计算机与现代化Issue(3)：12-21,10.DOI:10.3969/j.issn.1006-2475.2025.03.003
多级联合图嵌入亲脂性分子分类
Multilevel Joint Graph Embedding for Lipophilic Molecular Classification
摘要
Abstract
Classification of lipophilic molecules is an important area of research in the fields of bioinformatics and chemistry,where the goal is to efficiently classify molecules in terms of lipophilicity on the basis of their chemical structure and functional characteristics.However,due to the complex and diverse properties of lipophilic molecules,the traditional graph neural network classification methods fail to effectively extract the hierarchical relationships within the molecule and fully consider the structural information of the molecule when dealing with this type of problem,which results in the loss of information about the key atoms and the lack of global structural information.To address the above problems,a Multilevel Joint Graph Embedding Network(Mul-JoG)is proposed.Mul-JoG fuses Graph Transformer and graph pooling strategies to construct network layers,by concatenating the outputs of different network layers,and each layer fuses the information from all previous layers to form a multi-level joint graph embedding network.By obtaining the topological structure of molecules from multiple perspectives,the network captures the global information and interactions of molecules,effectively modeling the complex structure of molecules,and realizing the accurate classification of lipophilic molecules.The experimental results on the drug molecule dataset show that Mul-JoG achieved 97.96%and 92.94%in AUC and ACC,respectively.Compared with the benchmark method,the AUC and ACC is im-proved by 1.53 and 3.07 percentage points,respectively.The results showed that Mul-JoG is able to accurately classify lipophilic molecules.关键词
亲脂性分类/分子表示学习/图神经网络/图池化策略Key words
lipophilicity classification/molecules indicate learning/graph neural network/graph pooling strategy分类
计算机与自动化引用本文复制引用
曹璐,丁苍峰,马乐荣,延照耀,游浩..多级联合图嵌入亲脂性分子分类[J].计算机与现代化,2025,(3):12-21,10.基金项目
国家自然科学基金资助项目(62262067) （62262067）
陕西省人才项目(YAU202213065,CXY202107) （YAU202213065,CXY202107）
延安大学十四五重大科研项目(2021ZCQ012) （2021ZCQ012）
延安大学研究生教育创新计划项目(YCX2023006) （YCX2023006）
