Tabei et al. BMC Systems Biology 2019, 13(Suppl 2):39
https://doi.org/10.1186/s12918-019-0691-1

RESEARCH

Open AccessNetwork-based characterization of
drug-protein interaction signatures with aspace-efficient approach
Yasuo Tabei1* , Masaaki Kotera2 , Ryusuke Sawada3 and Yoshihiro Yamanishi3,4
From The 17th Asia Pacific Bioinformatics Conference (APBC 2019)
Wuhan, China. 14–16 January 2019

Abstract
Background: Characterization of drug-protein interaction networks with biological features has recently becomechallenging in recent pharmaceutical science toward a better understanding of polypharmacology.
Results: We present a novel method for systematic analyses of the underlying features characteristic of drug-protein
interaction networks, which we call “drug-protein interaction signatures” from the integration of large-scale
heterogeneous data of drugs and proteins. We develop a new efficient algorithm for extracting informative drugprotein interaction signatures from the integration of large-scale heterogeneous data of drugs and proteins, which ismade possible by space-efficient representations for fingerprints of drug-protein pairs and sparsity-induced classifiers.
Conclusions: Our method infers a set of drug-protein interaction signatures consisting of the associations between
drug chemical substructures, adverse drug reactions, protein domains, biological pathways, and pathway modules.
We argue the these signatures are biologically meaningful and useful for predicting unknown drug-protein
interactions and are expected to contribute to rational drug design.
Keywords: Drug-protein interaction prediction, Drug discovery, Large-scale prediction

Background
Target proteins of drug molecules are classified into a primary target and off-targets. The former is the desired
target, whereas the latter could lead to adverse drug reactions [1] or unexpected beneficial effects in drug repositioning [2]. Therefore, comprehensive analysis throughout
primary targets and off-targets on a genome-wide scaleis crucial in drug discovery. The in silico approach is
expected to improve the research productivity in this field.
Several computational methods have been presented
for predicting drug-protein interactions (or compoundprotein interactions) from chemogenomic and pharmacogenomic viewpoints on a large-scale. The basic idea
behind the chemogenomic approach is that chemically*Correspondence: yasuo.tabei@riken.jp
RIKEN Center for Advanced Intelligence Project, Nihonbashi 1-chome Mitsui
Building, 15th floor, 1-4-1 Nihonbashi, Chuo-ku, 103-0027, Tokyo, Japan
Full list of author information is available at the end of the article
1

similar drugs are expected to interact with similar proteins, with which the similarity of drugs and proteins aredefined based on their side-effects and the amino acidsequences, respectively [3–8]. On the other hand, the key
idea behind the pharmacogenomic approach is that phenotypically similar drugs are predicted to interact withsimilar proteins, on the basis of drug side effects and/or
protein sequences [9–12]. However, previous predictivemodels are not easily interpretable, making it difficult
to extract biological features characterizing drug-protein
interactions and making it impossible to give insights into
the theoretical basis of interactions.
The characterization of drug-protein interaction networks with biological characteristics has become achallenging problem in modern pharmaceutical science
toward better understanding of poly-pharmacology. It ishypothesized that polypharmacology is involved in various features of drugs and target proteins (e.g., chemical substructures, pharmacophores, functional sites, and© The Author(s). 2019 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0
International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, andreproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to theCreative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Tabei et al. BMC Systems Biology 2019, 13(Suppl 2):39

pathways) and complicated associations between the heterogeneous features.
A variety of feature extraction methods have recently
been proposed for automatically characterizing drugprotein interactions. A data mining method was proposed
for extracting molecular substructure pairs appearing frequently in interacting drug-target pairs [13]. Machinelearning methods with sparse statistical models were presented to associate protein domains with drug chemical
substructures [14, 15] or with drug side effects [16]. The
inference of proteins eliciting drug side effects has beenreported by several groups [17, 18]. However, the scalability of these methods is very limited, and these studies
were conducted from the perspective of either proteinfunctional sites, drug chemical substructures or drug phenotypic effects. There is a strong and growing need todevelop efficient and scalable methods for characterizing overall drug-protein interactions with many types offeatures of drugs and proteins at once.
We present a novel method for systematic analysesof the underlying features characteristic of drug-protein
interaction networks, which we call “drug-protein interaction signatures”. We develop a new efficient algorithm
for extracting informative drug-protein interaction signatures from the integration of large-scale heterogeneous
data of drugs and proteins, which is made possible by spaceefficient representations for fingerprints of drug-protein
pairs and sparsity-induced classifiers. In the results, ourmethod infers a set of drug-protein interaction signaturesconsisting of the associations between drug chemical substructures, adverse drug reactions, protein domains, biological pathways, and pathway modules. We argue thatthese signatures are biologically meaningful and usefulfor predicting unknown drug-protein interactions. To thebest of our knowledge, this is the first report on characterizing a large-scale drug-protein interaction networkwith various biological features of drugs and proteins in
an integrative framework. The drug-protein interaction
signatures comprehensively inferred with our method are
expected to contribute to rational drug design.

Results
Drug-protein interactions

We got the information on drug-protein interactions fromfive databases: ChEMBL [19], KEGG [20], DrugBank [21],PDSP Ki [22], and Matador [23]. The number of uniquedrug-protein interactions in the merged dataset is 78,692.
These interactions involve 2302 drugs and 2334 target
proteins, and the number of all possible drug-protein pairs
is 5,372,868. We utilized this dataset in our experiments.
Drug profiles

We described drug chemical structures by 17,017 chemical substructures using the KEGG Chemical Function andPage 2 of 15

Substructures (KCF-S) descriptor [24]. We represented
each drug by a 17,017-dimension binary vector where thepresence or absence of each of the KCF-S substructures
is coded as 1 or 0. The resulting vector is referred to as achemical profile.
We obtained the information about adverse drug reactions (ADRs) from the public release of the adverse
event reporting system (AERS) of the US Food and Drug
Administration (FDA) [25]. We derived 2,904,050 reports
from 2004 to 2010 and mapped the drug names to KEGG
following a previous study [12]. Based on the resulting
10,543 ADRs, we represented each drug by a 10,543dimension binary vector where the presence or absenceof each ADR is coded as 1 or 0. The resulting vector isreferred to as an ADR profile.
Finally, we constructed an integrative feature vector ofeach drug by concatenating the chemical and the ADR
profiles into a single one. The dimension of the resulting
feature vector of each drug was 27,560.
Protein profiles

We obtained functional domains, biological pathways,
and pathway modules (compactly clustered pathways)
about proteins from the KEGG [20] and the PFAM [26]databases.
Based on 2678 PFAM domains, we represented eachprotein by a 2678-dimension binary vector where thepresence or absence of a functional domain is coded as 1
or 0. The resulting vector is referred to as domain profile.
Based on 270 KEGG pathway maps, we represented eachprotein by a 270 dimension binary vector where the presence or absence of the involvement in a biological pathway
is coded as 1 or 0. The resulting vector is referred to as apathway profile. Based on 107 KEGG pathway modules,
we represented each protein by a 107-dimension binary
vector where the presence or absence of the involvement
in a pathway module is coded as 1 or 0. The resulting
vector is referred to as module profile.
Finally, we constructed an integrative feature vector ofeach protein by concatenating the domain, pathway, andmodule profiles into a single profile. The dimension of theresulting feature vector of each protein was 3055.
We address the problem of extracting features characterizing drug-protein interaction networks in the framework of supervised classification.
Linear model for drug-protein pairs

Let C be a drug (or a drug candidate compound) and
let P be a target protein (or a target candidate protein). We represent a drug-protein pair (C, P) as a highdimensional feature vector (C, P) and present a linear
function, f (C, P) = wT (C, P), whose output is used to
predict whether a (C, P) is an interacting pair or not. The
weight vector w is estimated such that each drug-protein

Tabei et al. BMC Systems Biology 2019, 13(Suppl 2):39

pair is correctly classified into the interaction class (positive class) or non-interaction class (negative class) basedon the training set.
An advantage of the linear model is that one caninterpret features effective for predictions from learned
models. Since each element in (C, P) corresponds to anelement of w, effective features can be selected by extracting highly weighted features. However, the performanceof the linear model depends heavily on the feature vector
design.
We represent each drug-protein pair as a high dimension feature vector by taking the tensor product of a drug
profile and protein profile. The representation is similar
to that in previous studies [15, 16]. The profile of a C isdefined as a D-dimension binary vector:
(C) = (c1 , c2 , ..., cD )T ,where ci ∈ {0, 1}, i = 1, ..., D. The profile of a P is defined
as a D -dimension binary vector: (P) = (p1 , p2 , ..., pD )T ,where pi ∈ {0, 1}, i = 1, ..., D . We compute the tensor
product between a drug profile (C) and protein profile
(P), and define a feature vector (C, P) as follows:(C, P) = (c1 p1 , c1 p2 , ..., c1 pD , c2 p1 , ...cD p1 , ..., cD pD )T .
where (C, P) is composed of all possible products
between elements in (C) and those in (P). The resulting feature vector is a D × D -dimension binary vector, i.e.,fingerprint, for encoding cross-integrated biological features. This is referred to as a “tensor-product fingerprint”.
In this study, (C) was a 27,560-dimension binary vector, and (P) was a 3055-dimension binary vector. Thus,
the tensor-product fingerprint (C, P) of each drugprotein pair is a 84,195,800-dimension binary vector.
A simpler way for representing each drug-protein pair is
to concatenate (C) and (P) into a single feature vectorT
as (C, P) = (C)T , (P)T [7]. However, it cannot
determine the correlation between drug and protein features. The feature vector is referred to as a “concatenatedfingerprint”.
Logistic regression

We apply logistic regression to train the weight vector in
the linear model and introduce L1 -regularizations to prevent over-fitting. The L1 -regularization induces sparsity inthe weight vector and drives most of the weight elements
corresponding to unimportant features to zeros, whichmakes it easier for us to interpret the model and extract
features.
Minimizing the logistic loss with L1 -regularization fora large number of high dimensional data is difficult, butseveral efficient algorithms have recently been proposed.
To the best of our knowledge, LIBLINEAR [27] is themost efficient and high-performance algorithm, but it
requires a huge amount of memory for extremely highdimensional data. In fact, it was not computationally

Page 3 of 15

feasible for our dataset in this study because of the memory problem (see the “Results” section). To overcome thisdifficulty, we introduce a gradient-based method.
of drug-protein pairs and their labels Given a collection
(Ci , Pj ), yij where yij ∈ {+1, −1} (i = 1, ..., n, j =1, ..., m), the logistic loss is defined as
LR(w) =

m
n 




log(1 + exp −yij wT (Ci , Pj ) .

i=1 j=1

The logistic loss with L1 -regularization is defined as
m
n 




L1 -LR(w) =log 1+exp −yij wT (Ci , Pj ) +Cw1 ,i=1 j=1

where w1 is L1 norm (the sum of absolute value in thevector) and C is a regularization parameter.
Since L1 -LR(w) is a convex function, the weight vector
w minimizing L1 -LR(w) can be found at zero of its gradient. However, it is impossible to compute the gradientof L1 -LR(w), because L1 norm contains non-differential
points where wd = 0. Instead, we compute the d-th
dimensional gradient ∇d LR(w) of LR(w) as follows:


m
n 

−yij d (Ci , Pj ) exp −yij wT (Ci , Pj ),∇d LR(w) =1 + exp −yij wT (Ci , Pj )i=1 j=1
where d (Ci , Pj ) is the d-th dimensional value of(Ci , Pj ). We then compute the D × D -dimensional gra
dient vector ∇LR(w) ∈ D×D asT∇LR(w) = ∇1 LR(w), ∇2 LR(w), ..., ∇D×D LR(w) .
The use of ∇LR(w) enables the global minimum forthe optimal w in L1 -LR(w) to be found using an efficient
gradient-based optimization algorithm called orthantwise limited-memory quasi-newton (OWL-QN) [28]. The
L1 -regularized logistic regression methods, with the tensor product of the fingerprint proposed and with theconcatenated fingerprint, is referred to as L1LOG-tensor
and L1LOG-concat, respectively.
For comparison, we also trained models with L2 regularized logistic regression using the gradient-basedalgorithm called the limited memory quasi-Newton(L-BFGS) [29]. The L2 -regularized logistic regression
method, with the tensor-product fingerprint and the concatenated fingerprint, are referred to as L2LOG-tensor and
L2LOG-concat, respectively.
Space-efficient representation of drug-protein pairs

Compact representation of drug-protein pairs is crucial
for training linear models in memory, so we use the set
representation with items corresponding to dimensions of
one bit in the fingerprint. However, this still consumes
a huge amount of memory for storing them, resulting

Tabei et al. BMC Systems Biology 2019, 13(Suppl 2):39

Page 4 of 15ii) Set represetation of
fingerprints (SET)i) Fingerprints of drug-protein pairsiv) Variable-length array representation of
fingerprints (VLA). S’ (Ci,Pj) is preresented by
two bit strings Rij ,and Pij. S’ (Ci,Pj)[k] is represented
by corresponding substring in Rij. Pij encodes
length of each substring of Rij.
Bit string R11
Bit string P11
Bit string R12
Bit string P12

1
1
10
01

1
1
10
01

10
01
10
01

10
01

Bit string R13
Bit string P13

10
01

10
01

10
01

100
001

iii) Compute differences between k-th element
S(Ci,Pj)[k] and (k-1)-th element S(Ci,Pj)[k-1]v) Trie representation of fingerprints (left) and succinct trie representation of
fingerprints (SUCTRIE) (right). SUCTRIE consists of bit string and two arrays.

Trie

0
1

1
2
1
4
2
6

Succinct trie (SUCTRIE)

2
3

BitstringT

2

Array D

5
7
2
8

2
4
9

S11 S12 S13Array I

6

8

9

Fig. 1 Brief summary of constructing space-efficient representations of fingerprints for drug-protein pairs constructed with our proposed method:
VLA and SUCTRIE

Fig. 2 Part of obtained drug-protein interaction signature network among five features, i.e., drug chemical substructures (blue), adverse drug
reactions (red), protein domain (gray), biological pathway (green), and pathway module (yellow). Node size represents degree of each feature, andedge width represents corresponding weight in model

Tabei et al. BMC Systems Biology 2019, 13(Suppl 2):39(a)Page 5 of 15(b)Fig. 3 Example of drug-protein interaction signature: association between drug-chemical substructure (SKELETON C1b(N1d)-C1b(O7a) in KCF-S
format) and biological pathway (hsa04080 Neuroactive ligand-receptor interaction). a Horizontal axis shows drugs sharing chemical substructure,and vertical axis shows proteins sharing biological pathway. Color of each element corresponds to number of databases storing corresponding
interaction. b Chemical structures of drugs sharing substructure are shown, and extracted substructure is highlighted in red (see Table 1 for further
details)Tabei et al. BMC Systems Biology 2019, 13(Suppl 2):39

Page 6 of 15

in limited scalability in memory for extremely highdimensional data. To overcome this memory problem,
we constructed two space-efficient representations of fingerprints. We present a brief overview of these representations (further details are given in the supplemental
material [30]).
Figure 1 illustrates the construction of our two representations. We first represent each fingerprint (Ci , Pj ) asa set S(Ci , Pj ) = {d|d (Ci , Pj ) = 1, d = 1, ..., D × D } that
Table 1 Association between KCF-S “RING C1x-C1x-C1y(C1z)C1y(C2x)-C1y-C1x-C1x-C1z(C5a+O7a)-C1z(C1a)” and KEGG
pathway hsa04080 Neuroactive “ligand-receptor interaction”. See
also Fig. 4
KCF-S

RING C1x-C1x-C1y(C1z)-C1y(C2x)-C1y-C1xC1x-C1z(C5a+O7a)-C1z(C1a)

PathwayDrughsa04080 Neuroactive ligand-receptor interaction
D00952 Megestrol acetate (antineoplastic)
D01299 Chlormadinone acetate (progestin)
D01368 Cyproterone acetate (anti-androgen)Proteinhsa:10800 cysteinyl leukotriene receptor 1
hsa:1128-hsa:1133 muscarinic acetylcholine receptor M1 - M5
hsa:1134 nicotinic acetylcholine receptor alpha-1
hsa:1268 cannabinoid receptor 1
hsa:134,hsa:135,hsa:140 adenosine A1 receptor A1, A2a, A3
hsa:146,hsa:150,hsa:151 adrenergic receptor alpha-1D,2A,2B
hsa:1511 cathepsin G
hsa:152 adrenergic receptor alpha-2C
hsa:153-hsa:155 adrenergic receptor beta-1,2,3
hsa:1812-hsa:1816 dopamine receptor D1-D5
hsa:185-hsa:186 angiotensin II receptor type 1,2
hsa:1909-hsa:1910 endothelin receptor type A, B
hsa:2908 glucocorticoid receptor
hsa:3269,hsa:3274 histamine receptor H1,H2
hsa:3356,hsa:3357,hsa:3358 5-hydroxytryptamine receptor 2
hsa:3362 5-hydroxytryptamine receptor 6
hsa:4159-hsa:4161 melanocortin receptor 3,4,5
hsa:4886,hsa:4887 neuropeptide Y receptor type 1/4/6,2
hsa:4985 delta-type opioid receptor
hsa:4986 kappa-type opioid receptor
hsa:4988 mu-type opioid receptor
hsa:552 arginine vasopressin receptor 1A
hsa:5724 platelet-activating factor receptor
hsa:624 bradykinin receptor B2
hsa:6865,hsa:6869 tachykinin receptor 1,2
hsa:7068 thyroid hormone receptor beta
hsa:7253 thyroid stimulating hormone receptor
hsa:7433 vasoactive intestinal peptide receptor 1
hsa:886 cholecystokinin A receptor

Fig. 4 The association between KCF-S “RING C1x-C1x-C1y(C1z)C1y(C2x)-C1y-C1x-C1x-C1z(C5a+O7a)-C1z(C1a)” and KEGG pathway
hsa04080 Neuroactive “ligand-receptor interaction” a The heat map
shows the numbers of databases that register confirmed drug-protein
interactions from KEGG, DrugBank, Matador, Chembl, PSD pi
databases. Horizontal and vertical axes show drugs and proteins,respectively. Gray, blue, green, yellow, orange and red indicate that 0,
1, 2, 3, 4 and 5 databases contain the corresponding interaction. b
Chemical structures of some drugs, where red areas (if any) show theextracted substructure indicated by KCF-S. See also Table 1

Tabei et al. BMC Systems Biology 2019, 13(Suppl 2):39

contains items corresponding to dimensions of one bit in(Ci , Pj ). We refer to a set representation of fingerprints
as SET. To minimize each item, we then compute the difference between the k-th item S(Ci , Pj )[ k] and (k − 1)-thitem S(Ci , Pj )[ k−1] as (S(Ci , Pj )[ k] −S(Ci , Pj )[ k−1] ) andkeep the results in a new set S (Ci , Pj ). We can recover
S(Ci , Pj ) by cumulatively adding the items in S (Ci , Pj ).
We constructed our two space-efficient representationsof fingerprints by leveraging the idea behind succinct
data structures that achieve space-efficient representations of data structures while preserving the property offast operations. The first one is a variable-length array forcompactly representing fingerprints. The S (Ci , Pj ) is represented by two bit strings Rij and Pij which are indexed byrank/select dictionary, i.e., a succinct data structure for bit
strings. We can randomly access any element in S (Ci , Pj )in O(1) time by using fast operations in the rank/selectdictionary [31]. We refer to this variable-length array
representation of fingerprints as VLA.
The second one is a type of succinct trie for representing fingerprints. The trie is a data structure forstrings, and it is also practical for representing fingerprints. A standard point-based implementation of trie
consumes a huge amount of memory, resulting in limited
scalability. Alternatively, we present a compact representation of trie by using a succinct data structure called
LOUDS [32]. We can recover the original fingerprints
by traversing a succinct trie in a depth-first manner. We
refer to this succinct trie representation of fingerprints as
SUCTRIE.

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Figure 2 shows a network representation of someof the drug-protein signatures extracted with L1LOGtensor, where highly weighted associations of five featuresof drugs or proteins, that is, drug-chemical substructures (blue), adverse drug reaction (red), protein pathway(green), pathway module (yellow) and protein domain(gray). Only selected results are shown due to space limitation. The inferred signature association network provides us with clues about the important features behindthe drug-protein interaction network. There has been no
study on the inference of these associations.
Biological interpretation of the extracted signatures

We constructed biological interpretations for the drugprotein interaction signatures extracted with L1LOGtensor. We give only two examples due to space
limitation. The result of all analyzed signatures andthe figures/tables are presented in the supplemental
material [30].

Table 2 Example of drug-protein interaction signature:association between adverse drug reaction (ADR) (R01631
Graft-versus-host disease) and protein domain (PF14446
Prokaryotic RING finger family 1)
Extracted ADR

R01631 Graft-versus-host disease

Extracted domain

PF14446 Prokaryotic RING finger family 1

Drugs sharing theextracted ADR

D00322 Fluconazole (antifungal)
D00333 Ganciclovir (antiviral)

Extraction of drug-protein interaction signatures

D00399 Valproic acid (anticonvulsant)We applied the proposed method (L1LOG-tensor)
to extract drug-protein interaction signatures fromdrug profiles (chemical substructures and adverse drug
reactions) and protein profiles (protein domains, biological pathways, and pathway modules), based on a largescale drug-protein interaction network. Each signature is
the association between a drug feature and protein feature, where two features in the same signature are thoughtof as being associated in terms of drug-protein interactions. The results of all extracted drug-protein interaction
signatures are presented in the supplemental material[30].
L1LOG-tensor extracted 105,684 signatures, while
L2LOG-tensor extracted 7,843,218 signatures. Note thatthe number of all possible combinations of drug featuresand protein features is 84,195,504. The number of signatures from our L1LOG-tensor method was much lessthan that of L2LOG-tensor, due to the sparsity inducedby L1-regularization. This makes it easier to analyze theextracted drug-protein interaction signatures for biological interpretation, so we focused on analyzing the resultsfrom L1LOG-tensor below.

D00407 Methylprednisolone(glucocorticoid)
D06272 Sorafenib tosilate (anticancer,
antineoplastic)
D06413 Nilotinib hydrochloride(antineoplastic)
D08062 Idarubicin (antineoplastic, antibiotic)
D08066 Imatinib (antineoplastic)
D08524 Sorafenib (antineoplastic,
anticancer)
D08556 Tacrolimus (immunosuppressant)Proteins sharing
extracted domain

hsa:5587,hsa:23683,hsa:25865 protein kinaseD
hsa:51317 PHD finger protein 21A
hsa:5580 protein kinase C
hsa:64283 Rho guanine nucleotide
exchange factor
hsa:673 B-Raf proto-oncogene protein
kinasehsa:80829 ZFP91 zinc finger protein

Further details are given in Fig. 5

Tabei et al. BMC Systems Biology 2019, 13(Suppl 2):39

Figure 3 shows an extracted signature representing
the association between a drug-chemical substructure(SKELETON C1b(N1d)-C1b(O7a) in the KCF-S format)
and biological pathway (hsa04080 Neuroactive ligandreceptor interaction), where the vertical axis on theheat map (a) shows all drugs sharing the extracted
substructure, and the horizontal axis shows all proteins sharing the extracted pathway. The extracted drugchemical substructures on the associated drug structures (b) are in pink. Drugs and proteins in known
interacting pairs tend to have such extracted featuresin the same signature. For example, Propantheline bromide (D00481), Methanthelinium bromide (D00721),
Acetylcholine chloride (D00999), Carbachol (D00524),
Succinylcholine chloride (D00766), and Suxamethonium
chloride (D02275) share a choline skeleton, and all known
to act on acetylcholine receptors. However, there aremany other drugs sharing the extracted drug featureand proteins sharing the extracted protein feature, andthe drug-protein interactions are not known. Thus, it
may be possible to predict previously unknown interactions between drugs and proteins through the extracted
features in the signatures. See Table 1 and Fig. 4
for detail.
Table 2 shows an extracted signature representing theassociation between an ADR (R01631 Graft-versus-hostdisease) and protein domain (PF14446 Prokaryotic RING
finger family 1), where all drugs sharing the extracted
ADR and all proteins sharing the extracted protein
domain are also shown. Interestingly, most drugs sharing
the ADR (R01631 Graft-versus-host disease) were related

Page 8 of 15

to inflammation, immunosuppression, and cancer, which
supports the recently expanded concept that inflammation is a critical component of cancer progression [33]. See
Fig. 5 and Table 3 for detail.
Figure 4 shows an extracted signature representing
the association between a drug-chemical substructure(RING C1x-C1x-C1y(C1z)-C1y(C2x)-C1y-C1x-C1x-C1z(C5a+O7a)-C1z(C1a) in the KCF-S format) and biological pathway (hsa04080 Neuroactive ligand-receptor
interaction). It was observed that Megestrol acetate(D00952), Cyproterone acetate (D01368) and Chlormadinone acetate (D01299) share common ring structures.
All these drugs are known to act on many neuroactive
ligand-receptors. See Table 1 for detail.
Figure 6 show an extracted signature representing
the association between a drug-chemical substructure(SKELETON C5a(N1b+O5a)-C1c(N1b)-C1b-C8y-C8xC8x-C8x-C8x-C8x in the KCF-S format) and biologicalpathway (hsa03050 Proteasome). Proteasome inhibitors
have been applied to the treatment of cancer, especiallymultiple myeloma. The substructure “SKELETON C5a(N1b+O5a)-C1c(N1b)-C1b-C8y-C8x-C8x-C8x-C8x-C8x”
corresponds to a phenylalanine residue, which is captured
as a characteristic substructure in known proteasome
inhibitors Bortezomib (D03150) and Carfilzomib (D08880).
See Table 4 for detail.
Performance evaluation on generalization property

If the extracted signatures are biologically meaningful interms of drug-protein interactions, they need to havegood generalization to predict drug-protein interactions.

(a)

(b)

Fig. 5 The association between KCF-S “RING C1x-C1x-C1y(C1z)-C1y(C1x)-C1y(C1x)-C1z(C1a+C1y)” and KEGG pathway module “hsa_M00110
C19/C18-Steroid hormone biosynthesis”. a The heat map shows the numbers of databases that register confirmed drug-protein interactions from
KEGG, DrugBank, Matador, Chembl, PSD pi databases. Horizontal and vertical axes show drugs and proteins, respectively. Gray, blue, green, yellow,
orange and red indicate that 0, 1, 2, 3, 4 and 5 databases contain the corresponding interaction. b Chemical structures of some drugs, where redareas (if any) show the extracted substructure indicated by KCF-S. See also Table 3

Tabei et al. BMC Systems Biology 2019, 13(Suppl 2):39

Page 9 of 15

Table 3 The association between KCF-S “RING C1x-C1x-C1y(C1z)-C1y(C1x)-C1y(C1x)-C1z(C1a+C1y)” and KEGG pathway module“hsa_M00110 C19/C18-Steroid hormone biosynthesis”
KCF-S

RING C1x-C1x-C1y(C1z)-C1y(C1x)-C1y(C1x)-C1z(C1a+C1y)

Modulehsa_M00110 C19/C18-Steroid hormone biosynthesis

Drug

D00040 Cholesterol (pharmaceutic aid)

D00066 Progesterone (progestin)

D00067 Estrone (estrogen)

D00075 Testosterone (androgen)

D00105 Estradiol (estrogen)

D00182 Norethisterone (progestin)

D00185 Estriol (estrogen)D00289 Danazol (Anterior pituitary suppressant)D00312 Estrone sodium sulfate (estrogen)D00321 Finasteride (alpha-reductase inhibitor)

D00389 Metandienone (androgen)

D00408 Methyltestosterone (androgen)D00443 Spironolactone (diuretic)

D00444 Stanozolol (androgen)

D00462 Oxandrolone (androgen)D00490 Oxymetholone (androgen)D00492 Pancuronium (neuromuscular blocking agent)

D00554 Ethinylestradiol (estrogen)D00575 Mestranol (estrogen)

D00734 Ursodeoxycholic acid (anticholelithogenic)

D00765 Rocuronium (neuromuscular blocking agent)

D00767 Vecuronium (neuromuscular blocking agent)

D00818 Maprotiline hydrochloride (antidepressant)D00948 Estropipate (estrogen)D00949 Hydroxyprogesterone caproate (progestin)

D00951 Medroxyprogesterone acetate (progestin)

D00952 Megestrol acetate (antineoplastic)

D00953 Norethisterone acetate (progestin)

D00954 Norgestrel (progestin)

D00955 Nandrolone decanoate (androgen)D00956 Nandrolone phenylpropionate (androgen)D00957 Testosterone cypionate (androgen)D00958 Testosterone enanthate (androgen)D00959 Testosterone propionate (androgen)

D00963 Exemestane (antineoplastic)

D01161 Fulvestrant (antiestrogen)D01180 Trilostane (adrenocortical suppressant)D01217 Dydrogesterone (progestin)

D01299 Chlormadinone acetate (progestin)

D01301 Metenolone enanthate (anabolic)D01368 Cyproterone acetate (anti-androgen)D01375 Metenolone acetate (anabolic)D01413 Estradiol valerate (estrogen)

D01617 Estradiol dipropionate (estrogen)D01639 Tibolone (Menopausal symptoms suppressant)D01943 Potassium canrenoate (aldosterone antagonist)

D01953 Estradiol benzoate (estrogen)D01986 Estriol tripropionate (estrogen)D01989 Estriol diacetate benzoate (estrogen)D02566 Maprotiline (antidepresant)D03820 Dutasteride (prostatic hyperplasia)

D03917 Drospirenone (aldosterone antagonist)

D04061 Estradiol acetate (estrogen)

D04063 Estradiol cypionate (estrogen)

D04064 Estradiol enanthate (estrogen)

D04065 Estradiol undecylate (estrogen)

D04066 Estramustine (antineoplastic)

D04316 Gestodene (progestin)

D04947 Mesterolone (androgen)D05020 Mexrenoate potassium (aldosterone antagonist)

D05209 Norgestimate (progestin)

D05640 Prorenoate potassium (aldosterone antagonist)

D06085 Testosterone ketolaurate (androgen)D06086 Testosterone phenylacetate (androgen)D06087 Testosterone undecanoate (testosterone)

D07121 Alfatradiol (five alfa-reductase inhibitor)

D07127 Norethandrolone (anabolic)

D07221 Promestriene (estrogen)D07222 Nomegestrol (progestin)

D07670 Chlormadinone (progestin)

D07766 Cyproterone (antiandrogen)D07918 Estradiol hemihydrate (estrogen)D07919 Estradiol 17 beta-hemisuccinate (estrogen)D07920 Estriol succinate (estrogen)D07921 Estriol sodium succinate (estrogen)D08052 Hydroxyprogesterone (progestin)

D08166 Medroxyprogesterone (progestin, antineoplastic)

D08167 Megestrol (progestin)

D08250 Nandrolone (anabolic, ophthalmic)

D08281 Nomegestrol acetate (contraceptive)

D08285 Norethisterone enantate (progestin)

D08409 Prasterone (androgen)D08573 Testosterone decanoate (androgen)D08574 Testosterone phenylpropionate (androgen)

D09701 Abiraterone acetate (anticancer)

Tabei et al. BMC Systems Biology 2019, 13(Suppl 2):39

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Table 3 The association between KCF-S “RING C1x-C1x-C1y(C1z)-C1y(C1x)-C1y(C1x)-C1z(C1a+C1y)” and KEGG pathway module“hsa_M00110 C19/C18-Steroid hormone biosynthesis” (Continued)
Proteinhsa:1586 cytochrome P450, family 17, subfamily A
hsa:1588 cytochrome P450, family 19, subfamily A
hsa:3283 steroid delta-isomerase
hsa:3284 steroid delta-isomerase

See also Fig. 5

We tested five feature extraction methods: L1LOGtensor, L2LOG-tensor, L1LOG-concat, L2LOG-concat,
and L1LOG-LIBLINEAR-tensor on their abilities
to reconstruct known drug-protein interactions. As
mentioned above, L1LOG-tensor is our proposed(a)method. The others are previous methods basedon current algorithms or conventional fingerprints(see the Logistic regression section for further
details). L1LOG-tensor and L2LOG-tensor use tensorfingerprints represented by our space-efficient algorithm.

(b)Fig. 6 The association between KCF-S “SKELETON C5a(N1b+O5a)-C1c(N1b)-C1b-C8y-C8x-C8x-C8x-C8x-C8x” and KEGG pathway “hsa03050
Proteasome” a The heat map shows the numbers of databases that register confirmed drug-protein interactions from KEGG, DrugBank, Matador,
Chembl, PSD pi databases. Horizontal and vertical axes show drugs and proteins, respectively. Gray, blue, green, yellow, orange and red indicate that0, 1, 2, 3, 4 and 5 databases contain the corresponding interaction. b Chemical structures of some drugs, where red areas (if any) show the extracted
substructure indicated by KCF-S. See also Table 4 for detailTabei et al. BMC Systems Biology 2019, 13(Suppl 2):39

Table 4 The association between KCF-S “SKELETON
C5a(N1b+O5a)-C1c(N1b)-C1b-C8y-C8x-C8x-C8x-C8x-C8x” andKEGG pathway “hsa03050 Proteasome”
KCF-S

SKELETON
C5a(N1b+O5a)-C1c(N1b)-C1b-C8y-C8xC8x-C8x-C8x-C8x

Pathwayhsa03050 Proteasome

Drug

D00284 Cosyntropin (hormone,
adrenocorticotropic)
D02108 Indium In 111 pentetreotide(radioactive agent)
D03150 Bortezomib (anticancer,
proteasome inhibitor)
D08880 Carfilzomib (anticancer, proteasome
inhibitor)

Proteinhsa:3458 interferon gamma
hsa:5682 - hsa:5688 20S proteasome subunit
alpha 1-7
hsa:5689 - hsa:5699 20S proteasome subunit
beta 1-10
hsa:5707, hsa:5708, hsa:5713 26S
proteasome regulatory subunit N1, N2, N8

See also Fig. 6

L1LOG-concat and L2LOG-concat use previous
concatenated fingerprints [7] represented by the LIBLINEAR algorithm [27]. L1LOG-LIBLINEAR-tensor
is a method [15, 16] which uses the tensorproduct fingerprints represented by the LIBLINEAR
algorithm [27].
We conducted the following fold cross-validation ina pair-wise manner. We first randomly divided all

Fig. 7 AUC score (left) and AUPR score (right) in pair-wise cross validation

Page 11 of 15

drug-protein pairs in the gold standard set into fivesubsets. Next, we considered four of the subsets as
a training set and the remaining subset as a test
set. We learned a predictive model on the drugprotein pairs in the training set. Finally, we applied
the predictive model to the drug-protein pairs in thetest set.
We used the receiver operating characteristic curve(ROC curve), which is defined as a plot of true positive
rates against false positive rates based on various thresholds, and the precision-recall curve (PR curve), which isdefined as a plot of precision (positive predictive value)
against recall (sensitivity) based on various thresholds, as
evaluation measures for prediction performance.
We computed the area under the ROC curve (AUC
score) and the area under the PR curve (AUPR score). The
parameters involved in each method (e.g., regularization
parameter) were fit with AUC and AUPR as the objectivefunctions.
Figure 7 shows the AUC and AUPR scores in the pairwise cross-validation, where the number of negative pairs
in the training set was changed from the same numberof positive examples to that of all possible negative examples in the training set. We observed that the prediction
accuracy of the models trained with all five methods
improved as the number of negative examples in the training set increased. This suggests that using all possible
negative examples for learning a predictive model will
enhance prediction reliability. L1LOG-tensor performed
the best.
L1LOG-LIBLINEAR-tensor did not perform well withan increasing number of negative examples in the training
set because of the memory storage problem. The learning process with the LIBLINEAR algorithm consumed
all the memory of our machine with 128GB-memory. In

Tabei et al. BMC Systems Biology 2019, 13(Suppl 2):39

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Table 5 AUC score, AUPR score, training time in seconds, and consumed memory in megabytes in the pair-wise cross validation
experiments
MethodAUC score

AUPR score

Training time (sec)Memory (MB)L1LOG-tensor

0.982 ± 0.000

0.643 ± 0.001

85,211

24,079

L1LOG-concat

0.965 ± 0.000

0.324 ± 0.000

9323

177

L2LOG-tensor

0.979 ± 0.000

0.621 ± 0.000

82,85324,079L2LOG-concat

0.963 ± 0.000

0.317 ± 0.000

9129

177

L1LOG-LIBLINEAR-tensor

−

−

−

> 131, 072

contrast, the other four methods with our space-efficientalgorithm were able to finish the training process. Thissuggests that our space-efficient algorithm is more suitable and powerful for learning a predictive model on
extremely high-dimensional data.
L1-LOG-tensor and L2LOG-tensor performed better
than L1-LOG-concat and L2LOG-concat, which suggeststhat the tensor-product fingerprint can capture relevant information for drug-protein interaction prediction.
On the other hand, the concatenated fingerprint cannot
capture enough information, even though calculation isfaster.
Table 5 shows the AUC score, AUPR score, training
time, and consumed memory in the pair-wise crossvalidation. L1LOG-tensor and L2LOG-tensor consumed
24 GB for learning predictive models on all possible drugprotein pairs, which suggests their applicability for largescale drug-protein interaction prediction. They also took
about 24 hours, which can be considered reasonable ona practical level, though they were slower than L1LOGconcat and L2LOG-concat.
In the pair-wise cross-validation, drugs and proteins intest pairs often overlap with those in the training set.

