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Deep Learning Applications for Predicting Pharmacological
Properties of Drugs and Drug Repurposing Using Transcriptomic
Data
Alexander Aliper,*
,†
Sergey Plis,
‡,§
Artem Artemov,
†
Alvaro Ulloa,
§
Polina Mamoshina,
†
and Alex Zhavoronkov*
,†,∥
†
Insilico Medicine, ETC, B301, Johns Hopkins University, Baltimore, Maryland 21218, United States
‡
Datalytic Solutions, 1101 Yale Boulevard NE, Albuquerque, New Mexico 87106, United States
§
The Mind Research Network, Albuquerque, New Mexico 87106, United States
∥
The Biogerontology Research Foundation, Oxford, U.K.
*
SSupporting Information
ABSTRACT: Deep learning is rapidly advancing many areas
of science and technology with multiple success stories in
image, text, voice and video recognition, robotics, and
autonomous driving. In this paper we demonstrate how deep
neural networks (DNN) trained on large transcriptional
response data sets can classify various drugs to therapeutic
categories solely based on their transcriptional profiles. We
used the perturbation samples of 678 drugs across A549,
MCF-7, and PC-3 cell lines from the LINCS Project and
linked those to 12 therapeutic use categories derived from
MeSH. To train the DNN, we utilized both gene level transcriptomic data and transcriptomic data processed using a pathway
activation scoring algorithm, for a pooled data set of samples perturbed with different concentrations of the drug for 6 and 24
hours. In both pathway and gene level classification, DNN achieved high classification accuracy and convincingly outperformed
the support vector machine (SVM) model on every multiclass classification problem, however, models based on pathway level
data performed significantly better. For the first time we demonstrate a deep learning neural net trained on transcriptomic data to
recognize pharmacological properties of multiple drugs across different biological systems and conditions. We also propose using
deep neural net confusion matrices for drug repositioning. This work is a proof of principle for applying deep learning to drug
discovery and development.
KEYWORDS: deep learning, DNN, predictor, drug repurposing, drug discovery, confusion matrix, deep neural networks
■INTRODUCTION
Drug discovery and development is a complicated and time and
resource consuming process, and various computational
approaches are regularly being developed to improve it. In
silico drug discovery
1,2
has evolved over the past decade and
offers a targeted, efficient approach compared to those of the
past, which often relied on either identifying active ingredients
in traditional remedies or, in many cases, serendipitous
discovery. Modern methods include data mining, structure
modeling (homology modeling), traditional machine learning
3
(ML), and its biologically inspired branch technique, deep
learning (DL).
4
DL
4
methods model high-level representations of data using
deep neural networks (DNNs). DNNs are flexible multilayer
systems of connected and interacting artificial neurons that
perform various data transformations. They have several hidden
layers of neurons, which number variation allows adjusting the
level of data abstraction. DL now plays a dominant role in the
areas of physics
5
and speech, signal, image, video, and text
mining and recognition,
6
improving state of the art perform-
ances by more than 30%, where the prior decade struggled to
obtain 1−2% improvements. Traditional machine learning
approaches have achieved significant levels of classification
accuracy, but at the price of manually selected and tuned
features. Arguably, feature engineering is the dominating
research component in practical applications of ML. In
contrast, the power of NNs is in automatic feature learning
from massive data sets. Not only does it simplify manual and
laborious feature engineering but also it allows learning task-
optimal features.
Modern biology has entered the era of Big Data, wherein
data sets are too large, high-dimensional, and complex for
classical computational biology methods. The ability to learn at
Received: March 18, 2016
Revised: May 13, 2016
Accepted: May 20, 2016
Article
pubs.acs.org/molecularpharmaceutics
© XXXX American Chemical Society ADOI: 10.1021/acs.molpharmaceut.6b00248
Mol. Pharmaceutics XXXX, XXX, XXX−XXX
This is an open access article published under an ACS AuthorChoice License, which permits
copying and redistribution of the article or any adaptations for non-commercial purposes.
the higher levels of abstraction made DL a promising and
effective tool for working with biological and chemical data.
7
Methods using DL architecture are capable of dealing with
sparse and complex information, which is especially demanded
in the analysis of high-dimensional gene expression data. “Curse
of dimensionality”is one of the major problems of gene
expression data that can be solved by feature selection
implementing standard data projection methods as PCA or
more biologically relevant as pathway analysis.
8
DNNs
demonstrate the state-of-the-art performance extracting features
from sparse transcriptomics data (both mRNA and miRNA
data),
9
in classifying cancer using gene expression data
10
and
predicting splicing code patterns.
11
DL has been effectively
applied in biomodeling and structural genomics to predict
protein 3-D structure using protein sequence (ordered or
disordered protein (with lack of fixed 3-D structure))
12,13
and
may become an essential tool for development of new drugs.
14
DL approaches were successfully implemented to predict
drug−target interactions,
15
model reaction properties of
molecules,
16
and calculate toxicity of drugs.
17
As deep networks
incorporate more features from biology,
18
application breadth
and accuracy will likely increase.
Drug repurposing or target extension allows prediction of
new potential applications of medications or even new
therapeutic classes of drugs using gene expression data before
and after treatment (e.g., before and after incubation of a cell
line with multiple drugs). There are multiple in silico
approaches to drug discovery and classification,
19−21
and
many attempts were made to predict transcriptional response
with functional properties of drugs.
22−24
In this study we
addressed this problem by classifying various drugs to
therapeutic categories with DNN solely based on their
transcriptional profiles. We used the perturbation samples of
X drugs across A549, MCF-7, and PC-3 cell lines from the
LINCS Project and linked those to 12 therapeutic use
categories derived from MeSH therapeutic use section (Figure
1). After that we independently used both gene expression level
data for “landmark genes”and pathway activation scores to
train DNN classifier.
■RESULTS
The main aim of this study was to apply and estimate the
accuracy of DL methods to classify various drugs to therapeutic
categories solely based on their transcriptional profiles. In total,
we analyzed 26,420 drug perturbation samples for three cell
lines from the Broad LINCS database. All samples were
assigned to 12 specific therapeutic use categories according to
MeSH classification of the particular drug (Supplementary
Table 1). Since a number of drugs were present in multiple
categories, we considered only those drugs that belong only to
one category. To increase the number of samples in each of the
categories and to make the classification more robust for each
given drug, we aggregated all samples corresponding to all
possible perturbation time, perturbation concentration, and cell
line parameters (Supplementary Table 2).
When dealing with transcriptional data at the gene level, a
common problem is the so-called “curse of dimensionality”.
Indeed, when we applied DNN on gene level data for whole
data set of 12,797 genes, it did not perform very well, achieving
only 0.24 mean F1 score on 12 classes. So our first step was
proper feature selection. Here we investigated two approaches:
pathway activation scoring and using “landmark genes”as new
features.
Pathway Level. For pathway level analysis we used a
previously established pathway analysis method called Onco-
Finder.
25−30
It preserves biological function and allows for
dimensionality reduction at the same time. In contrast to other
pathway analysis tools, which mostly implement pathway
enrichment analysis, OncoFinder performs quantitative estima-
tion of signaling pathway activation strength, and the sign of the
resulting value indicates how significantly the pathway is up- or
downregulated. All perturbation samples were analyzed with
this tool and for each sample we calculated pathway activation
profiles for 271 signaling pathways. Samples with zero pathway
activation score for all of the pathways were considered as
insignificantly perturbed and were excluded from further
analysis. That resulted in a final data set containing 308, 454,
and 433 drugs for A549, MCF7, and PC3 cell lines,
respectively, and totalling 9352 samples (Supplementary
Table 2).
Using this data set we built a deep learning classifier based
only on pathway activation scores for drug perturbation profiles
of 3 cell lines: A549, MCF-7, and PC-3. Making a classifier
based on a pooled data set with different cell lines, drug
concentration, and perturbation time, we are able to estimate
the classification performance in recognizing complex drug
Figure 1. Study design. Gene expression data from LINCS Project was linked to 12 MeSH therapeutic use categories. DNN was trained separately
on gene expression level data for “landmark genes”and pathway activation scores for significantly perturbed samples, forming input layers of 977 and
271 neural nodes, respectively.
Molecular Pharmaceutics Article
DOI: 10.1021/acs.molpharmaceut.6b00248
Mol. Pharmaceutics XXXX, XXX, XXX−XXX
B
action patterns across different biological conditions. For the 3-
class classification problem we chose the most abundant
categories: antineoplastic, cardiovascular, and central nervous
system agents. DNN achieved 10-fold cross-validation mean F1
score of 0.701. We compared the results of DNN to another
popular classification algorithm called support vector machine
(SVM) trained via nested 3-fold cross validation for several
hyperparameters (see Materials and Methods). On 3-class
classification problem SVM performed with mean F1 score of
0.530.
Addition of gastrointestinal and anti-infective classes
decreased the mean F1 score of DNN to 0.596. Mean F1
score for SVM dropped as well, down to 0.417.
When all 12 classes were considered, the classification neural
performance decreased in a minor way, with a cross-validation
mean F1 score of 0.546. SVM performed with cross-validation
mean F1 score of 0.366 on the same 12-class classification
problem. The performance comparison of DNN and SVM on
investigated classification problems is depicted in Figure 2a−c.
These results indicate that our model performance far exceeds
random chance,
31
and we can conclude that DNN out-
performed SVM on every multiclass classification problem.
Landmark Gene Level. In our second feature selection
approach we used a data set containing normalized gene
expression data for 977 “landmark genes”. According to the
authors of LINCS Project they can capture approximately 80%
of the information and possess great inferential value. For fair
comparison we trained DNN exactly the same way we did on
the pathway level. We used the same data set of 9352
significantly perturbed samples and tested the performance of
DNN on the same classification problems. DNN trained on
“landmark gene”data performed with 10-fold cross-validation
mean F1 scores of 0.397, 0.285, and 0.234 for 3-, 5-, and 12-
class classification tasks, respectively. The SVM model showed
mean F1 scores of 0.372, 0.238, and 0.202 for respective tasks
(Figure 2d−f).
DNN as Drug Repurposing Tool. Here we tried to dig a
bit deeper into classification results on pathway level, since the
DNN model worked best with pathways as features. To
determine which of the 12 therapeutic use categories are the
most detectable by DNN, we calculated 10-fold cross-validation
classification accuracy of each category. Antineoplastic agents
turned out to be the most “recognizable”category by a large
margin, with 0.686 accuracy on 12 classes. This was followed by
anti-infective, central nervous system, and dermatologic
categories, with 0.513, 0.506, and 0.505 accuracy, respectively.
The least “recognizable”on the same number of classes was
hematologic agents, with accuracy of 0.23. On 3- and 5-class
classification problems, the category antineoplastic drugs was
on top as well, with accuracy of 0.82 and 0.742. Separability of
therapeutic categories by DNN can be illustrated with
confusion matrices (Figure 3). Here we observed that the
cardiovascular category drugs was relatively often misclassified
as central nervous system and antineoplastic agents. In contrast,
the level of false positives for the antineoplastic category was
relatively small. If we look even closer into the results,
sometimes these misclassified false positive drugs may in fact
represent a possibility for drug repurposing. For instance, well-
known muscarinic receptor antagonist otenzepad was mis-
classified as central nervous system agent, but despite its
obvious role in brain function,
32,33
according to the MeSH
therapeutic use section, it is only used against cardiac
arrhythmia. Another example includes vasodilator pinacidil, a
cyanoguanidine drug that opens ATP-sensitive potassium
channels, which was misclassified as central nervous system
agent in several cross-validation iterations, although it is used
only in cardiovascular conditions. It is known that potassium
channels play important roles in different brain regions,
34
and
Figure 2. Classification results. Classification performance of DNN and SVM trained on signaling pathways (a, b, c) and landmark genes (d, e, f) for
3, 5, and 12 drug classes, respectively, after 10-fold cross validation. Training and validation set results are shown in gray and green colors,
respectively.
Molecular Pharmaceutics Article
DOI: 10.1021/acs.molpharmaceut.6b00248
Mol. Pharmaceutics XXXX, XXX, XXX−XXX
C
pinacidil might influence some of them. Aforementioned cases
hint to the fact that imperfect accuracy here might not be a bad
thing and the DNN model could serve as powerful drug
repositioning tool.
■DISCUSSION
With increasing availability of big data and GPU computing, the
entire field of deep learning is experiencing very rapid
development, and the breadth of DNN applications goes far
beyond text, voice, and image recognition problems. In this
paper we explored the possibility of using DL to classify various
drugs into therapeutic categories solely based on their
transcriptomic data. To our knowledge, this is the first DL
model to map transcriptomic data onto therapeutical category.
DNN trained on gene level data did not perform very well,
achieving only 0.24 F1 score on 12 classes. Thus, as a way to
reduce dimensionality and keep biological relevance, we
decided to apply pathway activation scoring.
27
Translation of
perturbation profiles onto the pathway level turned out to be
very beneficial. Pathways served as excellent features, and we
were able to exclude insignificantly perturbed samples and
demonstrate the ability of deep neural network to recognize
Figure 3. Validation confusion matrix representing deep neural network classification performance over a set of drugs profiled for A549, MCF7, and
PC3 cell lines, belonging to 3 (a), 5 (b), and 12 (c) therapeutic classes. C(i,j) element is a sample count of how many times i was the truth and j was
predicted.
Molecular Pharmaceutics Article
DOI: 10.1021/acs.molpharmaceut.6b00248
Mol. Pharmaceutics XXXX, XXX, XXX−XXX
D
sophisticated drug action mechanisms on the pathway level.
The power of this approach is further highlighted by the fact
that it performs with high accuracy on a pooled data set with
samples from cells treated by different drug concentrations and
perturbation times. Furthermore, the DNN achieves significant
classification accuracy even across different cell lines. Its
performance turned out to be better than SVM on every
multiclass classification problem. When we used the same set of
significantly perturbed samples that we selected with the
pathway activation approach, and trained DNN on a data set
with gene expression data for “landmark genes”, the results
turned out to be significantly worse. Hence, we can conclude
that pathway level data is more complementary for DNN and
more suitable for classifying drugs into therapeutic use
categories. Proper comparison to reference group of samples
plays an essential role, and merely normalized gene expression
data from perturbed samples is not sufficient for complex
classification tasks.
It is possible to interpret the classification results from
different angles. For instance, as it was shown, in confusion
matrices (Figure 3), that the “misclassified”samples for a
certain drug might in fact be an indication of its potential for
novel use, or repurposing, in these exact “incorrectly”assigned
conditions. Misclassification, therefore, may lead to unexpected
new discoveries. This approach opens a great avenue for
application of DL in the drug repurposing field.
■MATERIALS AND METHODS
Data Collection. In this study, we performed the analysis of
gene expression data produced by the LINCS Project
participants (http://www.lincsproject.org/). We utilized the
level 3 (Q2NORM) gene expression data for three cell lines:
MCF7, A549, and PC3. Q2NORM data contains the
expression levels of both directly measured landmark tran-
scripts plus inferred genes, which were normalized using
invariant set scaling followed by quantile normalization.
Mapping probes onto official HGNC
35
gene symbols resulted
in a gene expression data set comprising 12,797 genes total.
Drug Selection. To link the drugs profiled by LINCS
Project to medical conditions, we utilized MeSH (medical
subject headings) classification (https://www.nlm.nih.gov/
mesh/). We picked only those drugs that had a link to a
disease in the “therapeutic use”section of the classification tree.
Drugs belonging to several categories simultaneously were
excluded from the analysis.
Gene Expression Analysis. All of the selected drugs’
samples collected for three cell lines were grouped by the
combination of the following parameters: cell line, drug,
perturbation concentration, and perturbation time. This
resulted in a total number of 26,420 samples. For gene level
analysis we trained our models on both whole data set of
12,797 genes and a subset of 977 so-called “landmark genes”
defined in the LINCS Project.
In pathway level analysis, for each given case sample group
we generated a reference group consisting of samples perturbed
with DMSO, that came from the same RNA plate as samples
from the case group. After that, each case sample group was
independently analyzed using an algorithm called Onco-
Finder.
