Authors :
Hilma; Karthika Krishnan; Lima P Subran; Neetha Joseph
Volume/Issue :
Volume 7 - 2022, Issue 7 - July
Google Scholar :
https://bit.ly/3IIfn9N
Scribd :
https://bit.ly/3AiQSh2
DOI :
https://doi.org/10.5281/zenodo.7016204
Abstract :
In the processes of drug development and
discovery, Drug-target interactions (DTIs) take part a
vital position. DTI prediction through laboratory
experiments consumes a lot of time. Also they were costly
and tiring. Although computational approaches can
recognize new interactions between drug-target pairs and
speed up the drug conversion procedures, some problems
like large scope of data and imbalanced class have been
encountered in the course of the prediction procedures,
and the number of unknown interactions were huge.
Therefore, an approach on the grounds of deep learning
(deepACTION) is put forward to predict possible or
unrevealed DTIs. Here, each drug chemical structure and
protein sequence is transformed according to structural
and sequence information using different descriptors to
correctly constitute their properties. In this method the
majority and minority instances in the dataset are
balanced using the SMOTE technique. For accurate DTI
prediction a convolutional neural network (CNN)
algorithm is trained with balanced and reduced features.
For comparing the performance of the DeepACTION
model with that of other methods AUC is regarded as the
primary evaluation metric. An AUC curve of 0.933 is
achieved by Deep ACTION model for the experimental
dataset acquired from the Drug Bank database. Based on
exper-imental results it is evident that the model is
capable to predict a remarkable number of new DTI’s
and it produce thorough knowledge that inspires
scientists to instigate advanced drugs.
Keywords :
Drug-target interaction, CNN, Data balancing
In the processes of drug development and
discovery, Drug-target interactions (DTIs) take part a
vital position. DTI prediction through laboratory
experiments consumes a lot of time. Also they were costly
and tiring. Although computational approaches can
recognize new interactions between drug-target pairs and
speed up the drug conversion procedures, some problems
like large scope of data and imbalanced class have been
encountered in the course of the prediction procedures,
and the number of unknown interactions were huge.
Therefore, an approach on the grounds of deep learning
(deepACTION) is put forward to predict possible or
unrevealed DTIs. Here, each drug chemical structure and
protein sequence is transformed according to structural
and sequence information using different descriptors to
correctly constitute their properties. In this method the
majority and minority instances in the dataset are
balanced using the SMOTE technique. For accurate DTI
prediction a convolutional neural network (CNN)
algorithm is trained with balanced and reduced features.
For comparing the performance of the DeepACTION
model with that of other methods AUC is regarded as the
primary evaluation metric. An AUC curve of 0.933 is
achieved by Deep ACTION model for the experimental
dataset acquired from the Drug Bank database. Based on
exper-imental results it is evident that the model is
capable to predict a remarkable number of new DTI’s
and it produce thorough knowledge that inspires
scientists to instigate advanced drugs.
Keywords :
Drug-target interaction, CNN, Data balancing