Fig. 8 AUC score (left) and AUPR score (right) in block-wise cross validation

We conducted a different 5-fold cross-validation to avoid
the overlap of drugs and proteins in test pairs between
those in the training set, which we call “block-wise crossvalidation”. The results of this block-wise cross-validation
are shown in Fig. 8 and Table 6. The same tendency in thepair-wise cross-validation was also seen in the block-wisecross-validation. However, the AUC and AUPR scores inthe block-wise cross-validation were much lower thanthose in the pair-wise cross validation. The results indicatethat predictions of unknown interactions for new drugcandidates (without known targets) and orphan proteins(without known ligands) are much more difficult thandetecting missing interactions between drugs of known
targets and proteins of known ligands in practice.
Finally, we tested SUCTRIE, VLA, and SET on theirspace-efficiencies of fingerprint representations. Notethat SET is a standard representation, and SUCTRIE andVLA are those constructed with our proposed method.
Figure 9 shows a plot of the consumed memory against
the number of fingerprints. SET is known to use a large
amount of memory for storing all possible fingerprints.
In fact, it consumed 57GB for storing all possible drugprotein pairs in our dataset, which limits its practicalTabei et al. BMC Systems Biology 2019, 13(Suppl 2):39

Page 13 of 15

Table 6 AUC score, AUPR score, training time in seconds, and consumed memory in megabytes in the block-wise cross validation
experiments
MethodAUC score

AUPR score

Training time (sec)Memory (MB)L1LOG-tensor

0.574 ± 0.056

0.068 ± 0.002

71,353

19,482

L1LOG-concat

0.596 ± 0.058

0.064 ± 0.002

8323

175

L2LOG-tensor

0.562 ± 0.059

0.060 ± 0.019

70,253

19,482

L2LOG-concat

0.577 ± 0.069

0.054 ± 0.019

8010

175

L1LOG-LIBLINEAR-tensor

−

−

−

> 131, 072

usage. In contrast, our proposed representations SUCTREE and VLA are more space-efficient than SET.
The consumed memory of SUCTREE was slightly
smaller than that of VLA. SUCTREE and VLA consumed 16 and 20 GB, respectively, for storing all possible drug-protein pairs, Suggesting the usefulness of
our SUCTREE and VLA. In fact, we were not able
to conduct all the analyses for this study without
SUCTRIE.

Conclusions
We proposed a novel method of extracting the underlying features characterizing overall drug-protein interactions, which we call “drug-protein interaction signatures”. We extracted a set of drug-protein interaction
signatures consisting of the associations between drug
chemical substructures, adverse drug reactions, protein
domains, biological pathways, and pathway modules, andargued that the extracted drug-protein interaction signatures were biologically meaningful. Our proposed method
is original in that the space-efficient representations forhigh-dimensional fingerprints of drug-protein pairs, inthe characterization of a large-scale drug-protein interaction network with various features in an integrative
framework, and in the interpretability for the extracted
feature associations.
Our proposed method will be useful for various applications in drug discovery. A limitation of the method
is that it cannot extract the associations between
different attributes of drugs or proteins. For example, it cannot detect the associations between drugchemical substructures and adverse drug reactions or
the associations between protein domains and biological pathways. Extension of the method for analyzing
such more complicated features is an important future
work.

Fig. 9 Comparison of consumed memory between different fingerprint representations: SUCTRIE, VLA and SET

Tabei et al. BMC Systems Biology 2019, 13(Suppl 2):39

Page 14 of 15

Abbreviations
ADRs: Adverse drug reactions; AERS: Adverse event reporting system; AUC
score: area under the ROC curve; AUPR score: area under the PR curve FDA:
Food and drug administration; KCF-S: KEGG chemical function andsubstructures; KEGG: Kyoto encyclopedia of genes and genomes; L-BFGS:
Limited-memory quasi-newton; OWL-QN: Orthant-wise limited-memory
quasi-newton; PR: Precision recall; ROC curve: Receiver operating characteristic
curve

7.

Acknowledgements
We thank anonymous reviewers for their thoughtful and constructive reviews.

10.

Funding
Publication of this work was supported by JST PRESTO Grant Number
JPMJPR15D8.

11.

Availability of data and materials
All results and datasets are available at [30].
About this supplement
This article has been published as part of BMC Systems Biology Volume 13
Supplement 2, 2019: Selected articles from the 17th Asia Pacific Bioinformatics
Conference (APBC 2019): systems biology. The full contents of the supplement
are available online at https://bmcsystbiol.biomedcentral.com/articles/
supplements/volume-13-supplement-2.
Authors’ contributions
YT and YY designed research. YT designed the method. YT and MK performed
the experiments. YT, MK and YY wrote the paper. All of the authors have readand approved the final manuscript.

8.

9.

12.

13.

14.

15.

16.

Ethics approval and consent to participate
Not applicable.

17.

Consent for publication
Not applicable.

18.

Competing interests
The authors declare that they have no competing interests.

19.

Author details
1 RIKEN Center for Advanced Intelligence Project, Nihonbashi 1-chome Mitsui
Building, 15th floor, 1-4-1 Nihonbashi, Chuo-ku, 103-0027, Tokyo, Japan.
2 School of Engineering, Department of Chemical System Engineering, The
University of Tokyo, 7-3-1 Hongo, Bunkyo-ku 113-8656, Tokyo, Japan.
3 Department of Bioscience and Bioinformatics, Faculty of Computer Scienceand Systems Engineering, Kyushu Institute of Technology, 680-4 Kawazu,
Lizuka, 820-8502, Fukuoka, Japan. 4 PRESTO, Japan Science and Technology
Agency, 332-0012, Saitama, Japan.

20.

Published: 5 April 201923.

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