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

蛋白质-配体的结合亲和力预测是药物重定位回归中具有挑战性的任务。深度学习方法可以有效预测蛋白质与配体相互作用的结合亲和力,减少药物发现的时间和成本。由此,基于长短期记忆模块(LSTM)和注意力机制模块(attention)提出了一种深度卷积神经网络模型(DLLSA)。模型由嵌入LSTM和空间注意力模块(spatial-attention)的卷积网络并行模块构建,其中LSTM模块针对蛋白质-配体接触特征的长序列信息,spatial-attention注意力模块聚集接触特征局部信息。采用PDBbind(v.2020)数据集进行训练,CASF-2013和CASF-2016数据集进行验证,模型的皮尔逊相关系数相比于PLEC模型分别提高了0.6%和3%,实验结果显著优于其他相关方法。

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.