# FRnet-DTI: Deep Convolutional Neural Networks with Evolutionary and
Structural Features for Drug-Target Interaction

Farshid Rayhan<EMAIL_ADDRESS>Sajid Ahmed  Zaynab Mousavian
Dewan Md Farid  Swakkhar Shatabda  Department of Computer Science and
Engineering, United International University, Bangladesh  Department of
Computer Science, University of Tehran, Iran

###### Abstract

The task of drug-target interaction prediction holds significant importance in
pharmacology and therapeutic drug design. In this paper, we present FRnet-DTI,
an auto encoder and a convolutional classifier for feature manipulation and
drug target interaction prediction. Two convolutional neural neworks are
proposed where one model is used for feature manipulation and the other one
for classification. Using the first method FRnet-1, we generate 4096 features
for each of the instances in each of the datasets and use the second method,
FRnet-2, to identify interaction probability employing those features. We have
tested our method on four gold standard datasets exhaustively used by other
researchers. Experimental results shows that our method significantly improves
over the state-of-the-art method on three of the four drug-target interaction
gold standard datasets on both area under curve for Receiver Operating
Characteristic(auROC) and area under Precision Recall curve(auPR) metric. We
also introduce twenty new potential drug-target pairs for interaction based on
high prediction scores.
Codes Available: https://github.com/farshidrayhanuiu/FRnet-DTI/
Web Implementation: http://farshidrayhan.pythonanywhere.com/FRnet-DTI/

###### keywords:

Class imbalance, Drug-Target, Classification, Ensemble classifier, Feature
engineering

##  1 Introduction

The task of drug-target interaction prediction is very important in
pharmacology and therapeutic drug design. This problem can be addressed in
several ways. Firstly, for a already developed drug compound the task is to
find new targets with which the drug might have interactions. Secondly, for a
given target protein one might search for potential drugs in the library.
Another way to tackle the problem is to find the possibility of interaction
given a pair of drug and target protein. In this paper, we are interested in
the latter kind. Experimental methods in predicting drug-protein interactions
are expensive and time consuming and hence computational methods have been
used extensively in the recent years [21, 30].

One of the most successful computational method in drug-target interaction
prediction is docking simulations [29]. This method largely depends on the
availability of three dimensional native structure of the target protein
determined by sophisticated methods like X-Ray Crystallography. However, X-Ray
Crystallography is itself a time-consuming and expensive process and thus the
native structure of the targets proteins are often unavailable. These have
encouraged the researchers to apply machine learning based methods to tackle
the prediction problem by formulating it in a supervised learning setting
[34].

Success of supervised learning methods largely depend on the training
datasets. In a pioneering work on drug-target interaction prediction,
Yamanishi et al. [51] proposed gold standard datasets with four sets or target
proteins. Machine learning methods used in prediction of drug-target
interaction often use features generated from molecular fingerprints of drugs
[33] and sequence or structure based information [39]. A good number of
machine learning algorithms have been used in the literature of supervised
drug-target interaction prediction that includes: Support Vector Machines
(SVM) [33], Boosting [39], Deep Learning [6], etc. One of the major obstacle
in drug-target interaction prediction is due to the imbalance in the dataset.
Since the known validated interaction among drug target pairs are not large,
most of the approaches considers the unknown interactions as negative samples
and thus they outnumber positive samples. The representation of the drug-
target pair in the supervised learning dataset is another added challenge.

In the recent years, Chemo-genomic methods have received a lot of attention
for identifying drug target interaction. They usually include methods like
graph theory [47, 8], deep learning [6] , machine learning [51, 4] and network
analysis methods [2, 10]. In supervised learning setting, K-Nearest
Neighbor(KNN) [23], fuzzy logic [50], support vector machines [33, 27] are the
most commonly used classification algorithm. In [51], drug target interaction
problem was first introduced as a supervised problem and a gold standard
dataset was proposed. Later those datasets have been exhaustively used by
researchers. The same authors from [51], applied distance based learning to
association among pharmacological space of drug target interactions. A non-
linear kernel fusion with regularized least square method was proposed by
[22].

