Briefings in Bioinformatics, 22(1), 2021, 164–177
doi: 10.1093/bib/bbz140
Advance Access Publication Date: 15 December 2019
Review Article

A survey on adverse drug reaction studies: data, tasks
Duc Anh Nguyen, Canh Hao Nguyen and Hiroshi Mamitsuka
Corresponding author: Tel.: +81-774-38-3023 Fax: +81-774-38-3037; E-mail: ducanh@kuicr.kyoto-u.ac.jp

Abstract
Motivation: Adverse drug reaction (ADR) or drug side effect studies play a crucial role in drug discovery. Recently, with therapid increase of both clinical and non-clinical data, machine learning methods have emerged as prominent tools to
support analyzing and predicting ADRs. Nonetheless, there are still remaining challenges in ADR studies. Results:In thispaper, we summarized ADR data sources and review ADR studies in three tasks: drug-ADR benchmark data creation,
drug–ADR prediction and ADR mechanism analysis. We focused on machine learning methods used in each task and thencompare performances of the methods on the drug–ADR prediction task. Finally, we discussed open problems for further
ADR studies. Availability: Data and code are available at https://github.com/anhnda/ADRPModels. Supplementary:
Supplementary materials are available at Briefings in Bioinformatics online.
Key words: adverse drug reaction; ADR prediction; ADR mechanism; machine learning methods

Introduction
According to WHO, an adverse drug reaction (ADR) or drug side
effect is a response to a medicine that is noxious and unintended, and which occurs at doses normally used in human[1]. In reports of 2011, ADRs accounted for nearly 6% of total
hospitalizations in the USA, which cost billions of dollars andwere responsible for significant patient morbidity and mortality[2, 3]. Therefore, the studies of ADRs are important in drug
discovery.
The traditional methods for obtaining ADRs of drugs
often use clinical trials or post-marketing surveillance reports[4]. However, these methods are costly and time-consuming.
To deal with these disadvantages, machine learning methods

integrating various kinds of ADR data sources are used tomake inexpensive and fast predictions. These results provide
potential ADRs and their mechanism analysis for further clinicalverification to enhance ADR studies.
Data sources used in ADR studies consist of clinical and nonclinical data. The clinical data contains observations of ADRs
from clinical treatments of patients. These observations have
not only ADRs but also personal contexts, such as dosages oftreatments, ages, genders and diseases of patients. Since different patients can have different ADRs, such personal contexts can
support to build personalized ADR prediction models.
The non-clinical data contain information of biological systems such as drug–protein interactions and biological processes.
In fact, there are various possible mechanisms in ADRs, for

Duc Anh Nguyen is currently a PhD student at the Bioinformatics Center in Kyoto University. His current research interests focus on machine learningand bioinformatics.
Canh Hao Nguyen is an assistant professor at the Bioinformatics Center, Institute for Chemical Research, Kyoto University, working on machine learning
for graph data, with applications to biological networks.
Hiroshi Mamitsuka is a professor at the Bioinformatics Center, Institute for Chemical Research, Kyoto University, and a FiDiPro professor at the Departmentof Computer Science, Aalto University, working on machine learning, data mining and application to biology and chemistry.
Submitted: 07 Month 2019; Received (in revised form): 09 Month 2019
© The Author(s) 2019. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com

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Table 1. Recent surveys on ADR studies
Task
Paper

Clinical data extraction

Poloju andMuniganti, 2018 [7]Chen et al., 2016 [8]Ho et al., 2016 [9]

X

X

Data
Drug-ADR prediction

Clinical data

MethodanalysisDrug-ADR andnon-clinical data

X
X
X

Data sources in ADR studies
In this section, we summarize commonly used data sources in
ADR studies. Figure 1 illustrates a hierarchical classification ofdata sources in ADR studies containing two groups: clinical andnon-clinical data.

Clinical data
Clinical data contain observations of ADRs in clinical treatments, which are often electronic health records or records

X

Table 2. Commonly used clinical data sources
Data sources

Personelcontext

FAERS [13]OMOP-CDM [15]SIDER [16]Liu’ dataset [17]AEOLUS [18]OFFSIDES [19]TWOSIDES [19]X
X

Drug-ADR benchmark
Monopharmacy Polypharmacy

X
X
X
X
X

from adverse report systems. Each record contains drugs andobserved ADRs. In addition, personal contexts such as demographic and dosage information are also stored. There is evidence that ADRs are different from different patients [11]; therefore, these personal contexts are important to build personalized
ADR prediction models [12].
Table 2 provides the commonly used clinical data sources. For
personal contexts, it has FDA Adverse Event Reporting System(FAERS) [13] and Medical Outcomes Partnership Common Data
Model (OMOP CDM) [14]. There are four main tables in FAERS:
demographics, drug, therapy and reaction. The demographics
table describes patient information containing patient identification, age, gender, weight, location and other related information. The amount and routes of drug administration with patient
identifications come from the drug table, and the time of drug
treatments is from the drug therapy table. The reaction tablecontains the drug adverse reactions with patient identifications.
OMOP CDM is a data model provided by Observational Health
Data Sciences and Informatics [15], which is an international
collaboration with the aim to create and apply data analytic
solutions to a large number of observational health databases.
There are four domains of OMOP CDM v5.0: standardized clinicaldata, standardized health system data, standardized health economics data and standardized derived elements. Standardized
clinical data contain the core information with clinical eventsand demographic information of patients. With OMOP CDM, millions of health records from different resources are transformed
into pre-defined tables of the four domains, supporting further
analysis [32].
FAERS was used to extract drug-ADR benchmark datasets,
which contain reliable drug–ADR associations [16, 19]. SIDER,
a common ADR benchmark dataset for many ADR studies,
was extracted from FAERS for ADRs caused by single drugs(monopharmacy) [16]. Liu’ dataset [17] is a benchmark dataset
extracted from SIDER into the binary format with additional drug
information. AEOLUS is also a monopharmacy dataset extracted
from FAERS, and has more drug–ADR associations than SIDER.
Extracting from FAERS with a criteria of removing bias data,

