DeepDTA: deep drug-target binding affinity prediction.

ABSTRACT:

Motivation

The identification of novel drug-target (DT) interactions is a substantial part of the drug discovery process. Most of the computational methods that have been proposed to predict DT interactions have focused on binary classification, where the goal is to determine whether a DT pair interacts or not. However, protein-ligand interactions assume a continuum of binding strength values, also called binding affinity and predicting this value still remains a challenge. The increase in the affinity data available in DT knowledge-bases allows the use of advanced learning techniques such as deep learning architectures in the prediction of binding affinities. In this study, we propose a deep-learning based model that uses only sequence information of both targets and drugs to pred

SUBMITTER: Ozturk H 

PROVIDER: S-EPMC6129291 | biostudies-literature | 2018 Sep

REPOSITORIES: biostudies-literature