BatmanNet: bi-branch masked graph transformer autoencoder for molecular representation

Author:

Wang Zhen12,Feng Zheng34,Li Yanjun5678,Li Bowen2,Wang Yongrui2,Sha Chulin2,He Min12,Li Xiaolin29

Affiliation:

1. College of Electrical and Information Engineering, Hunan University , Changsha, 410082, Hunan , China

2. Hangzhou Institute of Medicine, Chinese Academy of Sciences , Hangzhou, 310018, Zhejiang , China

3. Department of Health Outcomes & Biomedical Informatics , College of Medecine, , Gainesville, 32611, FL , USA

4. University of Florida , College of Medecine, , Gainesville, 32611, FL , USA

5. Department of Medicinal Chemistry , College of Pharmacy, , Gainesville, 32610, FL , USA

6. University of Florida , College of Pharmacy, , Gainesville, 32610, FL , USA

7. Center for Natural Products , Drug Discovery and Development, , Gainesville, 32610, FL , USA

8. University of Florida , Drug Discovery and Development, , Gainesville, 32610, FL , USA

9. ElasticMind Inc , Hangzhou, 310018, Zhejiang , China

Abstract

Abstract Although substantial efforts have been made using graph neural networks (GNNs) for artificial intelligence (AI)-driven drug discovery, effective molecular representation learning remains an open challenge, especially in the case of insufficient labeled molecules. Recent studies suggest that big GNN models pre-trained by self-supervised learning on unlabeled datasets enable better transfer performance in downstream molecular property prediction tasks. However, the approaches in these studies require multiple complex self-supervised tasks and large-scale datasets , which are time-consuming, computationally expensive and difficult to pre-train end-to-end. Here, we design a simple yet effective self-supervised strategy to simultaneously learn local and global information about molecules, and further propose a novel bi-branch masked graph transformer autoencoder (BatmanNet) to learn molecular representations. BatmanNet features two tailored complementary and asymmetric graph autoencoders to reconstruct the missing nodes and edges, respectively, from a masked molecular graph. With this design, BatmanNet can effectively capture the underlying structure and semantic information of molecules, thus improving the performance of molecular representation. BatmanNet achieves state-of-the-art results for multiple drug discovery tasks, including molecular properties prediction, drug–drug interaction and drug–target interaction, on 13 benchmark datasets, demonstrating its great potential and superiority in molecular representation learning.

Funder

National Key Research and Development Program of China

Zhejiang Province Soft Science Key Project

Publisher

Oxford University Press (OUP)

Subject

Molecular Biology,Information Systems

Cited by 3 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Quantum-Informed Molecular Representation Learning Enhancing ADMET Property Prediction;Journal of Chemical Information and Modeling;2024-06-25

2. Graph Transformer GANs With Graph Masked Modeling for Architectural Layout Generation;IEEE Transactions on Pattern Analysis and Machine Intelligence;2024-06

3. Morphological profiling for drug discovery in the era of deep learning;Briefings in Bioinformatics;2024-05-23

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