HeMeNet: Heterogeneous Multichannel Equivariant Network for Protein Multitask Learning
arxiv(2024)
摘要
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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