Synthetic Generation of Escape Sequences for Escape Prediction of SARS-CoV-2

Prem Singh Bist; Hilal Tayara; Kil To Chong

doi:10.1007/978-3-031-85902-1_27

Synthetic Generation of Escape Sequences for Escape Prediction of SARS-CoV-2

Prem Singh Bist
, Hilal Tayara^*
, Kil To Chong^*

^*Corresponding author for this work

Division of International Engineering and Science

Jeonbuk National University

Research output: Contribution to conference › Conference paper › peer-review

Abstract

The challenges of developing drugs and vaccines against SARS-CoV-2 is intensified by the constant evolution of the spike protein. This study focuses on using Artificial Intelligence, specifically Generative Adversarial Networks (GANs), to simulate escape sequences, and utilize these sequences to improve the escape prediction model. Our novel GAN model generates synthetic spike protein sequences with potentially higher infectivity and transmissibility. This approach showed a promising increase in prediction accuracy, with improvements noted across various datasets. Such advancements could revolutionize our ability to anticipate future mutations, aiding in the creation of more effective treatments and preventive measures against COVID-19 and its variants. Our findings underscore the potential of AI in addressing challenges posed by fast-evolving pathogens.

Original language	English
Title of host publication	Scientific Computing and Bioinformatics and Computational Biology - 22nd International Conference, CSC 2024, and 25th International Conference, BIOCOMP 2024, Held as Part of the World Congress in Computer Science, Computer Engineering and Applied Computing, CSCE 2024
Editors	Douglas D. Hodson, Michael R. Grimaila, Torrey J. Wagner, Hamid R. Arabnia, Leonidas Deligiannidis
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	313-324
Number of pages	12
ISBN (Print)	9783031859014
DOIs	https://doi.org/10.1007/978-3-031-85902-1_27
State	Published - 2025
Event	22nd International Conference on Scientific Computing and Bioinformatics, CSC 2024, and 25th International Conference on Computational Biology, BIOCOMP 2024, held as part of the World Congress in Computer Science, Computer Engineering and Applied Computing, CSCE 2024 - Las Vegas, United States Duration: 2024.07.22 → 2024.07.25

Publication series

Name	Communications in Computer and Information Science
Volume	2258 CCIS
ISSN (Print)	1865-0929
ISSN (Electronic)	1865-0937

Conference

Conference	22nd International Conference on Scientific Computing and Bioinformatics, CSC 2024, and 25th International Conference on Computational Biology, BIOCOMP 2024, held as part of the World Congress in Computer Science, Computer Engineering and Applied Computing, CSCE 2024
Country/Territory	United States
City	Las Vegas
Period	24.07.22 → 24.07.25

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

Escape Prediction
Escape Sequence Generation
Generative Adversarial Network
SARS-CoV-2
Spike Protein
Vaccine Design

Quacquarelli Symonds(QS) Subject Topics

Computer Science & Information Systems
Mathematics

Access to Document

10.1007/978-3-031-85902-1_27

Cite this

Bist, P. S., Tayara, H., & Chong, K. T. (2025). Synthetic Generation of Escape Sequences for Escape Prediction of SARS-CoV-2. In D. D. Hodson, M. R. Grimaila, T. J. Wagner, H. R. Arabnia, & L. Deligiannidis (Eds.), Scientific Computing and Bioinformatics and Computational Biology - 22nd International Conference, CSC 2024, and 25th International Conference, BIOCOMP 2024, Held as Part of the World Congress in Computer Science, Computer Engineering and Applied Computing, CSCE 2024 (pp. 313-324). (Communications in Computer and Information Science; Vol. 2258 CCIS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-85902-1_27

Bist, Prem Singh ; Tayara, Hilal ; Chong, Kil To. / Synthetic Generation of Escape Sequences for Escape Prediction of SARS-CoV-2. Scientific Computing and Bioinformatics and Computational Biology - 22nd International Conference, CSC 2024, and 25th International Conference, BIOCOMP 2024, Held as Part of the World Congress in Computer Science, Computer Engineering and Applied Computing, CSCE 2024. editor / Douglas D. Hodson ; Michael R. Grimaila ; Torrey J. Wagner ; Hamid R. Arabnia ; Leonidas Deligiannidis. Springer Science and Business Media Deutschland GmbH, 2025. pp. 313-324 (Communications in Computer and Information Science).

