DEEPScreen: high performance drug-target interaction prediction with convolutional neural networks using 2-D structural compound representations.

ABSTRACT: The identification of physical interactions between drug candidate compounds and target biomolecules is an important process in drug discovery. Since conventional screening procedures are expensive and time consuming, computational approaches are employed to provide aid by automatically predicting novel drug-target interactions (DTIs). In this study, we propose a large-scale DTI prediction system, DEEPScreen, for early stage drug discovery, using deep convolutional neural networks. One of the main advantages of DEEPScreen is employing readily available 2-D structural representations of compounds at the input level instead of conventional descriptors that display limited performance. DEEPScreen learns complex features inherently from the 2-D representations, thus producing highly accurate p

SUBMITTER: Rifaioglu AS 

PROVIDER: S-EPMC7643205 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature