基于LSTM和注意力机制的蛋白质-配体结合亲和力预测OA北大核心CSTPCD

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.