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

Abstract:

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