[18] used Chemical and genomic kernels and Bayesian factorization. Another
method , DBSI (drug based similarity interface) [10] proposed two dimensional
chemical-structural similarity for drug similarity. Later methods like
DASPfind [3], NetCBP [7] , SELF-BLM [27] were proposed in order to solve the
problem. Bigram based features as fingerprints, extracted from position
specific scoring matrix, were found very helpful solving the drug target
interaction problem [33]. Most of the supervised learning methods do not
exploit the structure based features because most protein targets’ three
dimensional native structure are not available. [24] used extremely randomized
trees as classifier. The authors of that paper represented the drugs as
molecular fingerprint and proteins as pseudo substitution matrix. Theses
matrix were generated from its amino acid sequence information. Other relevant
works include self-organizing theory [11, 13], similarity based methods [53],
ensemble methods [14, 15]. A in depth literature review was done by Chen et
al. on computational methods for this particular problem [9].

One of the most recent work was done by [48], where the authors presented a
model which consisted of multiple stacked RBM. The output layer consisted of 2
neurons each predicting the interaction and non-interaction probability
respectively. [6] also presented a model that used deep representations for
drug target interaction predictions. In our recent work, iDTI-ESBoost [39], we
exploited evolutionary features along with structural features to predict drug
protein interaction. SPIDER3, a successful secondary structural prediction
tool [32, 45], was used to generate a novel set of features for supervised
learning. The novel set of features include seven primary set of features. A
short description of each feature set is given in S1 of the supplementary
material . In that paper, two balancing methods were used to handle the
imbalance ratio of the datasets, and Adaboost [16] was used for
classification.

In this paper, we propose two deep convolutional methods for feature
manipulation and predicting drug target interaction. There are named FRnet-1
and FRnet-2 where FRnet-1 is used to generate 4096 features or deep
representation of each datasets and FRnet-2 is used for classification using
the extracted features. We use the latest version of 4 gold standard datasets
with 1476 features to test our method. In the experimental results using both
of our method as one, we have observed magnificent auROC and auPR metric
scores and therefore we strongly claim that our method is an excellent
alternative for most other proposed methods for Drug-Target-Interaction (DTI).

Figure 1: Architecture of FRnet-1

##  2 Methodology

This section provides a description of the methodology used in this paper:
algorithmic details, datasets and performance evaluation methods.

###  2.1 Convolutional Models

In this paper, we propose two novel deep learning architectures for drug
target interaction, each network having its own purpose and goal. Throughout
the rest of the paper these two models are referred as FRnet-1 and FRnet-2.
FRnet-1 is used as a auto encoder that extracts 4096 features from the given
feature sets which is than fed as input to FRnet-2 for classification.

The proposed models FRnet-1 and FRnet-2 both have over millions of hyper-
parameter to tune. While exhaustively tuning each of the parameter would
provide the best result it is seldom done as it requires tremendous
computational cost and time and risks over-fit. Four of the most effective
hyper parameters is chosen [19] to tune and from a given range. Aggressive
regularization is also employed to remove over-fitting to maximum extend.

###  2.2 Intuition

Recently, Deep learning methods have been receiving a lot of attention for
biological applications. In the article [12], the authors used a model called
Wide-and-Deep. Authors of [48] showed impressive improvement in prediction
capability using deep learning.

The authors of [46] used stacked auto-encoders to further improve the problem
of DTI. We closely follow that article and employ an auto encoder to extract
features and a classifier for final classification. While the above mentioned
papers address a separate problem of DTI using deep learning their intuition
behind the architectural design wasn’t very clear.

