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Multi-label Metabolic Pathway Prediction with Auto Molecular Structure Representation Learning
Conference proceeding

Multi-label Metabolic Pathway Prediction with Auto Molecular Structure Representation Learning

Jiamin Chen, Jianliang Gao, Tengfei Lyu, Babatounde Moctard Oloulade, Xiaohua Hu and Jie Chen
2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp 171-176
09 Dec 2021
DOI: https://doi.org/10.1109/BIBM52615.2021.9669309

Abstract

Automatic Graph Representation Learning Biological system modeling Graph Neural Network Graph neural networks Manuals Metabolic Pathway Prediction Prediction algorithms Predictive models Representation learning Semantics
Understanding the relationship between molecular structure and metabolic pathway classes is significant for optimizing drug metabolization. In bioinformatics, graph neural networks (GNNs) can effectively capture structural and semantic features for molecular representation. Graph neural networks have become an essential method to encode molecular structures for multi-label prediction of metabolic pathways. However, building a GNN model for a given molecular structure dataset requires the manual design of GNN structure and fine-tuning of the hyperparameters for the GNN model, which is time-consuming and relies on expert experience. In this paper, we design an automatic end-to-end molecular structure representation learning framework named Auto-MSR that can design a GNN model for molecular structure encoding with little manual intervention. For a given compound molecule structure dataset, Auto-MSR first uses an efficient pa rallel GNN structure search algorithm to identify the optimal GNN structure from the GNN structure subspace. Then, it adopts a tree-structured parzen estimator approach to obtain the best hyperparameters of the GNN model in the hyperparameters subspace. We test AutoMSR on the dataset KEGG based on the multi-label metabolic pathway prediction task. The comparing results show that AutoMSR outperforms state-of-art manual graph neural networks on performance.

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