Affinity2Vec: drug-target binding affinity prediction through representation learning, graph mining, and machine learning.

ABSTRACT: Drug-target interaction (DTI) prediction plays a crucial role in drug repositioning and virtual drug screening. Most DTI prediction methods cast the problem as a binary classification task to predict if interactions exist or as a regression task to predict continuous values that indicate a drug's ability to bind to a specific target. The regression-based methods provide insight beyond the binary relationship. However, most of these methods require the three-dimensional (3D) structural information of targets which are still not generally available to the targets. Despite this bottleneck, only a few methods address the drug-target binding affinity (DTBA) problem from a non-structure-based approach to avoid the 3D structure limitations. Here we propose Affinity2Vec, as a novel regression-base

SUBMITTER: Thafar MA 

PROVIDER: S-EPMC8934358 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature