DeepBindPoc: a deep learning method to rank ligand binding pockets using molecular vector representation.

ABSTRACT: Accurate identification of ligand-binding pockets in a protein is important for structure-based drug design. In recent years, several deep learning models were developed to learn important physical-chemical and spatial information to predict ligand-binding pockets in a protein. However, ranking the native ligand binding pockets from a pool of predicted pockets is still a hard task for computational molecular biologists using a single web-based tool. Hence, we believe, by using closer to real application data set as training and by providing ligand information, an enhanced model to identify accurate pockets can be obtained. In this article, we propose a new deep learning method called DeepBindPoc for identifying and ranking ligand-binding pockets in proteins. The model is built by using inf

SUBMITTER: Zhang H 

PROVIDER: S-EPMC7144620 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature