HeMeNet: Heterogeneous Multichannel Equivariant Network for Protein
  Multitask Learning
Rong Han Wenbing Huang Lingxiao Luo Xinyan Han Jiaming Shen Zhiqiang Zhang Jun Zhou Ting Chen
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.