@inproceedings{317a918bb11a42958921ed2ea4effe87,

title = "Synthetic Generation of Escape Sequences for Escape Prediction of SARS-CoV-2",

abstract = "The challenges of developing drugs and vaccines against SARS-CoV-2 is intensified by the constant evolution of the spike protein. This study focuses on using Artificial Intelligence, specifically Generative Adversarial Networks (GANs), to simulate escape sequences, and utilize these sequences to improve the escape prediction model. Our novel GAN model generates synthetic spike protein sequences with potentially higher infectivity and transmissibility. This approach showed a promising increase in prediction accuracy, with improvements noted across various datasets. Such advancements could revolutionize our ability to anticipate future mutations, aiding in the creation of more effective treatments and preventive measures against COVID-19 and its variants. Our findings underscore the potential of AI in addressing challenges posed by fast-evolving pathogens.",

keywords = "Escape Prediction, Escape Sequence Generation, Generative Adversarial Network, SARS-CoV-2, Spike Protein, Vaccine Design",

author = "Bist, \{Prem Singh\} and Hilal Tayara and Chong, \{Kil To\}",

note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.; 22nd International Conference on Scientific Computing and Bioinformatics, CSC 2024, and 25th International Conference on Computational Biology, BIOCOMP 2024, held as part of the World Congress in Computer Science, Computer Engineering and Applied Computing, CSCE 2024 ; Conference date: 22-07-2024 Through 25-07-2024",

year = "2025",

doi = "10.1007/978-3-031-85902-1\_27",

language = "English",

isbn = "9783031859014",

series = "Communications in Computer and Information Science",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "313--324",

editor = "Hodson, \{Douglas D.\} and Grimaila, \{Michael R.\} and Wagner, \{Torrey J.\} and Arabnia, \{Hamid R.\} and Leonidas Deligiannidis",

booktitle = "Scientific Computing and Bioinformatics and Computational Biology - 22nd International Conference, CSC 2024, and 25th International Conference, BIOCOMP 2024, Held as Part of the World Congress in Computer Science, Computer Engineering and Applied Computing, CSCE 2024",

}

Bist, PS, Tayara, H & Chong, KT 2025, Synthetic Generation of Escape Sequences for Escape Prediction of SARS-CoV-2. in DD Hodson, MR Grimaila, TJ Wagner, HR Arabnia & L Deligiannidis (eds), Scientific Computing and Bioinformatics and Computational Biology - 22nd International Conference, CSC 2024, and 25th International Conference, BIOCOMP 2024, Held as Part of the World Congress in Computer Science, Computer Engineering and Applied Computing, CSCE 2024. Communications in Computer and Information Science, vol. 2258 CCIS, Springer Science and Business Media Deutschland GmbH, pp. 313-324, 22nd International Conference on Scientific Computing and Bioinformatics, CSC 2024, and 25th International Conference on Computational Biology, BIOCOMP 2024, held as part of the World Congress in Computer Science, Computer Engineering and Applied Computing, CSCE 2024, Las Vegas, United States, 24.07.22. https://doi.org/10.1007/978-3-031-85902-1_27

Synthetic Generation of Escape Sequences for Escape Prediction of SARS-CoV-2. / Bist, Prem Singh; Tayara, Hilal; Chong, Kil To.
Scientific Computing and Bioinformatics and Computational Biology - 22nd International Conference, CSC 2024, and 25th International Conference, BIOCOMP 2024, Held as Part of the World Congress in Computer Science, Computer Engineering and Applied Computing, CSCE 2024. ed. / Douglas D. Hodson; Michael R. Grimaila; Torrey J. Wagner; Hamid R. Arabnia; Leonidas Deligiannidis. Springer Science and Business Media Deutschland GmbH, 2025. p. 313-324 (Communications in Computer and Information Science; Vol. 2258 CCIS).

Research output: Contribution to conference › Conference paper › peer-review

TY - GEN

T1 - Synthetic Generation of Escape Sequences for Escape Prediction of SARS-CoV-2

AU - Bist, Prem Singh

AU - Tayara, Hilal

AU - Chong, Kil To

N1 - Publisher Copyright: © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

PY - 2025

Y1 - 2025

N2 - The challenges of developing drugs and vaccines against SARS-CoV-2 is intensified by the constant evolution of the spike protein. This study focuses on using Artificial Intelligence, specifically Generative Adversarial Networks (GANs), to simulate escape sequences, and utilize these sequences to improve the escape prediction model. Our novel GAN model generates synthetic spike protein sequences with potentially higher infectivity and transmissibility. This approach showed a promising increase in prediction accuracy, with improvements noted across various datasets. Such advancements could revolutionize our ability to anticipate future mutations, aiding in the creation of more effective treatments and preventive measures against COVID-19 and its variants. Our findings underscore the potential of AI in addressing challenges posed by fast-evolving pathogens.

AB - The challenges of developing drugs and vaccines against SARS-CoV-2 is intensified by the constant evolution of the spike protein. This study focuses on using Artificial Intelligence, specifically Generative Adversarial Networks (GANs), to simulate escape sequences, and utilize these sequences to improve the escape prediction model. Our novel GAN model generates synthetic spike protein sequences with potentially higher infectivity and transmissibility. This approach showed a promising increase in prediction accuracy, with improvements noted across various datasets. Such advancements could revolutionize our ability to anticipate future mutations, aiding in the creation of more effective treatments and preventive measures against COVID-19 and its variants. Our findings underscore the potential of AI in addressing challenges posed by fast-evolving pathogens.

KW - Escape Prediction

KW - Escape Sequence Generation

KW - Generative Adversarial Network

KW - SARS-CoV-2

KW - Spike Protein

KW - Vaccine Design

UR - https://www.scopus.com/pages/publications/105003907005

U2 - 10.1007/978-3-031-85902-1_27

DO - 10.1007/978-3-031-85902-1_27

M3 - Conference paper

AN - SCOPUS:105003907005

SN - 9783031859014

T3 - Communications in Computer and Information Science

SP - 313

EP - 324

BT - Scientific Computing and Bioinformatics and Computational Biology - 22nd International Conference, CSC 2024, and 25th International Conference, BIOCOMP 2024, Held as Part of the World Congress in Computer Science, Computer Engineering and Applied Computing, CSCE 2024

A2 - Hodson, Douglas D.

A2 - Grimaila, Michael R.

A2 - Wagner, Torrey J.

A2 - Arabnia, Hamid R.

A2 - Deligiannidis, Leonidas

PB - Springer Science and Business Media Deutschland GmbH

T2 - 22nd International Conference on Scientific Computing and Bioinformatics, CSC 2024, and 25th International Conference on Computational Biology, BIOCOMP 2024, held as part of the World Congress in Computer Science, Computer Engineering and Applied Computing, CSCE 2024

Y2 - 22 July 2024 through 25 July 2024

ER -

Bist PS, Tayara H, Chong KT. Synthetic Generation of Escape Sequences for Escape Prediction of SARS-CoV-2. In Hodson DD, Grimaila MR, Wagner TJ, Arabnia HR, Deligiannidis L, editors, Scientific Computing and Bioinformatics and Computational Biology - 22nd International Conference, CSC 2024, and 25th International Conference, BIOCOMP 2024, Held as Part of the World Congress in Computer Science, Computer Engineering and Applied Computing, CSCE 2024. Springer Science and Business Media Deutschland GmbH. 2025. p. 313-324. (Communications in Computer and Information Science). doi: 10.1007/978-3-031-85902-1_27