27
Taking the preprocessed gene expression data as an
input, it allows for cross-platform data set comparison with low
error rate and has the ability to obtain functional features of
intracellular regulation using mathematical estimations. For
each investigated sample group it performs a case-reference
comparison using Student’sttest, generates the list of
significantly differentially expressed genes, and calculates the
pathway activation strength (PAS), a value which serves as a
qualitative measure of pathway activation. Positive and negative
PAS values indicate pathway up- and downregulation,
respectively. In this study the genes with FDR-adjusted p-
value <0.05 were considered significantly differentially ex-
pressed. Samples with zero pathway activation score for all of
the pathways were considered as insignificantly perturbed and
were excluded from further analysis. The filtered data set
contained 308, 454, and 433 drugs for A549, MCF7, and PC3
cell lines, respectively, and comprised 9352 samples total
(Supplementary Tables 1 and 2).
Classification Methods. Among the multitude of available
classification methods we have employed two that are highly
robust and widely successful in fields outside drug prediction:
SVM
36,37
and deep neural network.
38
SVMs are a celebrated
classification method for their flexibility and ease of use, while
deep learning approaches are continuing to break records in
many pattern recognition tasks.
Flexibility of SVM, as a maximum margin classifier, is in part
reflected in provided ability to select a kernel that fits the data
best and choose a soft-margin parameter that allows for best
generalization. However, these parameters are not evident for
any given data set. It has been previously shown that radial basis
function (RBF) kernel SVMs perform well on largely different
data. In order to be more objective in selection of kernel we
have allowed our nested cross validation choice of three. We
used grid search for hyperparameter optimization. We have
trained the SVM via nested 3-fold cross validation for
hyperparameters that include kernel type (linear, RBF, or
polynomial) and soft-margin parameter Cembedded in the
outer 10-fold cross-validation loop. For each fold, the algorithm
could have selected different kernels and different soft-margin
parameters. Nevertheless, RBF was indeed the most preferred.
The deep learning method used in our work was the standard
fully connected multilayer perceptron with 977 input nodes for
gene expression level data and 271 for pathway activation
scores. Similarly to SVM, we used grid search for hyper-
parameter optimization. We used cross entropy as cost function
and AdaDelta
39
as cost function optimizer. We searched for the
optimal number of layers, number of hidden units, and dropout
rejection rate in a nested cross-validation framework. For the
hyperparameter search, the number of layers varied from 3 to 8,
where the number of hidden units per layer was reduced with
the depth of the network to half the previous layer. We set the
search for the starting hidden layer from 300 to 900 in steps of
150. Each layer was initialized with the Glorot uniform
approach.
40
The experiment shows that the best combination
of parameters was 3 hidden layers with 200 in each with
rectified linear activation function. The dropout rejection ratio
was tested for 20% and 80% at each layer. We chose an
antisymmetric activation function, hyperbolic tangent, as the
nonlinear function of hidden neurons because the data is
normalized to zero mean and unit variance, thus we rely on
deviations from the mean to train the network. Also, it has been
reported that the hyperbolic tangent speeds up convergence
compared to sigmoid functions.
39
The number of output nodes
was equal to the number of classes to explore in each particular
experiment with a softmax activation function. The code that
implements the feed-forward neural network used in our
experiments is publicly available at https://github.com/
Molecular Pharmaceutics Article
DOI: 10.1021/acs.molpharmaceut.6b00248
Mol. Pharmaceutics XXXX, XXX, XXX−XXX
E
alvarouc/mlp (commit: 8b07a1a18b17ca530fdcb482fce-
c24e26e36b27a).
■ASSOCIATED CONTENT
*
SSupporting Information
The Supporting Information is available free of charge on the
ACS Publications website at DOI: 10.1021/acs.molpharma-
ceut.6b00248.
MeSH category stratification binary matrix (XLSX)
Number of drugs selected for A549, MCF7, and PC3 cell
lines (XLSX)
■AUTHOR INFORMATION
Corresponding Author
*E-mail: aliper@insilicomedicine.com.
Notes
The authors declare no competing financial interest.
■ACKNOWLEDGMENTS
We would like to thank Dr. Leslie C. Jellen of Insilico Medicine
for editing this manuscript for language and style. Authors
thank Mark Berger and NVIDIA Corporation for providing
valuable advice and high performance GPU equipment for deep
learning applications.
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