We follow the architectural design of GoogleNet [43] which is regarded as one
of the most successful classification network [44, 42]. They use a module
called the inception module (see fig: 3) where convolution operation with
different filters sizes are done in parallel with a pooling operation. Each of
the output are then merged together as the final output of the module. This
process reliefs the burden of choosing of proper filter size or operation type
between convolution and pooling. Following this intuition, we design our two
models which are further explained below.

####  2.2.1 FRnet-1

In order to perform a convolution operation, a 4D tensor with the shape
$[X,a,b,c]$ is required [1] where $a,b,c$ are the 3d representation of the
features and $X$ is the input batch size. The latest version of the gold
standard datasets were represented with 1476 features by [39] and showed very
optimistic results. For convenience, a new feature with value 0 was added
which extended the feature length to 1476 so that the input can be reshaped
into $[X,211,7,1]$. Here 211 and 7 are unique numbers and interchanging them
has no effect and 1 represents that the input has only 1 channel. Basically
the dataset is represented as a gray scale $211\times 7$ sized image.

This model consists of several convolutional layers, Max-Pooling layer and
Fully-connected layer. Fig. 1 shows the visual representation of the complete
network. Input layer takes the input in the shape of $[X,211,7,1]$ and passes
to a $1\times 1$ Convolutional layer with 32 filters and 2 strides which
outputs a tensor with the shape $[X,106,4,32]$. Here $1\times 1$ convolution
means the size of the filter were $1\times 1$. ‘Relu’ activation, ‘SAME’
padding and ‘L2’ regularizer were used in each layer. After convolution, a
Max-Pool operation is done with a kernel and stride value of 2 to reduce the
tensor shape to $[X,53,2,32]$. This network output is then fed to four
parallel processes. They are depicted in Fig. 1 from left to right. The first
process is a $1\times 1$ convolution with 8 filters followed by a $3\times 3$
convolution with 64 filter. Next is a $1\times 1$ convolution with 8 filters
followed by a $2\times 2$ convolution with 64 filters. Then, there is a
$1\times 1$ convolution with 8 filters followed by a $5\times 5$ convolution
with 32 filters and lastly, a Max-pool operation with kernel and stride 1. In
the next stage, a merge operator combines the 4 network outputs on the 3rd
axis of the resulting tensor with the shape $[X,53,2,192]$. Detailed
description is provided in table 1. The merged network is then fed to fully
connected layers with 4096 and 2048 neurons followed by a dropout operation
with $Keep\\_Prb$ value at $0.5$. Finally the output layer consists of 1476
neurons each neuron representing a feature value of an instance in the
dataset. The model was trained with a learning rate 0.001, ‘$Adam$’ as
optimizer [28] and $binary\\_crossentropy$ as loss function. The fully
connected layer with 4096 neurons is used as features to predict interaction
probability using the FRnet-2 method. The model achieved 0.85% accuracy just
after 3 iterations and reached over 90% after 20 iteration. Accuracy curves
with respect to each iteration are described in figure 2.

|
---|---
(a) | (b)
|
(c) | (d)
Figure 2: Accuracy curves of FRnet-1using $Binary\ crossentropy$ as loss function on four datasets: (a) enzymes (b) ion channels (ic) (c) GPCRs (d) nuclear receptors (nr). Index | Input shape | Output shape after 1st ConV | Output shape after 2nd ConV/Max-pool
---|---|---|---
a | [X, 53, 2, 32] | [X, 53, 2, 16] | [X, 53, 2, 64]
b | [X, 53, 2, 32] | [X, 53, 2, 16] | [X, 53, 2, 64]
c | [X, 53, 2, 32] | [X, 53, 2, 16] | [X, 53, 2, 32]
d | [X, 53, 2, 32] | - | [X, 53, 2, 32]
|  | Tensor shape after merge operation | [X, 53, 2, 192]
Table 1: Shapes of tensor after each convolution operation leading up to the
merge operation. Also know as Inception Operation

The merge operation is used to retire the burden of choosing filter size from
$1\times 1$, $2\times 2$ and $5\times 5$. In stead, the model exploits all of
them and chooses the better set of features by itself. This concept was
inspired form the $inception$ model [43] which was later used to build the
various versions of imageNet by the same authors [44, 42]. However, this model
also increases computational complexity as different sized filters are used at
the same time. In order to reduce some computational cost, FRnet-1 employs
$1\times 1$ convolution before and after each convolution operation with
different filter size to reduce computational complexity of the model. This
concept was first introduced in 2013 in the article by [31]. They showed that
$1\times 1$ convolutional operations can be used as tool to reduce channel
size of a tensor. The hypothesis of [31] states that converting a tensor form
shape $[X,106,4,32]$ to $[X,53,2,192]$ will cost much more than converting
$[X,106,4,32]$ to $[X,106,4,16]$ using a $1\times 1$ convolution using 16
filters than converting $[X,106,4,16]$ to $[X,106,4,192]$ and have the same
effect on the network. This same methodology is later incorporated in FRnet-2
also.

####  2.2.2 FRnet-2

FRnet-2 serves for the purpose of classifying interaction probability between
a given drug-target pair. Similar to FRnet-1 this model employs the inception
module (details figure of the model is given in figure 3). It uses the 4096
features generated by FRnet-1 as a $64\times 64\times 1$ shaped instance. In
this model, the first convolution and Max-Pool operation is kept the same as
the previous method. Following those operations, the tensors are parallelly
fed to 2 inception modules, one with stride size of 1 (left module of Fig. 4)
and another with stride size 2.

The model merges those outputs in order to take befit of both stride size and
put it through a final inception layer before connecting in to fully connected
layers of 2048, 512 and finally 1 neuron for prediction. Similar to FRnet-1
this model also uses $L2$ regularization, ‘$Adam$’ as optimizer with
$Binary\\_crossentropy$ as loss function with learning rate set to 0.001 and
$Keep\\_Prb$ value set to 0.5 in the dropout layer.

Figure 3: Inception module from [44, 43, 42] Figure 4: Architecture of FRnet-2

###  2.3 Datasets

The benchmark datasets used in this article were first introduced by [52] in
2010 using DrugBank [49], KEGG [26], BRENDA [41] and SuperTarget [20] to
extract information about drug-target interactions. These datasets are
regarded as $gold\ standard$ and have been exhaustively been used by
researchers throughout the years [33, 6, 9, 39]. These datasets are publicly
available at: http://web.kuicr.kyoto-u.ac.jp/supp/yoshi/drugtarget/.

In this paper, an extended version of those dataset is used which consists of
structural and evolutionary features. This version of dataset was first
introduced in 2016 by [33] and later further extended in 2017 by [39]. The
exacted dataset from [39] were used in this project for experimentation. A
short description of each dataset is given in table 2.

Dataset | Drugs | Proteins | Positive Interactions | Imbalance Ratio
---|---|---|---|---
Enzyme | 445 | 664 | 2926 | 99.98
Ion Chanel | 210 | 204 | 1476 | 28.02
GPCR | 223 | 95 | 635 | 32.36
Nuclear Receptor | 54 | 26 | 90 | 14.6
Table 2: Description of the gold standard datasets with structural and
evolutionary features [39]

###  2.4 Performance Evaluation

A wide variety of performance metrics are available to show case and compare
performance of classification models. Even though the $accuracy$ metric is
sufficient enough to show the accuracy percentage of a model, in highly
imbalanced dataset, such as gold standard datasets used in this experiment,
that value holds little to no significance. In imbalance binary datasets, one
class highly outnumbers samples of other class thus a measure of accuracy in
this case makes little sense. In these type of cases, sensitivity and
specificity thresholds are a very useful metric.

Assume, P denotes the number of positive samples and N denotes the negative
number of samples of a dataset. Also lets assume, $TN$ is the number of true
negative and $TP$ is true positive. Similarly $FP$ represents false positive
and $FN$ true negative. False positive means the negative sample that the
classifier wrongly predicted as positive and false negative means a positive
sample that the model has classifier predicted as negative. Conversely, True
positive and true negative denotes the correctly classified positive and
negative samples.

