细胞穿透肽
导线
计算机科学
肽
提取器
人工智能
化学
工程类
生物化学
大地测量学
工艺工程
地理
作者
Fan Zhang,Shun Zhang,Chun Fang
标识
DOI:10.1145/3627377.3627380
摘要
Cell-penetrating peptide, with their ability to traverse cell membranes and access the intracellular environment, has garnered significant attention in contemporary medical research. Predicting cell-penetrating peptide can expand the therapeutic scope, improve the therapeutic effect, and promote the development of drug delivery, gene therapy, and other fields. Compared with the traditional wet-lab method, predicting cell-penetrating peptide by computational methods has the advantages of low cost and accuracy. However, due to the difficulty of collecting cell-penetrating peptide datasets and the performance limitation of traditional feature extractors, Predicting the performance of cell-penetrating peptide using traditional computational methods has limitations. To address these problems, a cell-penetrating peptide prediction method named CPPGAN-ESM is proposed in this paper. CPPGAN-ESM firstly augments the cell-penetrating peptide dataset by using Deep Convolutional Generative Adversarial Network, and then fine-tunes the predictor based on the Evolutionary Scale Modeling 2 pre-trained feature extractor with the augmented dataset, to realize the prediction of the cell-penetrating peptide. Extensive experiments on a public dataset show that the method achieves an efficient performance of 0.979 AUC and 0.977 ACC in the cell-penetrating peptide prediction task.
科研通智能强力驱动
Strongly Powered by AbleSci AI