Peptide-Based Drug Predictions for Cancer Therapy Using Deep Learning

Yih Yun Sun, Tzu Tang Lin, Wen Chih Cheng, I. Hsuan Lu, Chung Yen Lin, Shu Hwa Chen

    研究成果: 雜誌貢獻文章同行評審

    6 引文 斯高帕斯(Scopus)


    Anticancer peptides (ACPs) are selective and toxic to cancer cells as new anticancer drugs. Identifying new ACPs is time-consuming and expensive to evaluate all candidates’ anticancer abilities. To reduce the cost of ACP drug development, we collected the most updated ACP data to train a convolutional neural network (CNN) with a peptide sequence encoding method for initial in silico evaluation. Here we introduced PC6, a novel protein-encoding method, to convert a peptide sequence into a computational matrix, representing six physicochemical properties of each amino acid. By integrating data, encoding method, and deep learning model, we developed AI4ACP, a user-friendly web-based ACP distinguisher that can predict the anticancer property of query peptides and promote the discovery of peptides with anticancer activity. The experimental results demonstrate that AI4ACP in CNN, trained using the new ACP collection, outperforms the existing ACP predictors. The 5-fold cross-validation of AI4ACP with the new collection also showed that the model could perform at a stable level on high accuracy around 0.89 without overfitting. Using AI4ACP, users can easily accomplish an early-stage evaluation of unknown peptides and select potential candidates to test their anticancer activities quickly.

    出版狀態已發佈 - 4月 2022

    ASJC Scopus subject areas

    • 分子醫學
    • 藥學科學
    • 藥物發現


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