pLM4ACP: a model for predicting anticancer peptides based on machine learning and protein language models.
- Author:
Yitong LIU
1
;
Wenxin CHEN
1
;
Juanjuan LI
1
;
Xue CHI
1
;
Xiang MA
1
;
Yanqiong TANG
1
;
Hong LI
1
Author Information
- Publication Type:Journal Article
- Keywords: anticancer peptides; bioinformatics; machine learning; prediction model; protein language models
- MeSH: Machine Learning; Antineoplastic Agents/chemistry*; Humans; Peptides/chemistry*; Support Vector Machine; Algorithms; Computational Biology/methods*; Neoplasms/drug therapy*
- From: Chinese Journal of Biotechnology 2025;41(8):3252-3261
- CountryChina
- Language:Chinese
- Abstract: Cancer is a serious global health problem and a major cause of human death. Conventional cancer treatments often run the risk of impairing vital organ functions. Anticancer peptides (ACPs) are considered to be one of the most promising therapeutic agents against common human cancers due to their small sizes, high specificity, and low toxicity. Since ACP recognition is highly limited to the laboratory, expensive, and time-consuming, we proposed pLM4ACP, a model for predicting ACPs based on machine learning and protein language models. In this model, the protein language model ProtT5 was used to extract the features of ACPs, and the extracted features were input into the support vector machine (SVM) classification algorithm for optimization and performance evaluation. The model showcased significantly higher accuracy than other methods, with the overall accuracy of 0.763, F1-score of 0.767, Matthews correlation coefficient of 0.527, and area under the curve of 0.827 on the independent test set. This study constructs an efficient anticancer peptide prediction model based on protein language models, further advancing the application of artificial intelligence in the biomedical field and promoting the development of precision medicine and computational biology.
