人工智能在细胞穿透肽预测中的研究进展OA

Cell penetrating peptides(CPPs)refer to polypeptides that can enter cells through direct transport or endocytosis,and generally do not exceed 30 amino acids.CPPs can carry a variety of active substances into cells and is expected to become a new drug delivery carrier.The traditional experimental method to obtain CPPs has many problems,such as heavy workload,low flux and long cycle.With the development of computational biology,the artificial intelligence model based on machine learning algorithm improves the prediction efficiency of candidate CPPs.This paper introduces the prediction method of CPPs based on support vector machine,random forest,extreme learning machine,extreme random tree and deep learning,and discusses the influence of sequence feature extraction and insufficient training set on the accuracy of artificial intelligence prediction of CPPs.It is believed that with the development of artificial intelligence technology,researchers will be able to develop a CPPs prediction model with higher accuracy and stronger generalization ability.