HeMeNet: Heterogeneous Multichannel Equivariant Network for Protein Multitask Learning
<|reference_start|>HeMeNet: Heterogeneous Multichannel Equivariant Network for Protein Multitask Learning: 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.<|reference_end|>
@article{han2024hemenet:,
    title={HeMeNet: Heterogeneous Multichannel Equivariant Network for Protein
  Multitask Learning},
    author={Rong Han, Wenbing Huang, Lingxiao Luo, Xinyan Han, Jiaming Shen,
  Zhiqiang Zhang, Jun Zhou, Ting Chen},
    journal={arXiv preprint arXiv:2404.01693},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.01693},
    primaryClass={cs.LG}
}