MGPLI: exploring multigranular representations for protein-ligand interaction prediction

作者：Wang, Junjie; Hu, Jie; Sun, Huiting; Xu, MengDie; Yu, Yun; Liu, Yun^*; Cheng, Liang^*

来源：Bioinformatics, 2022, 38(21): 4859-4867.

DOI：10.1093/bioinformatics/btac597

• NEURAL-NETWORK
• BINDING-AFFINITY
• DATABASE

Motivation: The capability to predict the potential drug binding affinity against a protein target has always been a fundamental challenge in silico drug discovery. The traditional experiments in vitro and in vivo are costly and time-consuming which need to search over large compound space. Recent years have witnessed significant success on deep learning-based models for drug-target binding affinity prediction task. @@@ Results: Following the recent success of the Transformer model, we propose a multigranularity protein-ligand interaction (MGPLI) model, which adopts the Transformer encoders to represent the character-level features and fragment-level features, modeling the possible interaction between residues and atoms or their segments. In addition, we use the convolutional neural network to extract higher-level features based on transformer encoder outputs and a highway layer to fuse the protein and drug features. We evaluate MGPLI on different protein-ligand interaction datasets and show the improvement of prediction performance compared to state-of-the-art baselines.

• 单位
  中国医学科学院; 哈尔滨医科大学; y; 河北医科大学; 1