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example, by interactions of drugs with proteins, but the details ofthese mechanisms are still unknown [5, 6]. By integrating clinicaldata with non-clinical data, it is expected that the quality of ADR
studies will be improved, and ADR mechanisms can be revealed.
Since there are different machine learning methods using
various kinds of ADR data sources, an overview of current methods in ADRs is necessary. Table 1 summarizes the most recent
survey papers related to ADR studies. These studies often use
either clinical data [7] or non-clinical data [8]. There is only
one survey that uses both kinds of data [9], but there is no
detailed analysis on methods, such as providing a taxonomy orconducting experiments to compare performances of methods.
Recently, there are new studies in ADRs with the emerging
of using machine learning methods, leading to a need for amore detailed classification for these methods. Moreover, ADR
studies are not only drug-ADR prediction [8] but also analyzing
ADR mechanisms by revealing biological components associatedwith ADRs [10]. Motivated by this, we give a broader view of ADR
studies containing ADR data sources and how computationaltasks of ADRs use these kinds of data.
The contributions of our paper can be summarized as follows.
(i) We summarize the ADR data sources containing both clinicaland non-clinical data. (ii) We summarize a wide range of drug
descriptors used in ADR studies. (iii) We analyze methods usedin ADR studies in three main tasks: (a) drug-ADR benchmark
data creation, (b) drug-ADR prediction and (c) ADR mechanism
analysis (We focus on papers on the main journals with themost numbers of papers on this topic such as Bioinformatics,
BMC Informatics, Briefing in Bioinformatics and Nucleic Acid
Research, then we follow cited papers. Papers are collected up
to February 2019.). In each task, we analyze data and commonlyused machine learning methods. (iv) We conduct an experiment
to compare the drug-ADR prediction performances of eight commonly used methods.
The organization of the paper is as follows: Section 2 presents
the data sources used in ADR studies. Section 3 details different
kinds of drug descriptors that encode drug information. ADR
studies with tasks and methods are detailed in Section 4. Finally,
discussions on current ADR studies and open problems arepresented in Section 5.

X
X

X

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Table 3. Commonly used non-clinical databases
Database

Having Interactions

Chemical / drug

Protein / gene

X
X

X

X

X
X
X
X
X
X

X
X
X
X
X

X
X
X
X

Pathway

ADR term

Disease

X
X

X
X

X
X

X
X

X
X

X

X
X
X

OFFSIDES for ADRs caused by single drugs and TWOSIDES for
ADRs caused by combinations of two drugs (polypharmacy) werecreated [19]. However, SIDER, AEOLUS, OFFSIDES and TWOSIDES
only contain two kinds of information: drugs and ADRs. As far as
we know, there is no benchmark ADR data for academic researchthat contains personal contexts such as diseases and durationof drug treatments.
In recent years, data from social media such as Facebook
and Twitter are another kind of data to analyze ADR. This social
media data contain comments of patients during drug treatments. However, the tasks on this kind of data are mainly ADR
identification using techniques of natural language processing [33–35], which are considered as data pre-processing steps,
therefore, we do not cover them in this survey.

Non-clinical data
The non-clinical data contain information of chemical, physicaland biological properties of drugs and biological systems,
which can help reveal mechanisms of drugs and ADRs. In
fact, ADRs are results of complex reactions of drugs withbiological components. Some studies have shown that drug side
effects can be the results of reactions of drug chemicals withproteins [5, 6, 36], which interrupts normal biological processes
leading to abnormal reactions of human bodies. By using thiskind of data, we can improve the performance of models
and extract possibly associated biological components with
ADRs.
Table 3 summarizes the commonly used non-clinicaldatabases in ADR studies in two aspects: elements in eachdatabase and interactions among elements existed or not.
For example, ADReCS [28] is a database for only ADR term
definitions, and KEGG [26] contains information of proteins,
drugs, biological pathways, diseases and interactions among
them such as drugs with proteins targets. To link these
databases, Bio2RDF [31] provides interconnections among
elements of different databases.
Finally, the connection between clinical and non-clinical data
can be illustrated by a network in Figure 2. The clinical data
provide information of drug-ADR connections with personal
contexts. The non-clinical data contain connections of drug–
drug, drug–protein, protein–protein and protein–biological pathway. This network is used to support some computational tasks
represented in Section 4.

Fig. 1. Data source hierarchy in ADR studies.

Fig. 2. A network for clinical and non-clinical data.

X
X
X

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DrugBank [20]PubChem [21]
PDB [22]
BindingDB [23]HPRD [24]CTD [25]
KEGG [26]SuperTarget [27]
ADReCS [28]DART [29]
TTD [30]
Bio2RDF [31]Elements

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Table 4. Two groups of structural descriptors implemented in CDK
[40]
Group

Name

Number ofdescriptors

Variable-size

Daylight family [41]E-State fragments [42]Klekota-Roth [43]
MACCS keys [44]
PubChem descriptors [21]CDK substructures [40]

79
4860
166
881
307

Fixed-size

One possible way of encoding drugs is to use descriptors, which
are physical, chemical and biological characteristics of a drug.
Since the quality of these descriptors impacts ADR prediction
performances, the understanding of drug descriptors is a basic
need. Figure 3 presents a classification for drug descriptors. In
general, drug descriptors can be categorized into two classes:
physical or chemical descriptors (PC-descriptors) and biologicaldescriptors (BIO-descriptors).

Fig. 3. Different kinds of drug descriptors.

PC-descriptors
The PC-descriptors describe the structure of drug molecules andtheir physical and chemical properties [37–39]. Based on theirdimensionalities and properties, this class of descriptors canbe divided into three subgroups: structural descriptors, spatialdescriptors and other miscellaneous descriptors.
The structural descriptors describe features of molecular
structures such as atom counters, atom pairs, rings and othersubstructures. Table 4 presents two groups of structural descriptors (fingerprints) implemented in Chemistry Development Kit(CDK) [40]: variable-size and fixed-size groups. The former group
generates substructures from a given set of molecules, in which
the number of substructures can be changed depending on theprovided molecule set [41]. In contrast, the latter group uses predefined substructures, for example, MACCS keys and PubChem
descriptors. An illustration of PubChem descriptors is shown in
Figure 4. The PubChem descriptors contain pre-defined 881 bits,
which are divided into seven sections with corresponding bits.
For instance, bit 308, which belongs to section 3 of simple atom
pairs, indicates the existence of O-H connection.
The spatial descriptors describe spatial properties of drug
molecules. In PubChem 3D database [45], 3D conformers descriptors of molecules are used. These descriptors are calculated
by OMEGA [46], a tool published by OpenEye. Molecular interaction fields (MIFs) are another kind of spatial descriptors for
drugs. MIFs describe spatial variation of the interaction energy
between a molecular target and a chosen probe. Probes are small
molecules representing common interactions such as hydrophobic, hydrogen bond donors and acceptors [47]. Some well-known
MIFs are GRID [48], VolSurf [49], CoMFA [50] and MetaSite [51].
Figure 5 illustrates the idea of GRID descriptors. A molecule is put
into a cube with grids. An empirical energy function will be used
to calculate the interaction field of each cell at position (x, y, z)of the cube. The energy function is defined by

Exyz =

X

Elj +

X

Eel +

X

Ehb

Fig. 4. Seven sections in PubChem descriptors.

Fig. 5. A molecule with 3D GRID.

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Drug descriptors

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Table 6. Contingency table for Fisher’s exact test

Table 5. Main notations
Notation

Description

Number of records of drugsi ∈ {1, . . . , d}j ∈ {1, . . . , s}xi ∈ Re
X = [x1 . . . xd ]T ∈ Rd×e

A drug index in a set of given d drugs
An ADR index in a set of given s ADRs
A descriptor vector of size e of drug i
A descriptor matrix of given d drugs, T
is the transpose operator.
An association score of drug i andADR j
A vector for association scores of drug
i with s ADRs
A given drug–ADR association scorematrixA vector of size m representing
associated biological components ofdrug iA given drug-biological component
matrixDrug iOther drugs

n1
n2

n3
n4

yi,j ∈ R

yi = [yi,1 . . . yi,s ]T ∈ Rs

hi ∈ R m

H = [h1 , . . . , hd ]T ∈ Rd×m

where Elj , Eel and Eeb are the Lennard–Jones function, the electronic function and the hydrogen bound function, respectively[48].
Other miscellaneous descriptors such as physicochemical
properties of drugs also affect the action of drugs. Lipophilicity[52, 53] impacts solubility, absorption, distribution, membrane
penetration and plasma protein binding of drugs. Hydrogenbond [54] is another physical property of electrostatic attraction,
which takes two out of five Lipinski’s rules [55]. Size/geometricfeatures of drugs such as molecular weight and atom counters
can also reflect drug properties.

Yes
No

Task 1: drug-ADR benchmark data creation
ADR clinical data contain millions of records with redundant
information, for example, some records contain similar information. Creating a drug-ADR benchmark dataset is a necessary task
in ADR studies. It helps other studies in evaluating performancesof new methods and comparing with existing methods.
In ADR studies, benchmark data are extracted from clinicalrecords to retrieve reliable drug–ADR associations, which arepairs of drugs with corresponding ADRs. However, drug-ADR
pairs have different levels of association significance in clinicalrecords. Some pairs of drug-ADR rarely appear in the clinicalrecords, leading to their low association significance. In addition,some records often contain a combination of more than one
drug, making the verification of drug–ADR associations difficult.
To check the significance of drug–ADR associations, association rule mining or statistical significance tests can be applied[58]. We will briefly explain a typical significance test, Fisher’s
exact test [59]. Consider drug i and ADR j in a clinical database,the association information of drug i and ADR j is stored into
a contingency table as in Table 6. In this table, n1 denotes thenumber of records containing ADR j of drug i, while n2 is that of
the other drugs. The number of records that do not contain ADR
j of drug i is n3 , and that of the other drugs is n4 . The Fisher’s
exact test evaluates the significance of the association of drug i
and ADR j by a P-value:p=(n1 + n2 )! (n3 + n4 )! (n1 + n3 )! (n2 + n4 )!
n1 ! n2 ! n3 ! n4 ! (n1 + n2 + n3 + n4 )!

BIO-descriptors
The BIO-descriptors describe biological properties of drugs,
which can be classified into two subgroups: function-based
descriptors and interaction-profile descriptors. The functionbased descriptors describe purposes of drugs in therapy. ATCcode [56], which is a classification system for drugs based ontherapeutic properties, is a typical example of function-based
descriptors.
The interaction-profile descriptors describe associated biological components of drugs containing protein targets and associated biological pathways of drugs [17, 57]. These interactionprofile descriptors are taken from the databases having druginteraction information in Table 3, such as DrugBank [20], BindingDB [23] and Bio2RDF [31].

ADR studies: tasks, data and methods
In this section, we summarize three main computational tasks
in ADR studies: (i) drug-ADR benchmark data creation, (ii) drug–
ADR prediction and (iii) ADR mechanism analysis. Figure 6 provides an overview of ADR studies of these three tasks. In eachtask, we analyze objectives, data and commonly used methods.
Main notations used in the following subsections are describedin Table 5.

This technique was used on FAERS to extract SIDER, amonopharmacy ADR benchmark dataset used in a large numberof ADR studies [16, 60]. The technique was also used to extract
OFFSIDES for monopharmacy ADR, which are ADRs of drugs that
do not appear in the drug’s package insert, and TWOSIDES for
polypharmacy ADRs of drug–drug interactions [19].

Task 2: drug–ADR prediction
Predicting ADRs of drugs, or drug–ADR association scores, isa main objective of ADR studies. Depending on the personal
context information is used or not, studies in drug–ADR prediction can be divided into two classes: personalized drug–ADR
prediction and general drug–ADR prediction. In the following
subsections, we analyze machine learning methods according toeach class.
Personalized drug–ADR prediction
The personalized drug–ADR prediction uses personal contexts
taken from clinical data with information such as dosages oftreatments, gender and age of each patient. Therefore, the prediction result will be different among patients even with thesame drugs. For this prediction, we focus on methods using

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Y = [y1 . . . yd ]T ∈ Rd×s

ADR j

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Poisson models, which are commonly used models for personalized drug–ADR prediction.
i. Poisson models
The aim of using Poisson models is to predict the probabilities of
the numbers of occurrences of ADRs during drug treatments. It
is assumed that these numbers follow Poisson distributions withexpectations depending on the taken drugs [12, 32]. For simplicity, considering a patient p in a drug treatment, the probabilities
of numbers of occurrences of s ADRs 8(ỹ|x̃) ∈ Rs are calculatedby8(ỹ|x̃) = [P(ỹ1 |φ1 (x̃)) . . . P(ỹj |φj (x̃)) . . . P(ỹs |φs (x̃))]T
where x̃ = [x̃p,1 . . . x̃p,i . . . x̃p,d ]T is a vector indicating drugs taken
by patient p during the treatment, ỹ = [ỹ1 . . . ỹj . . . ỹs ]T is a vector
denoting the numbers of occurrences of s ADRs and P(ỹj |φj (x̃))) =φj (x̃)ỹj e−φj (x̃) /ỹj ! is the Poisson distribution for the number ofoccurrences of ADR j with expectation φj (x̃). A commonly used
formulation of φj is

φj (x̃) = exp (θp,j +dXx̃p,i .wi,j )i=1

where θp,j is a parameter depending on the patient, leading todifferences in ADR occurrences of different patients, and wi,j isa parameter used as a weight for the association of drug i and
ADR j [32]. This formulation shows a multiplicative contributionof each drug to the expectation of the number of occurrences ofeach ADR.
However, the existing Poisson models have a limitation interms of integrating other information such as weights, gendersof patients and also non-clinical data.

ii. Other methods
There are other methods that were used to combine drugs withpersonal contexts into medical case vectors. A feature-basedsimilarity method was proposed to learn weights for these medical case vectors with the idea was to distinguish cases having
an ADR with cases not having the ADR [61]. These medical casevectors were also used as inputs for a classification problem [62].

General drug–ADR prediction
In contrast to personalized drug–ADR prediction, general drug–
ADR prediction predicts drug–ADR association scores without
using personal contexts. A common approach for this class is
to combine knowledge of drugs from non-clinical data to enrichdrug information and apply machine learning methods to builddrug–ADR prediction models. As presented in Section 3, drug
information is described by various types of drug descriptors.
The drug–ADR prediction models receive the drug descriptors as
the inputs and output all corresponding ADRs.
In this study, we consider general drug–ADR prediction as amulti-label classification problem such that each ADR is a label
and each drug can have many labels [64, 65]. The prediction
models calculate the association scores, which are real numbers,of each drug with all labels. The final labels of the drug areselected from these scores by a ranking method. In detail, a drug–
ADR prediction model is formulated as a function f : Re → Rs ,where e is the number of descriptors and s is the number of ADRs.
Given a drug with descriptor vector x ∈ Re , the model predicts
drug–ADR association scores with s ADRs: f(x) ∈ Rs .
We further classify the models into two classes: (i) non-latentvariable models and (ii) latent variable models. Latent variables
are ones that are not directly observed or measured, and needed
to infer from observed data. Figure 7 presents an example oflatent variables from a study on finding patterns of psychoactive
substances used in adolescents [63]. There are thirteen psychoactive substances from beer to hallucinogenics, which are

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Fig. 6. Computational tasks of ADR studies: data and commonly used methods.

