陕西师范大学学报（自然科学版）2024，Vol.52Issue(3)：76-84,9.DOI:10.15983/j.cnki.jsnu.2024011
基于LSTM和注意力机制的蛋白质-配体结合亲和力预测
Prediction of protein-ligand binding affinity based on LSTM and attention mechanism
摘要
Abstract
Protein-ligand binding affinity prediction is a challenging task in drug repositioning regression.Deep learning methods can effectively predict the binding affinity of protein-ligand interactions,reducing the time and cost of drug discovery.This study proposes a deep convolutional neural network model(DLLSA)based on long short-term memory module(LSTM)and attention mechanism module.The model is constructed using a convolutional network parallel pattern embedded with LSTM and spatial attention module.The LSTM module focuses on the long sequence information of protein ligand contact features,while the spatial attention module aggregates local information of contact features.PDBbind(v.2020)dataset was used for training,and CASF-2013 and CASF-2016 datasets were used for validating.Pearson correlation coefficients of the model were improved by 0.6%and 3%compared to the PLEC model,and the experimental results were significantly better than the current correlation methods.关键词
结合亲和力/卷积神经网络/注意力机制/评分功能/机器学习Key words
binding affinity/convolution neural network/attention mechanism/scoring function/machine learning分类
信息技术与安全科学引用本文复制引用
王伟,吴世玉,刘栋,梁慧茹,史进玲,周运,张红军,王鲜芳..基于LSTM和注意力机制的蛋白质-配体结合亲和力预测[J].陕西师范大学学报（自然科学版）,2024,52(3):76-84,9.基金项目
国家自然科学基金(62072157) （62072157）
河南省科技攻关项目(242102211045,242102210001) （242102211045,242102210001）
河南师范大学高性能计算中心项目 （）
