Predicting target-ligand interactions with graph convolutional networks for interpretable pharmaceutical discovery.

ABSTRACT: Drug Discovery is an active research area that demands great investments and generates low returns due to its inherent complexity and great costs. To identify potential therapeutic candidates more effectively, we propose protein-ligand with adversarial augmentations network (PLA-Net), a deep learning-based approach to predict target-ligand interactions. PLA-Net consists of a two-module deep graph convolutional network that considers ligands' and targets' most relevant chemical information, successfully combining them to find their binding capability. Moreover, we generate adversarial data augmentations that preserve relevant biological backgrounds and improve the interpretability of our model, highlighting the relevant substructures of the ligands reported to interact with the protein targ

SUBMITTER: Ruiz Puentes P 

PROVIDER: S-EPMC9119967 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature