MGPLI: exploring multigranular representations for protein-ligand interaction prediction

Abstract

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.

Availability and implementation: The model scripts are available at https://github.com/IILab-Resource/MGDTA.git.