Hybrid transformer-CNN model for accurate prediction of peptide hemolytic potential

Hemolysis is a crucial factor in various biomedical and pharmaceutical contexts, driving our interest in developing advanced computational techniques for precise prediction. Our proposed approach takes advantage of the unique capabilities of convolutional neural networks (CNNs) and transformers to d...

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Bibliographic Details
Published in:Scientific reports 2024-06, Vol.14 (1), p.14263-9, Article 14263
Main Authors: Almotairi, Sultan, Badr, Elsayed, Abdelbaky, Ibrahim, Elhakeem, Mohamed, Abdul Salam, Mustafa
Format: Article
Language:English
Subjects:
631/45
631/45/611
Bioinformatics
Computational Biology - methods
Computer applications
Convolutional neural networks (CNNs)
Correlation coefficient
Deep learning
Drug design
Drug development
Hemolysis
Hemolysis - drug effects
Humanities and Social Sciences
Humans
multidisciplinary
Neural networks
Neural Networks, Computer
Peptides
Peptides - chemistry
Predictions
Science
Science (multidisciplinary)
Transformers
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Summary:Hemolysis is a crucial factor in various biomedical and pharmaceutical contexts, driving our interest in developing advanced computational techniques for precise prediction. Our proposed approach takes advantage of the unique capabilities of convolutional neural networks (CNNs) and transformers to detect complex patterns inherent in the data. The integration of CNN and transformers' attention mechanisms allows for the extraction of relevant information, leading to accurate predictions of hemolytic potential. The proposed method was trained on three distinct data sets of peptide sequences known as recurrent neural network-hemolytic (RNN-Hem), Hlppredfuse, and Combined. Our computational results demonstrated the superior efficacy of our models compared to existing methods. The proposed approach demonstrated impressive Matthews correlation coefficients of 0.5962, 0.9111, and 0.7788 respectively, indicating its effectiveness in predicting hemolytic activity. With its potential to guide experimental efforts in peptide design and drug development, this method holds great promise for practical applications. Integrating CNNs and transformers proves to be a powerful tool in the fields of bioinformatics and therapeutic research, highlighting their potential to drive advancement in this area.
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-024-63446-5