Construction method of anti-cancer peptide generation model and training method thereof

The anticancer peptide generation model constructed through SeqGAN and GNN automatically extracts peptide data features and learns structural information, solving the problems of cumbersome generation process and insufficient information in existing technologies, and achieving efficient and accurate anticancer peptide generation.

CN116030908BActive Publication Date: 2025-10-10HUNAN UNIV
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Patent Information

Application Number
CN202310149714.8
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2023-02-22
Publication Date
2025-10-10
Estimated Expiration
2043-02-22

AI Technical Summary

Technical Problem

Existing methods for generating anticancer peptides require a large amount of domain expertise to design feature extractors, which makes the generation process cumbersome and fails to effectively consider the structural information of the physical and chemical properties of the peptides, affecting the generation efficiency and accuracy.

Method used

The sequence generative adversarial network SeqGAN and graph neural network GNN are used to construct an anti-cancer peptide generation model. By combining the generation module and the prediction module, data features are automatically extracted and the sequence properties and structural information of the peptides are learned. Reinforcement learning and feedback information are used to guide the generation process.

Benefits of technology

It achieves efficient anticancer peptide generation without the need for manually designed feature processors, can learn the sequence properties and structural information of peptides, improve the speed and accuracy of generation models, and enhance the efficiency and diversity of anticancer peptide design.

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Abstract

The application discloses a kind of anti-cancer peptide generation model training methods, comprising: obtaining general polypeptide sequence data, anti-cancer peptide sequence data and non-anti-cancer peptide sequence data, and according to amino acid glossary general polypeptide sequence data, anti-cancer peptide sequence data and non-anti-cancer peptide sequence data are digitally encoded, to obtain general polypeptide sequence data set, anti-cancer peptide sequence data set and non-anti-cancer peptide sequence data set, general polypeptide sequence data set, anti-cancer peptide sequence data set and non-anti-cancer peptide sequence data set are respectively divided according to training set and test set 8:2 ratio, to obtain general polypeptide sequence training set and test set, anti-cancer peptide sequence training set and test set and non-anti-cancer peptide sequence training set and test set, general polypeptide sequence training set is input into the generation module of anti-cancer peptide generation model, and the weight parameter and bias parameter of each layer in generation module are updated and optimized using back propagation algorithm, to obtain pre-trained generation module.
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