De Novo Protein Design for Novel Folds Using Guided Conditional Wasserstein Generative Adversarial Networks-Reference-Cited by-同舟云学术

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De Novo Protein Design for Novel Folds Using Guided Conditional Wasserstein Generative Adversarial Networks

Published:2020-09-18 Issue:12 Volume:60 Page:5667-5681
ISSN:1549-9596
Container-title:Journal of Chemical Information and Modeling
language:en
Short-container-title:J. Chem. Inf. Model.

Author:

Karimi Mostafa¹²,Zhu Shaowen¹,Cao Yue¹,Shen Yang¹²^ORCID

Affiliation:

1. Department of Electrical and Computer Engineering, Texas A&M University, College Station, Texas 77843, United States

2. TEES-AgriLife Center for Bioinformatics and Genomic Systems Engineering, Texas A&M University, College Station, Texas 77840, United States

Funder

National Institute of General Medical Sciences

Publisher

American Chemical Society (ACS)

Subject

Library and Information Sciences,Computer Science Applications,General Chemical Engineering,General Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.jcim.0c00593

Reference73 articles.

1. The Reversible Reduction of Disulfide Bonds in Polyalanyl Ribonuclease

2. Principles that Govern the Folding of Protein Chains

3. Protein Structure Prediction and Structural Genomics

4. Molecular technology: Designing proteins and peptides

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