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observed variables. In addition, there are some correlated pairsof substance usages, for example, cocaine and amphetamines.
The study suggested that these substances can be grouped into
three groups: alcohol, cannabis and hard drug. The patterns of
substance usage will be taken from these three groups, which
are called latent variables.
A latent variable model is a model that contains latent variables obtained from observed ones. In application to drug–ADR
prediction, latent variables of drugs can be interpreted as groupsof drug descriptors that are highly correlated with each other.
The representations of drugs in the space created by latentvariables are called latent vectors.
In the following contents, we first describe the formulation
for function f according to models of the two classes: non-latentvariable models and latent variable models, which are based on
the criteria that latent vectors of drugs are learned or not. Then
we present an experiment to compare prediction performancesof these models.
i. Non-latent variable models
In non-latent variable models, drug descriptors are used to predict drug–ADR associations without learning drug latent vectors.
We present three typical methods: (i) k nearest neighbors (k-NN),(ii) kernel methods and (iii) mining networks of drug-ADR.
(i) k-NN
The idea of using k-NN is that drugs having similar descriptor
vectors tend to have similar ADRs [17, 64, 66–68]. Suppose that
there is a similarity measure sim : Re × Re → R, for example,
cosine similarity. To predict drug–ADR association scores f(x),first the top k most similar drugs to x are identified resulting ina set of indices of the similar drugs T(x, k). Then the drug–ADR
association scores are calculated byf(x) = [f1 (x) . . . fj (x) . . . fs (x)]T ,where fj is a weighted average function:

fj (x) =Xwi (x)yi,j , j ∈ {1 . . . s},i∈T(x,k)with weights wi are obtained from drug similarities, for example:

wi (x) = P

sim(x, xi ).
sim(x, xi′ )i′ ∈T(x,k)(1)

Some extensions of KNN were also applied, for example thelinear neighborhood similarity method (LNSM) [64]. In LNSM, thesimilarity weights are calculated such that a drug descriptor vector is a linear combination of descriptor vectors of the neighbordrugs with corresponding similarity weights.
(ii) Kernel methods
The idea of using kernel methods, for example, support vector machines (SVMs), is to use classification functions calculated
from kernel functions in the form of inner products of drug
descriptor vectors [17, 57, 67, 69]. To predict drug–ADR association scores f(x) = [f1 (x) . . . fj (x) . . . fs (x)]T , the kernel methods use
the following form for fj :fj (x) = g(dXwi,j yi,j K(x, xi )), j ∈ {1 . . . s}i=1

where g is a function, for example, a sign function. K : Re ×Re → R
is a kernel function, for example, a radial basis function (rbf):)T (x−xi )) with a hyperparameter δ, and wi,j is aK(x, xi ) = exp(− (x−xi2δ
2parameter used as a weight for the association of drug i and ADR
j.
Different from k-NN, the kernel methods learn weights froma training process, which depends on both drugs and ADRs, while
weights in k-NN are calculated only from drug similarities.
c. Mining networks of drug–ADR
Consider a drug–ADR network G = (V, E), where V is aset of nodes of d drug and s ADRs: V = {v1 , . . . , vi , . . . , vd } ∪{ν1 , . . . , νj , . . . , νs }, and E is a set of edges of drug nodes–ADR nodes
for known ADRs of drugs and drug nodes–drug nodes for drug
similarities. The idea of mining this network is that if a drug andan ADR in the network are well-connected, they possibly have ahigh association score [70–72]. This approach can be formulated
in two steps:
1. Calculate partial connection scores r(vi , νj ) ∈ Rl of each pairof drug node vi and ADR node νj using l different measures
on G. A commonly used measure is Jaccard index [70, 72].
Let Ni = {v|(v, vi ) ∈ E} be a set of neighbor nodes of drug
node vi , and Nj = {v|(v, νj ) ∈ E} be that of ADR node νj , thepartial connection score calculated by Jaccard index is |Ni ∩Nj |/|Ni ∪ Nj |, where | i̇| denotes the cardinality of a set. Someother measures such as Dice index and Adamic/Adar index
were also applied [72]. Random walk [73] was also applied to
calculate r [74].
2. Calculate drug–ADR association scores f(x) of a drug withdescriptor vector x. Let v(x) be the corresponding node in G

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Fig. 7. An example of latent variables with 13 psychoactive substances [63].

