HeMeNet: Heterogeneous Multichannel Equivariant Network for Protein Multitask Learning

• URL: http://arxiv.org/abs/2404.01693v1
• Date: Tue, 2 Apr 2024 06:53:45 GMT
• Title: HeMeNet: Heterogeneous Multichannel Equivariant Network for Protein Multitask Learning
• Authors: Rong Han, Wenbing Huang, Lingxiao Luo, Xinyan Han, Jiaming Shen, Zhiqiang Zhang, Jun Zhou, Ting Chen,
• Abstract summary: We propose a neural network model to address multiple tasks jointly upon the input of 3D protein structures. In particular, we first construct a standard structure-based multi-task benchmark called Protein-MT. Then, we develop a novel graph neural network for multi-task learning, dubbed Heterogeneous Multichannel Equivariant Network (HeMeNet)
• Score: 33.972536394058004
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Understanding and leveraging the 3D structures of proteins is central to a variety of biological and drug discovery tasks. While deep learning has been applied successfully for structure-based protein function prediction tasks, current methods usually employ distinct training for each task. However, each of the tasks is of small size, and such a single-task strategy hinders the models' performance and generalization ability. As some labeled 3D protein datasets are biologically related, combining multi-source datasets for larger-scale multi-task learning is one way to overcome this problem. In this paper, we propose a neural network model to address multiple tasks jointly upon the input of 3D protein structures. In particular, we first construct a standard structure-based multi-task benchmark called Protein-MT, consisting of 6 biologically relevant tasks, including affinity prediction and property prediction, integrated from 4 public datasets. Then, we develop a novel graph neural network for multi-task learning, dubbed Heterogeneous Multichannel Equivariant Network (HeMeNet), which is E(3) equivariant and able to capture heterogeneous relationships between different atoms. Besides, HeMeNet can achieve task-specific learning via the task-aware readout mechanism. Extensive evaluations on our benchmark verify the effectiveness of multi-task learning, and our model generally surpasses state-of-the-art models.

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