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Volume 20, Issue 1
  • ISSN: 1574-8936
  • E-ISSN: 2212-392X

Abstract

Background

The identification and functional prediction of Multifunctional Therapeutic Peptides (MFTP) play a pivotal role in drug discovery, particularly for conditions such as inflammation and hyperglycemia. Current computational methods exhibit limitations in their ability to accurately predict the multifunctionality of these peptides.

Methods

We propose a novel Wide and Deep Learning Framework that integrates both deep learning and machine learning approaches. The deep learning segment processes sequence vectors using a neural network model, while the wide segment utilizes the physicochemical properties of peptides in a random forest-based model. This hybrid approach aims to enhance the accuracy of MFTP function prediction.

Results

Our framework outperformed the existing PrMFTP predictor in terms of precision, coverage, accuracy, and absolute true values. The evaluation was conducted on both training and independent testing datasets, demonstrating the robustness and generalizability of our model.

Conclusion

The proposed Wide & Deep Learning Framework offers a significant advancement in the computational prediction of MFTP functions. The availability of our model through a user-friendly web interface at MFTP-Tool.m6aminer.cn provides a valuable tool for researchers in the field of therapeutic peptide-based drug discovery, potentially accelerating the development of new treatments.

© 2025 Bentham Science Publishers
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2024-07-09
2025-01-19

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/content/journals/cbio/10.2174/0115748936299646240625092734
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MFTP-Tool: A Wide & Deep Learning Framework for Multi-Functional Therapeutic Peptides Prediction
Curr Bioinform 20, 1 (2025); https://doi.org/10.2174/0115748936299646240625092734
/content/journals/cbio/10.2174/0115748936299646240625092734
/content/journals/cbio/10.2174/0115748936299646240625092734
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  • Article Type:     Research Article
Keyword(s): class weight optimization; Multi-functional therapeutic peptides; pharmaceutical landscape; prediction; single-function peptides; wide & deep learning framework

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