Novel drug-target interactions via link prediction and network embedding.

ABSTRACT:

Background

As many interactions between the chemical and genomic space remain undiscovered, computational methods able to identify potential drug-target interactions (DTIs) are employed to accelerate drug discovery and reduce the required cost. Predicting new DTIs can leverage drug repurposing by identifying new targets for approved drugs. However, developing an accurate computational framework that can efficiently incorporate chemical and genomic spaces remains extremely demanding. A key issue is that most DTI predictions suffer from the lack of experimentally validated negative interactions or limited availability of target 3D structures.

Results

We report DT2Vec, a pipeline for DTI prediction based on graph embedding and gradient boosted tree classification. It maps drug-

SUBMITTER: Amiri Souri E 

PROVIDER: S-EPMC8978405 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature