Benchmarking protein structure predictors to assist machine learning-guided peptide discovery-Reference-Cited by-同舟云学术

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

Published:2023 Issue:4 Volume:2 Page:981-993
ISSN:2635-098X
Container-title:Digital Discovery
language:en
Short-container-title:Digital Discovery

Author:

Aldas-Bulos Victor Daniel¹,Plisson Fabien¹²^ORCID

Affiliation:

1. Centre for Research and Advanced Studies of the National Polytechnic Institute (CINVESTAV-IPN), Advanced Genomic Unit, National Laboratory of Genomics for Biodiversity (LANGEBIO), 36824 Irapuato, Guanajuato State, Mexico

2. Centre for Research and Advanced Studies of the National Polytechnic Institute (CINVESTAV-IPN), Irapuato Unit, Department of Biotechnology and Biochemistry, 36824 Irapuato, Guanajuato State, Mexico

Abstract

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

Funder

Consejo Nacional de Ciencia y Tecnología

Publisher

Royal Society of Chemistry (RSC)

Link

http://pubs.rsc.org/en/content/articlepdf/2023/DD/D3DD00045A

Reference67 articles.

1. Designing antimicrobial peptides: form follows function

2. Computer-Aided Design of Antimicrobial Peptides: Are We Generating Effective Drug Candidates?

3. Accelerating antibiotic discovery through artificial intelligence

4. Deep generative models for peptide design

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