Now from these, we can represent true positive rate or sensitivity rate as
follows:

$Sensitivity=\frac{TP}{TP+FN}$ (1)

Sensitivity represents the ratio of correctly predicted positive samples.
Precision is the definition of the positive predictive rate (PPV). It is
defined as follows:

$Precision=\frac{TP}{TP+FP}$ (2)

Precision shows precision as the percentage of accurate positive predictions
of the classifiers. Specificity (SPC) or true negative rate is another
important metric. It is defined as in Eq. 3. False positive rate (FPR) is the
representation of the ratio of the number of misclassified negative samples.

$Specificity=\frac{TN}{TN+FP}$ (3)

$FPR=\frac{FP}{TN+FP}=1-Specificity$ (4)

Also there are two other metrics, who are independent from the imbalance ratio
of the datasets, called area under curve for Receiver Operating
Characteristic(auROC) and area under Precision Recall curve(auPR). Due to
their ignorance towards the imbalance ratio of datasets, they have been widely
used [39, 6, 7, 5] as standard metric for comparison. Both metrics value range
from 0 to 1 where a random classifier should have a score of 0.5 and a perfect
classification model will have a auPR and auROC score of 1. In both cases the
higher the value the better.

Another important factor is the balance of bias and variance trade off [17].
$k$-fold cross validation and jack knife tests are mostly used as an attempt
to solve the bias-variance problem. In our experiment, we used $5$-fold
shuffled cross validation on each datasets. Each time the dataset is shuffled
and spitted into 5 equal parts. Then 4 of them are used for training and the
rest for testing.

##  3 Results and Discussion

In the experiments reported in this paper, Python v3.6, $Tensorflow$ Library
and Sci-kit learn [36] were used for the implementation. Each experiments were
executed 10 times and the average result was considered. Each dataset were
split into two sets, train set and test set using 5 fold cross validation.

Dataset | Classifier | auPR | auROC
---|---|---|---
enzymes | Decision Tree | 0.28 | 0.9376
| SVM | 0.53 | 0.9010
| MEBoost | 0.41 | 0.9404
| CUSBoost | 0.71 | 0.9345
| FRnet-2 | 0.70 | 0.9754
GPCR | Decision Tree | 0.31 | 0.9038
| SVM | 0.44 | 0.8859
| MEBoost | 0.46 | 0.9075
| CUSBoost | 0.65 | 0.8989
| FRnet-2 | 0.69 | 0.9512
Ion Channel | Decision Tree | 0.29 | 0.933
| SVM | 0.40 | 0.8904
| MEBoost | 0.39 | 0.928
| CUSBoost | 0.45 | 0.8851
| FRnet-2 | 0.49 | 0.9478
NR | Decision Tree | 0.46 | 0.8147
| SVM | 0.41 | 0.7605
| MEBoost | 0.23 | 0.9165
| CUSBoost | 0.71 | 0.8989
| FRnet-2 | 0.73 | 0.9241

Table 3: A comparison of performances among FRnet-2 and other classifiers on
the gold standard datasets in terms of auROC and auPR using 4096 features
generated by FRnet-1.

