[1]李大舟,陈思思,高 巍,等.基于改进 Attention Mask 编解码器 CPI 的研究[J].计算机技术与发展,2022,32(02):214-220.[doi:10. 3969 / j. issn. 1673-629X. 2022. 02. 035]
　LI Da-zhou,CHEN Si-si,GAO Wei,et al.Research on Compound-protein Interaction Classification Based on Improved Attention Mask Encoder-decoder[J].,2022,32(02):214-220.[doi:10. 3969 / j. issn. 1673-629X. 2022. 02. 035]
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基于改进 Attention Mask 编解码器 CPI 的研究()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
32
期数:
2022年02期
页码:
214-220
栏目:
应用前沿与综合
出版日期:
2022-02-10

文章信息/Info

Title:
Research on Compound-protein Interaction Classification Based on Improved Attention Mask Encoder-decoder
文章编号:
1673-629X(2022)02-0214-07
作者:
李大舟; 陈思思; 高 巍; 于锦涛
沈阳化工大学 计算机科学与技术学院,辽宁 沈阳 110142
Author(s):
LI Da-zhou; CHEN Si-si; GAO Wei; YU Jin-tao
School of Computer and Technology,Shenyang University of Chemical Technology,Shenyang 110142,China
关键词:
深度学习; 多头自注意力; 化合物蛋白相互作用; Item2vec; 编码器-解码器
Keywords:
deep learning; multi-head self-attention; compound-protein interaction; Item2vec; encoder-decoder
分类号:
TP31
DOI:
10. 3969 / j. issn. 1673-629X. 2022. 02. 035
摘要:
化合物-蛋白质相互作用(CPI) 的研究对药物发现有着重要作用,它可以为药物靶标选择提供有价值的信息,在一定程度上提高先导化合物的命中率,进而加快药物发现的进程。 由此提出了一种基于改进 Attention Mask 编解码器的化合物与蛋白质相互作用分类的预测模型,分别使用 RDkit 和 Item2vec 处理化合物的 SMILES 字符串和蛋白质的氨基酸序列,将得到的化合物和蛋白质低维特征表示的向量输入到该模型,通过分配权重的方式来计算蛋白质中的哪个子序列对化合物分子更重要,使用带有 Attention 机制的神经网络计算权重,模拟化合物和蛋白质之间的相互作用关系,最后作为一个二分类问题输出化合物和蛋白质是否相互作用的预测概率。 模型性能测评采用 ROC 曲线下面积、准确召回率曲线作为评价指标,实验结果表明,该模型相比于 GraphDTA 和 GCN 模型而言,拥有更好的性能表现,AUC 值提高了 0. 04 左右,PRC 值提高了 0. 07 左右。
Abstract:
The study of compound-protein interaction ( CPI) plays an important role in drug discovery,which can provide valuable information for drug target selection,improve the hit rate of lead compounds to some extent,and accelerate the process of drug discovery.Therefore,a prediction model of compound-protein interaction classification based on the improved Attention Mask encoder-decoder isproposed. RDkit and Item2vec are used to process the SMILES string of the compound and the amino acid sequence of the protein,andthe vector representation of low - dimensional characteristics of compounds and proteins is input into the model. The assigned weight isused to calculate which subsequence in the protein is more important for the compound molecule. The neural network with Attentionmechanism is to calculate the weight and simulate the interaction between the compound and the protein. Finally as a binary classification problem, output the predicted probability of whether the compound and the protein interact. The model performance evaluation uses thearea under the ROC curve and the accurate recall curve as evaluation indicators. According to the experimental results,this model hasbetter performance than the GraphDTA and GCN models,with the AUC value increased by about 0. 04, and the PRC value increased byabout 0. 07.

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更新日期/Last Update: 2022-02-10