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 improved by 1.53 and 3.07 percentage points， respectively. The results showed that Mul-JoG is able to accurately classify lipophilic molecules.

Key words:  , lipophilicity classification, molecules indicate learning, graph neural network, graph pooling strategy