FRnet-1 method is multilayer deep auto-encoder that uses convolution, max-pool
and fully connected layers to regenerate the input as output in the final
fully connected layer. For each of the datasets, the model was trained to
achieve accuracy over of 95%. Due to the use of aggressive regularization,
$Keep\\_Prb$ value of 0.5 in dropout and $L2$ regularization in each layer
using learning rate of 0.001 to avoid over fitting, the models were unable to
achieve accuracy higher than 97% for any of the datasets. The first fully
connected layer in the network has 4096 neurons in the output and those were
used to extract 4096 features from each dataset. For a fair sake of
comparison, FRnet-2 was tested with several state of the art machine learning
algorithms like, Decision Tree [40], SVM [25], MEBoost [38] and CUSBoost [37].
Each of these classifiers were fed the 4096 features generated by FRnet-1.
Results in terms of auPR and auROC are given Table 3. Table 3 shows that
FRnet-2 is able to produce results with better auROC for all the datasets.
However, in terms of auPR the results in three datasets are better than the
competitor algorithms. However, for the ‘enzymes’ dataset, the performance of
FRnet-2 is very close to the best performing CUSBoost. Note that other
classifiers also achieved very impressive auROC and auPR score which shows the
effectiveness of the features generated by FRnet-1. Therefore even though
FRnet-1 is designed for feature manipulation of the four datasets mentioned in
this article, it can be used as a strong feature manipulation tool on other
domains as well.

Dataset | Reference | Classifier | auPR | auROC
---|---|---|---|---
enzymes | [39] | AdaBoost | 0.68 | 0.9689
| [39] | Random Forest | 0.43 | 0.9457
| [33] | SVM | 0.54 | 0.9194
|  | FRnet-2 | 0.70 | 0.9754
GPCR | [39] | AdaBoost | 0.31 | 0.9128
| [39] | Random Forest | 0.30 | 0.9168
| [33] | SVM | 0.28 | 0.8720
|  | FRnet-2 | 0.69 | 0.9512
Ion Channel | [39] | AdaBoost | 0.48 | 0.9369
| [39] | Random Forest | 0.40 | 0.9234
| [33] | SVM | 0.39 | 0.8890
|  | FRnet-2 | 0.49 | 0.9512
NR | [39] | AdaBoost | 0.79 | 0.9285
| [39] | Random Forest | 0.29 | 0.7723
| [33] | SVM | 0.41 | 0.8690
|  | FRnet-2 | 0.73 | 0.9241

Table 4: A performance comparison among FRnet-2 with AdaBoost, Support Vector
Machine and Random Forest classifiers on the gold standard datasets auROC and
auPR curve

We have compared our method with other state of the art classifiers mentioned
in recent literatures such as SVM, AdaBoost and random forest. FRnet-2 shows
superior performance in both metric on all the datasets except for NR which
holds only 1048 instances and is the smallest dataset among the others.
Comparison with other classification models are shown in Table 4. Results for
the other methods were taken from the experiments reported in the literature
[39, 33]. Note that, for each of the datasets except the nuclear receptor (NR)
dataset, performance of FRnet-2 is superior to the other methods both in terms
of auPR and auROC. For the NR dataset, the performance of FRnet-2 is almost
similar to the best performing boosting classifier. The auPR value is second
best and probably because of the fact that this dataset is highly clustered
and clustered sampling techniques for balancing used in [39] makes it perform
better in this particular case.

Dataset | Enzyme | GPCR | ion channels | nuclear receptor
---|---|---|---|---
[51] | 0.904 | 0.8510 | 0.8990 | 0.8430
[52] | 0.8920 | 0.8120 | 0.8270 | 0.8350
[10] | 0.8075 | 0.8029 | 0.8022 | 0.7578
[18] | 0.8320 | 0.7990 | 0.8570 | 0.8240
[7] | 0.8251 | .8034 | 0.8235 | 0.8394
[47] | 0.8860 | 0.8930 | 0.8730 | 0.8240
[35] | 0.9480 | 0.8990 | 0.8720 | 0.8690
[39] | 0.9689 | 0.9369 | 0.9222 | 0.9285
Our Method | 0.9754 | 0.9478 | 0.9512 | 0.9241

Table 5: Performance of FRnet-2 on the four benchmark gold datasets in terms
of auROC with comparison to other state-of-the-art methods.

We have also compared our results against methods which used unsupervised and
semi-supervised methods reported in the literature [10, 18, 7, 51, 52, 47].
Table 5 shows a comparisons of auROC scores of other methods including
supervised methods [33, 39]. Note that, our proposed method achieves
significantly higher auROC for three datasets among four and for the NR
dataset, the performance is only second best and very close to the best
performing one.

Predictor | enzymes | GPCRs | ion channels | nuclear receptors
---|---|---|---|---
[33] | 0.54 | 0.39 | 0.28 | 0.41
[39] | 0.68 | 0.48 | 0.48 | 0.79
FRnet-2 | 0.70 | 0.69 | 0.49 | 0.73

Table 6: Comparison of the performance of FRnet-2 on the four benchmark gold
datasets from [39] in terms of auPR with other the state-of-the-art methods.

In the literature of imbalanced classification problems, it has been often
argued that between area under Precision Recall curve (auPR) and area under
Receiver Operating Curve (auROC), auPR should be considered more significant.
However, only [33] and [39] reported auPR scores in their paper. A comparison
in terms of auPR score are given in Table 6. Here too, its interesting to note
the superior performance of our proposed model on all the datasets.

We have also tested our method with input shape $[X,7,211,1]$ instead of
$[X,211,7,1]$ and found similar results which concludes that changing the
input shape has little to no effect on the performance on the models. Results
using input shape $[X,7,211,1]$ is provided in table 7.

Dataset | Classifier | auPR | auROC
---|---|---|---
enzymes | Decision Tree | 0.27 | 0.9299
| SVM | 0.54 | 0.9035
| FRnet-2 | 0.70 | 0.9713
GPCR | Decision Tree | 0.32 | 0.9038
| SVM | 0.48 | 0.8859
| FRnet-2 | 0.70 | 0.9255
Ion Channel | Decision Tree | 0.60 | 0.9235
| SVM | 0.52 | 0.8894
| FRnet-2 | 0.50 | 0.9507
NR | Decision Tree | 0.43 | 0.8207
| SVM | 0.42 | 0.7588
| FRnet-2 | 0.62 | 0.9134

Table 7: Performance comparison of FRnet-2 and other classifiers on the gold
standard datasets in terms of auROC and auPR using 4096 features generated by
FRnet-1 with input shape [X, 7, 211, 1].

Since the prediction scores with high confidence are interesting in practical
applications, A list of top five the false positive interactions based on
FRnet-2’s prediction score is given in S2 of the supplementary materials.
These are the interactions that are known as not interacting pair but the
model highly suggests other wise.

##  4 Conclusion

In This paper, we propose two novel deep neural net architectures, FRnet-1 and
FRnet-2 where FRnet-1 aims to extract convolutional features and FRnet-2 tries
to identify drug target interaction using the extracted features. From [39],
we exploit our algorithm with datasets consisting with both structural and
evolutionary features and with the help of FRnet-1 we try to generate 4096
informative features. These datasets are regarded as gold standard datasets
and are exhaustively used by researchers. We have conducted extensive
experiments and produced the results in term of auROC and auPR scores. In many
previous literatures like [33, 39], it was argued that in case of drug target
interaction, it is more appropriate to use auPR metric over auROC as the gold
standard datasets are highly imbalanced with very few interaction samples. For
this reason, FRnet-2 was focused on getting a superior auPR score even by
sacrificing auROC score. We also proposed 5 new possible interaction pair for
each of the 4 datasets based on prediction score. Up to this moment, our
proposed method outperforms other state of that art methods in 3 of the 4
benchmark datasets in auPR and auROC metric. We believe the excellent
performance our method will motivate other practitioners and researchers to
exploit both methods for not only drug target interaction but also in other
domains.

## Author Contributions

FR and SS initiated the project with the idea of using structural features. FR
proposed and implemented the convolutional architecture under supervision of
SS. All the methods, algorithms and results have been analyzed and verified by
MSR and the other 2 authors. MSR and SS provided significant biological
insights. FR prepared the manuscript with help from SS where the other authors
contributed in the process and approved the final version.

## Competing Interest

The authors declare that they have no competing interests.

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