Journal of Computer Applications ›› 2022, Vol. 42 ›› Issue (1): 325-332.DOI: 10.11772/j.issn.1001-9081.2021071218

• Frontier and comprehensive applications • Previous Articles    

Multi-aspect multi-attention fusion of molecular features for drug-target affinity prediction

Runze WANG1, Yueqin ZHANG1(), Qiqi QIN1, Zehua ZHANG1, Xumin GUO2   

  1. 1.College of Information and Computer,Taiyuan University of Technology,Taiyuan Shanxi 030600,China
    2.Department of Computer and Information Engineering,Shanxi Youth Vocational College,Taiyuan Shanxi 030032,China
  • Received:2021-07-14 Revised:2021-08-16 Accepted:2021-08-23 Online:2021-08-16 Published:2022-01-10
  • Contact: Yueqin ZHANG
  • About author:WANG Runze, born in 1997, M. S. candidate. His research interests include graph representation learning, biometric identification.
    ZHANG Yueqin, born in 1963, M. S., professor. Her research interests include data mining, intelligent information processing.
    QIN Qiqi, born in 1996, M. S. candidate. Her research interests include recommendation system.
    ZHANG Zehua, born in 1981, Ph. D., lecturer. His research interests include soft computing, machine learning, biometric identification, social network, complex network pattern analysis.
    GUO Xumin, born in 1987, M. S., lecturer. His research interests include big data.
  • Supported by:
    the National Natural Science Foundation of China(61702356);Industry-University Cooperation Education Program of Ministry of Education, Research Support Project for Returned Overseas Students in Shanxi Province


王润泽1, 张月琴1(), 秦琪琦1, 张泽华1, 郭旭敏2   

  1. 1.太原理工大学 信息与计算机学院,太原 030600
    2.山西青年职业学院 计算机与信息工程系,太原 030032
  • 通讯作者: 张月琴
  • 作者简介:王润泽(1997—),男,山东德州人,硕士研究生,CCF会员,主要研究方向:图表示学习、生物特征识别
  • 基金资助:


Recent deep learning achieves great attention on the tasks of Drug-Target Affinity (DTA). However, most existing works embed the molecular single structure as a vector, while ignoring the information gain provided by multi-aspect fusion of molecular features to the final feature representation. To address the feature incompleteness problem of single-structured molecules, an end-to-end deep learning method based on attentive fusion of multi-aspect molecular features was proposed for DTA prediction. Multi-aspect molecular structure embedding (Mas) and Multi-attention feature fusion (Mat) are the core modules of the proposed method. Firstly, the multi-aspect molecular structure was embedded into the feature vector space by Mas module. Secondly, the attention mechanism of molecular feature level was incorporated for the weighted fusion of molecular features from different aspects through Mat module. Thirdly, feature cascade of the above two was performed according to Drug-Target Interaction (DTI). Finally, the fully connected neural network was used to realize the regression prediction of the affinity. Experiments on Davis and KIBA datasets were carried out to evaluate the influence of training ratio, multi-aspect feature incorporation, multi-attention fusion, and related parameters on the performance of affinity prediction. Compared with the GraphDTA method, the proposed method has the Mean Square Error (MSE) reduced by 4.8% and 6% on the two datasets, respectively. Experimental results show that attentive fusion of multi-aspect molecular features can capture the molecular features that are more relevant for linkages on protein targets.

Key words: Drug-Target Affinity (DTA) prediction, multi-attention molecular feature fusion, multi-aspect molecular structure embedding, molecular feature level, attention mechanism



关键词: 药物-靶标亲和力预测, 多注意力分子特征融合, 多视角分子结构嵌入, 分子特征层级, 注意力机制

CLC Number: