基于双向长短时记忆网络和自注意力机制的药物-药物相互作用预测  

Predicting Drug-Drug Interactions with BiLSTM and Self-Attention Mechanism

作　　者：张明香 顾海明[1] 于彬 ZHANG Mingxiang;GU Haiming;YU Bin(College of Mathematics and Physics,Qingdao University of Science and Technology,Qingdao 266061,China;College of Data Science,Qingdao University of Science and Technology,Qingdao 266061,China)

机构地区：[1]青岛科技大学数理学院,山东青岛266061 [2]青岛科技大学数据科学学院,山东青岛266061

出　　处：《青岛科技大学学报（自然科学版）》2024年第5期149-158,共10页Journal of Qingdao University of Science and Technology:Natural Science Edition

基　　金：国家自然科学基金项目(62172248);山东省自然科学基金项目(ZR2021MF098)。

摘　　要：提出了一种基于双层双向长短时记忆网络(bi-directional long short term memory,BiLSTM)和自注意力(self-attention)机制的药物-药物相互作用(drug-drug interactions,DDIs)预测方法SA-BiLSTM。首先,利用FP3指纹、MACCS指纹、Pubchem指纹和PaDEL分子描述符对药物特征信息进行提取。其次,使用套索回归(least absolute shrinkage and selection operator,Lasso)方法消除对分类无关的特征,并利用重复编辑最近邻(repeated edited nearest neighbors,RENN)方法对数据进行平衡处理,得到最优特征向量。最后,将最优特征向量输入结合自注意力机制和双向长短时记忆网络的分类器预测DDIs。基于五折交叉验证,同时与其它预测方法进行比较,本工作所提出的方法在两个数据集上获得较高的预测准确率。为了综合评价SA-BiLSTM的性能,对药物-药物相互作用网络进行验证。实验结果表明,SA-BiLSTM表现出优秀的预测能力,可以为DDIs的预测提供一种新的思路。In this paper,we propose a drug-drug interactions(DDIs)prediction method called SA-BiLSTM,which is based on bi-directional long short term memory(BiLSTM)and selfattention mechanism.First,the characteristic information of the drug is extracted using FP3 fingerprints,MACCS fingerprints,Pubchem fingerprints,and PaDEL molecular descriptors.Second,Lasso is applied to eliminate redundant features.Then,repeated edited nearest neighbors(RENN)method is used to balance the data to obtain the best feature vector.Finally,the best feature vectors are imported into the classifier combined with self-attention and BiLSTM for predicting DDIs.SA-BiLSTM achieves high prediction accuracy on both datasets based on 5-fold cross-validation and comparison with other prediction methods.To further evaluate the predictive performance of SA-BiLSTM,the drug-drug interaction net-work is validated.The experimental results show that SA-BiLSTM achieves better predic-tion and can provide a new idea for predicting DDIs.

关 键 词：药物-药物相互作用 特征提取 重复编辑最近邻 双向长短时记忆网络 自注意力机制 

分 类 号：Q811.4[生物学—生物工程]