-Pi Networks.Chemistry (Weinheim an der Bergstrasse, Germany) · 2026Article
- Machine learning-based development of Gadolinium binding peptides.bioRxiv : the preprint server for biology · 2025Article
- Engineered Peptide Scrambling for Enhanced Drug Delivery to Resistant Breast Cancer Cells via Small Extracellular Vesicles.ACS applied bio materials · 2025Article
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
Abstract
The development of peptides for therapeutic targets or biomarkers for disease diagnosis is a challenging task in protein engineering. Current approaches are tedious, often time-consuming and require complex laboratory data due to the vast search spaces that need to be considered. 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 POET
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.