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of the drug. The association scores are obtained byf(x) = [f (r(v(x), ν1 )) . . . f (r(v(x), νj )) . . . f (r(v(x), νs ))]T
where f was often a binary function [71] or a logistic regression function (LR) [70]. In addition, random forest (RF) was
also applied to f [72].

ii. Latent variable models
In latent variable models, drug–ADR association scores are calculated by using drug latent vectors learned from drug descriptors.
Figure 8 illustrates two stages of using latent models: learning
latent vectors of drugs and then using these latent vectors forprediction. It is expected that latent vectors can remove redundant information from drug descriptors, for example, unnecessary descriptors. In addition, calculating with latent vectorsof small size can reduce complexity of high-dimensional data.
In this paper, we briefly describe three commonly used latentvariable models (canonical correlation analysis (CCA), matrix
factorization (MF) and neural networks), and some other miscellaneous models.
(i) Canonical correlation analysis
The aim of using CCA is to find weight vectors a ∈ Re and b ∈Rs such that the correlation of the projections of drug descriptor
matrix X and drug–ADR association matrix Y is maximized [57]:arg max pa,b(Xa)T (Yb)p.
(Yb)T (Yb)(Xa)T (Xa)The first pair of (Xa, Yb) is called the first pair of canonicalvariables (latent variables). The remaining pairs of canonicalvariables have an additional constraint that they are uncorrelated with existing pairs of canonical variables. c pairs of weightvectors a and b form two weight matrices: A ∈ Re×c and B ∈ Rs×c ,respectively.
The latent vector of a drug with descriptor vector x is calculated by z(x) = AT x. Drug–ADR association scores f(x) are
obtained by minimizing the distance of latent vectors:
f(x) = arg min z(x) − BT y .
y∈Rs

where k. k is a norm, for example, Euclidean norm.
Sparse canonical correlation analysis (SCCA), a variant of
CCA, was also applied to predict drug–ADR association scores[67]. In SCCA, L1 regularization is applied to columns of A andB, leading to their sparsity.
(ii) Matrix factorization
The idea of using MF is illustrated in Figure 9 [75]. It is
assumed that drugs and ADRs share c unknown latent variables.
Then the drug–ADR association matrix Y is decomposed into
two matrices of latent vectors of drugs and ADRs in the spaceof latent variables: U ∈ Rd×c and V ∈ Rs×c , such that Y ≈ UVT .
Supposing there is a drug similarity matrix Sd ∈ Rd×d calculated
from drug descriptors matrix X, and an ADR similarity matrix
Ss ∈ Rs×s calculated from ADR definitions, the objective functionisarg min Y − UVT + R(U, V, Sd , Ss ),U,Vwhere the first part is the error from MF, and the second oneis the regularization for U and V given Sd and Ss , for example,
Laplacian regularization.
To calculate drug–ADR association scores f(x), first k-NN isapplied to calculate a new latent vector z(x) from the existing
drug latent vectors:z(x) =Xwi (x)uii∈T(x,k)where ui ∈ Rc is the latent vector of drug i such that uTi corresponds to the ith row of U, T(x, k) is the set of indices of the top k
most similar drugs to x, and wi (x) are similarity weights definedin Equation 1.
Then, the drug–ADR association scores are obtained byf(x) = Vz(x).
Different from CCA, MF only focuses on Y to learn latentvectors and uses X as additional information, which can beomitted from the regularization part. Meanwhile, CCA requires
both X and Y to obtain latent vectors.
(iii) Neural networks
Neural networks, which are machine learning models
featured by the ability to learn non-linear relationships, wereapplied to predict drug–ADR association [76–78]. Figure 10
illustrates this technique in detail. The basic components ofneural networks are neurons. Each neuron receives an input
vector x′ = [x′1 x′2 . . . x′n ]T and outputs a value y′ by a function:y′ = f (wT x′ + b), where b is a bias, w = [w1 w2 . . . wn ]T is aweight vector and f is an activation function, for example, asigmoid function, making non-linear combinations. A neuralnetwork module is composed of multiple layers of neurons thatthe output of each neuron of a layer is used as an input forneurons of other layers. The outputs of a neural module, forexample, named Encoder, given an input vector x is denoted by
Encoder(x).
To predict drug–ADR association scores f(x), there are two
steps to process:1. Obtain the latent vector: z(x) = Encoder(x), where Encoder is aneural module receiving drug descriptor vector x as the input
vector.
2. Predict drug–ADR association scores: f(x) = Decoder(z(x)),
where Decoder is a neural module receiving drug latent vectorz(x) as the input vector.
An advantage of using neural networks is the ability toapproximate any continuous function. If there is no hidden
layer, neural networks become logistic regression functions. The
architecture of neural networks can be more complex whenchanging connections of neurons and number of layers, forexample, a multi-layer feedforward neural network (MLN) [77], ora deep convolutional neural network (DCN) [78]. These complex
neural networks aim to approximate mapping functions from
inputs to outputs better. However, the number of parameters
in a neural network are often much larger than that ofother models. This problem leads to increasing computational complexity and the potential for overfitting of neuralnetworks.

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However, a problem with mining drug–ADR networks is sparsity that there are too few edges between drugs and ADRs, forexample, in SIDER dataset, the edge density is 0.017. This makes
the prediction less effective since there is only a small numberof ADRs predicted for each drug.

171

172

A survey on ADR

Table 7. Statistics of the used dataset.
Num drugs

1,385

2,707

Num drug-ADRs

605,121Avg.

