An image-based protein-ligand binding representation learning framework via multi-level flexible dynamics trajectory pre-training.

ABSTRACT:

Motivation

Accurate prediction of protein-ligand binding (PLB) relationships plays a crucial role in drug discovery, which helps identify drugs that modulate the activity of specific targets. Traditional biological assays for measuring PLB relationships are time consuming and costly. In addition, models for predicting PLB relationships have been developed and widely used in drug discovery tasks. However, learning more accurate PLB representations is essential to meet the stringent standards required for drug discovery.

Results

We propose an image-based PLB representation learning framework, called ImagePLB, which equips ligand representation learner (LRL) and protein representation learner (PRL) to accept 3D multi-view ligand images and protein graphs as input, respectively,

SUBMITTER: Xiang H 

PROVIDER: S-EPMC12502907 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature