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Accelerating Prediction of Antiviral Peptides Using Genetic Algorithm-Based Weighted Multiperspective Descriptors with Self-Normalized Deep Networks.

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Bibliographic Details
Title: Accelerating Prediction of Antiviral Peptides Using Genetic Algorithm-Based Weighted Multiperspective Descriptors with Self-Normalized Deep Networks.
Authors: Akbar S; Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu 610054, China.; Department of Computer Science, Abdul Wali Khan University Mardan, Mardan, Khyber Pakhtunkhwa 23200, Pakistan., Raza A; School of Electronic and Communication Engineering, Shenzhen Polytechnic University, Shenzhen 518055, China.; School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China.; Department of Computer Science, Bahria University, Islamabad 44220, Pakistan., Zou Q; Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu 610054, China.; Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou 324000, PR China., Alghamdi W; Department of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia., Kang X; Faculty of Applied Sciences, Macao Polytechnic University, Macao 999078, China., Ali H; Department of Computer Science, Abdul Wali Khan University Mardan, Mardan, Khyber Pakhtunkhwa 23200, Pakistan., Luo X; Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu 610054, China.
Source: Journal of chemical information and modeling [J Chem Inf Model] 2025 Sep 22; Vol. 65 (18), pp. 9815-9830. Date of Electronic Publication: 2025 Sep 03.
Publication Type: Journal Article
Language: English
Journal Info: Publisher: American Chemical Society Country of Publication: United States NLM ID: 101230060 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1549-960X (Electronic) Linking ISSN: 15499596 NLM ISO Abbreviation: J Chem Inf Model Subsets: MEDLINE
Imprint Name(s): Original Publication: Washington, D.C. : American Chemical Society, c2005-
MeSH Terms: Antiviral Agents*/chemistry , Antiviral Agents*/pharmacology , Peptides*/chemistry , Peptides*/pharmacology , Algorithms* , Deep Learning*, Humans ; Genetic Algorithms
Abstract: The accurate prediction of antiviral peptides (AVPs) plays a crucial role in accelerating the development of peptide-based therapeutics. Despite extensive production of antiviral medications, viral diseases remain a major human health concern. AVPs have emerged as potential candidates for the development of novel antiviral drugs. However, the available traditional methods are labor-intensive, expensive, and cannot provide a deeper structural and contextual understanding of the peptide sequences. To address these problems, we propose a novel deep computational model, TargetAVP-DeepCaps, for the precise prediction of AVPs. In this model, multiple innovative feature representation strategies were presented by encoding the input peptides using a pretrained ProtGPT2 model for contextual embeddings. On the other hand, sequence-to-image transformations are performed using SMR and RECM matrices. Additionally, the produced 2D images were locally decomposed using the CLBP approach to obtain the SMR-CLBP and RECM-CLBP descriptors. A differential evolution mechanism was applied to form a weighted-feature-based multiperspective vector. The optimal features were selected using a hybrid MRMD + SFLA feature selection approach. Finally, a novel self-normalized capsule network (Sn-CapsNet) model was developed to achieve a superior predictive accuracy of 97.36%, outperforming the available predictors by approximately 12% with an area under the curve (AUC) of 0.98. To ensure the generalization of the TargetAVP-DeepCaps model, our training achieved an approximately 8% higher prediction than previous models using an independent data set. The demonstrated effectiveness and robustness of TargetAVP-DeepCaps provide an advanced therapeutic tool for understanding peptide mechanisms and related applications in drug discovery.
Substance Nomenclature: 0 (Antiviral Agents)
0 (Peptides)
Entry Date(s): Date Created: 20250903 Date Completed: 20250922 Latest Revision: 20250922
Update Code: 20250922
DOI: 10.1021/acs.jcim.5c01777
PMID: 40901705
Database: MEDLINE
Description
Abstract:The accurate prediction of antiviral peptides (AVPs) plays a crucial role in accelerating the development of peptide-based therapeutics. Despite extensive production of antiviral medications, viral diseases remain a major human health concern. AVPs have emerged as potential candidates for the development of novel antiviral drugs. However, the available traditional methods are labor-intensive, expensive, and cannot provide a deeper structural and contextual understanding of the peptide sequences. To address these problems, we propose a novel deep computational model, TargetAVP-DeepCaps, for the precise prediction of AVPs. In this model, multiple innovative feature representation strategies were presented by encoding the input peptides using a pretrained ProtGPT2 model for contextual embeddings. On the other hand, sequence-to-image transformations are performed using SMR and RECM matrices. Additionally, the produced 2D images were locally decomposed using the CLBP approach to obtain the SMR-CLBP and RECM-CLBP descriptors. A differential evolution mechanism was applied to form a weighted-feature-based multiperspective vector. The optimal features were selected using a hybrid MRMD + SFLA feature selection approach. Finally, a novel self-normalized capsule network (Sn-CapsNet) model was developed to achieve a superior predictive accuracy of 97.36%, outperforming the available predictors by approximately 12% with an area under the curve (AUC) of 0.98. To ensure the generalization of the TargetAVP-DeepCaps model, our training achieved an approximately 8% higher prediction than previous models using an independent data set. The demonstrated effectiveness and robustness of TargetAVP-DeepCaps provide an advanced therapeutic tool for understanding peptide mechanisms and related applications in drug discovery.
ISSN:1549-960X
DOI:10.1021/acs.jcim.5c01777