Min

ADRs/drug
Max

445

1

2,703

(iv) Other methods
There are some miscellaneous methods to obtain latent vectors of drugs to predict drug–ADR associations, for example,
mapping drugs into an ADR space [79–81] and mapping drugs
into a metabolic reaction space [82]. In mapping drugs into an
ADR space, groups of highly correlated ADRs were extracted,
then each drug was represented by a vector over these groups. In
mapping drugs into a metabolic reaction space, flux variability
analysis was applied to represent drug–protein/gene interaction
profiles by a vector over metabolic reactions [83], then these
vectors were used to predict ADRs.
Performance comparison in general drug–ADR prediction
We conducted experiments to compare the general drug–ADR
prediction performances on monopharmacy cases of eightmachine learning models. There were four non-latent variable
models: LNSM [64, 68], SVMs [17, 69], RF [17, 72] and LR [17,70], and four latent variable models: CCA [57, 67], MF [75], MLN[77] and DCN [78] (The convolutional network proposed in [76]addressed polypharmacy ADRs, so we do not compare.).
i. Experimental setups

Fig. 9. An illustration of matrix factorization.

Fig. 10. An illustration of a neural network.

We ran experiments with AEOLUS dataset [18], a monopharmacy
dataset for drug–ADR prediction, which was also used in [65]
(AEOLUS is the largest one among AEOLUS, SIDER and Liu’s
datasets.). We only selected drugs appearing in DrugBank and
ADRs occurring in more than 50 drugs. The final statisticalinformation of the dataset is provided in Table 7, containing thenumber of drugs, the numbers of ADRs, the numbers of drug–
ADR associations and the average, minimum and maximum
numbers of ADRs per each drug.
In the experiments, we used PCBio and Chem2D as two kindsof drug descriptors with information presented in Table 8. PCBio
descriptors are the combinations of PubChem descriptors takenfrom PubChem and chemical, physical and biological descriptors
taken from Bio2RDF. We extracted descriptors with informationfrom DrugBank in Bio2RDF as in [68], and selected descriptors
occurring in at least three drugs. 2DChem descriptors are drug
chemical descriptors represented in the form of a matrix suchthat each row of the matrix corresponds to chemical featuresof an atom in a drug. To represent 2DChem descriptors, we
extracted 53 chemical properties of each atom in the drug’s
molecule, hence each drug is represented in the form of a matrix
that the number of rows equals to the number of atoms ofthe drug and the number of columns is 53 (see supplement
materials). In our experiments, 2DChem descriptors are onlyused for DCN model [78], other models use PCBio descriptors.
Two commonly used metrics were selected to evaluate prediction performance: area under the ROC curve (AUC) and area
under the precision-recall curve (AUPR) [57, 64, 67, 79].
We used 10-fold cross-validation for the experiment. The
hyperparameters of each model were selected by grid searches
to obtain the highest prediction performances. In detail, thenumber of neighbors for LNSM was 60, SVMs were run with an
rbf kernel and the soft-margin hyperparameter was 1. RF was

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Fig. 8. Learning latent variables and using latent variables.

Num ADRs

Nguyen et al.

173

Table 8. Statistics of the used drug descriptors
Name

SourceSize

PCBio
2DChemPubchem+Bio2RDF
Pubchem7593[N_ATOMS_OF_DRUG,53]

Table 9. Performance comparison of drug–ADR prediction models on Aeolus dataset and PCBio descriptors. Results for
AUC and AUPR contain mean and standard error values in the format value × 10−2
Models
RF

LR

CCA

MF

Latent models
MLN

86.07 ± 0.56
59.04 ±1.58
7389.26 ±0.47
67.57 ±1.6322642

86.82 ±0.41
61.92 ±1.11
18189.00 ±0.40
66.75 ±1.08
365864.51 ±1.05
34.17 ±2.0731787.13 ±0.03
61.03 ±1.132589.55 ±0.39
68.70 ±1.23
186

Table 10. Summary of the models in terms of performance, non-linearity and dimensional reduction

Non-latent

LatentModels

AUC ranking

Time ranking

LNSM
SVMs
RF
LR
CCA
MF
MLN
DCN

6
2
5
3
7
4
1
8

2
8
3
6
4
1
4
7

run with 80 estimators. CCA had 60 pairs of canonical variables,
MF had 60 latent factors and MLF had two hidden layers with thesizes of 1000 and 800. DCN had the same architecture describedin [78] with four convolutional and pooling layers. The detailof selecting hyperparamters is provided in the supplemental
material.
We calculated the average computational time of each fold.
The computational time was evaluated on a computer with Intel
Core i7-6700 CPU and 16 GB RAM.

ii. Experimental results
The results of prediction performances and computational time
are presented in Table 9. In addition, DCN with 2DChem descriptors achieved 73.80±0.46 in AUC, 39.10±0.63 in AUPR and 4862(s)of computational time.
The results show that MLN is the model having the highest
prediction performances in both AUC and AUPR (89.55 ×10−2
and 68.70 ×10−2 ). SVMs are the second highest model with
89.26 × 10−2 and 67.57 × 10− 2 for AUC and AUPR. In terms ofcomputational time, MF is the fastest model, and SVMs are theslowest one. CCA and DCN are the two models having the lowest
prediction performances.
We summarize the properties of the models in terms oflinearity and dimensional reduction, and rank the performancesof the models in AUC and computational time as in Table ??.
This table shows that in balancing between prediction accuracy
and computational time, two latent variable models, MLN and
MF, are the two most promising ones. In addition, latent variable
models learn latent representation vectors of small size fordrugs, which are much smaller than the original size of the
drug descriptor vectors. This dimensional reduction can help
to remove redundant information from drug descriptors. We

Non-linearity

Dimensional reduction

X
X
X
X
X
X
X

X
X

also can see that three out of the four highest AUC models are
non-linear, suggesting that there are non-linear relationships
between drug descriptors and ADRs.

Task 3: ADR mechanism analysis
The objective of this task is to reveal associated biological components such as proteins or pathways of ADRs. In this task,
non-clinical data of drug–protein interactions, protein-pathways
and chemical-pathways are combined with clinical data, usually drug-ADR benchmark data. There are two commonly usedapproaches for this task: (i) using sparse learning and (ii) using
network mining.
i. Using sparse learning
In the sparse learning approach, the idea is to consider associated biological components of each drug as a feature vector,
and then find associated features corresponding to ADRs. To
do this, weight vectors over biological components and ADR
are used with sparse constraints by applying L1 regularization.
Remaining subsets with high weights of biological components
and ADRs are associated with each other. We describe twostudies using this approach with logistic regression and CCA.
Logistic regression with regularization was proposed toobtain associated biological pathways with each ADR [84]. To
obtain pathways associated with ADR j, let wj ∈ Rm be weights
over m pathways obtaining fromdarg minwj

11X− yi,j log
d
1 + exp(−hi · wj )i=1

− (1 − yi,j ) log(1 −
1) + λw wj 1
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AUC (×10−2 )AUPR (×10−2 )Time (s)LNSM

Non-latent models
SVMs

174

A survey on ADR

Discussion
This survey addresses ADR-related studies in three aspects: data,
drug descriptors and tasks with corresponding methods. We
divide data sources into clinical and non-clinical data. Clinicaldata contain important personal context information such as
ADRs, diseases, dosages of treatments and demographic information. Non-clinical data contain more detailed information ofdrugs and biological systems with chemical, physical propertiesof drugs, drug–protein interactions and biological pathways.
We summarize the commonly used drug descriptors in ADR
studies. In addition to traditional physical and chemical descriptors, many studies integrate biological descriptors of drugs tohave better drug information.
There are three main tasks in ADR studies: creating drugADR benchmark data, drug–ADR prediction and ADR mechanism analysis. Association rule mining is the commonly usedmethod for creating drug–ADR benchmark data. The drug–ADR
prediction task is classified into two classes: personalized drug–
ADR prediction and general drug–ADR prediction. In the formerclass, Poisson models are widely used. In the latter class, thecommonly used machine learning models can be categorized
into non-latent variable models and latent variable models. The
non-latent variable models predict drug-ADR without learning
latent variables, while the latent variable models learn latentvectors of small size to represent drugs such that these latentvectors can help the prediction efficiently. The experimental
results show that MLN is the model having the highest prediction performances, and the latent variable models have thepotential for further development. In ADR mechanism analysis,using sparse learning and network mining are two commonlyused approaches.
From this survey, we have three remarks on problems incurrent ADR studies as follows in current ADR studies as follows:
1) Most of drug–ADR prediction studies address monopharmacy cases in SIDER benchmark data. There are few studies
proposed models for polypharmacy prediction, for example, predicting with TWOSIDES benchmark data [76], in spite of the factthat most of significant ADRs come from drug combinations [19,76].
2) ADR data sources are not effectively used. Recent ADR
studies only use either clinical data without non-clinical data

information or use ADR benchmark data and non-clinical data
without personal context information. There are no studies thatcombine full clinical data with non-clinical data. In addition,
current ADR benchmark data such as SIDER, OFFSIDES andTWOSIDES only contain drugs and ADRs, other personal context
information still remains in original clinical records.
3) Machine learning models are mostly used as black boxes
for drug–ADR prediction, since they only output association
scores of drugs and ADRs. In ADR discovery, explaining ADR
mechanisms is a big challenge. It is not only a problem of predicting corresponding ADRs of drugs but also how ADRs occur.
However, predicting and revealing ADR mechanisms are now
considered as two separate parts. Designing drug–ADR prediction models that reveal related information of ADR mechanisms
seems to be an important topic.
In conclusion, the use of machine learning models in ADR
studies is likely to develop in the future. Effectively using available data with suitable models still remains a big challenge. It
is not only drug–ADR prediction is an important task, but also
revealing ADR mechanisms is another task to concentrate on.

Key Points• Machine learning methods are prominent tools for ADR
studies.
• There are three main tasks in ADR studies: creating
ADR benchmark data, drug–ADR prediction and ADR
mechanism analysis.
• For drug–ADR prediction, latent variables models havethe potential for further development.
• Remaining issues of ADR studies: (i) There are very few
drug–ADR prediction models addressing polypharmacy
ADR, (ii) ADR data sources are not effectively usedand (iii) drug–ADR prediction models lack the ability to
explain ADR mechanisms.

Funding
D.A.N. has been supported in part by Otsuka Toshimi ScholarshipFoundation. C.H.N. has been supported in part by MEXT Kakenhi
18K11434. H.M. has been supported in part by JST ACCEL [grant
number JPMJAC1503], MEXT Kakenhi [grant numbers 16H02868
and 19H04169], FiDiPro by Tekes (currently Business Finland) and
AIPSE by Academy of Finland.

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