医薬品の標的分子や副作用の予測における機械学習
山西芳裕
九州大学生体防御医学研究所

Outlinen Background¨ Chemical, genomic and pharmacological spacesn Methods for pharmaceutical applications¨ Drug target prediction from chemical andgenomic data¨ Side-effect prediction from biological datan Resultsn Concluding remarks

Drug-target interaction	
Phenotypic effect	Drug 	
interaction

Primary target
protein 	

Off-target
protein 	

Efficacy	

Side-effect	Phenotypic scale

Identification of interactions between drugs andtarget proteins is crucial in the drug development

Heterogeneous omics data	
Genomic spaceetc.	

Target proteinsetc.	

Drug chemical structures
Chemical spaceetc.	

Phenotypic effects
Pharmacological spaceOutlinen Background¨ Chemical, genomic and pharmacological spacesn Methods for pharmaceutical applications¨ Drug target prediction from chemical andgenomic data¨ Side-effect prediction from biological datan Resultsn Concluding remarks

Heterogeneous omics data	
Genomic spaceetc.	

Target proteinsetc.	

Drug chemical structures
Chemical spaceetc.	

Phenotypic effects
Pharmacological space

Objectiven Prediction of unknown drug-target interactions ona large scale from chemical and genomic data

Known interactions	112

Phenotypic 3scale

Drug 	

4
5
6

2
3

Target protein	

4
5

Unknown interactions (to be predicted)	

Examples of the data structures	
Drug	

Target protein	
MAHAAQVGLQDATSPIMEELITFHDHALMIIFLICFLVLYALFLTLTTKLTNTNISDAQEMETVWTILPAIILVLIALPSLRILYMTDEVNDPSLTIKSIGHQWYWTYEYTDYGGLIFNSYMLPPLFLEPGDLRLLDVDNRVVLPIEAPIRMMITSQDVLHSWAVPTLGLKTDAIPGRLNQTTFTATRPGVYYGQCSEICGANHSFMPIVLELIPLKIFEMGPVFTL	

Chemical graph structure	Amino acid sequence	Chemogenomic approach
Strategy: Chemically similar drugs are predicted tointeract with similar target proteins(Yamanishi et al, Bioinformatics, 2008; Faulon et al., Bioinformatics, 2008;
Jacob et al, Bioinformatics, 2008, Yabuuchi et al, Mol Sys Bio, 2011)

Chemical structure similarity for drugs
is evaluated by a graph kernel: (Mahe et al, J Chem Inf Model, 2005)(K x )ij = k x (x i , x j ) for i, j = 1, 2,..., nx

Genomic sequence similarity for target proteinsis evaluated by a string kernel: (Saigo et al, Bioinformatics, 2004)(K z )ij = kz (z i , z j ) for i, j = 1, 2,..., nz

Binary classification approachn Classification of drug-target pairs into theinteraction class or non-interaction class

Support Vector Machine (SVM) with pairwise kernels•Faulon et al., Bioinformatics, 24:225-233, 2008
•Jacob and Vert, Bioinformatics, 24:2149-2156, 2008
•Yabuuchi et al, Mol Sys Bio, 2011

SVM with pairwise kernels(pairwise SVM)
ordinary SVM :nf (x) = ∑ ai k(x i , x ") + b
i=1

where x : an object
pairwise SVM :n x ×n z

f (x,z) = ∑ ai k((x,z) i ,( x ", z")) + b
i=1

where (x,z) : a compound − protein pairSupervised bipartie graph inference
Chemical space

known drug
Compounds with similar structures
are close to each other

Interaction space

known interaction
Interacting drugs and targets
are connected on the graph

Genomic space

known target
Proteins with similar sequences
are close to each otherStep 1: embedding drugs and targets on thedknown graph into a unified feature space R
Chemical space

Interaction space

Genomic space€

known drug
Compounds with similar structures
are close to each otherknown interaction
Interacting drugs and targets
are close to each otherknown target
Proteins with similar sequences
are close to each otherStep 2. Learning a model between the chemical/
genomic space and the interaction space
Chemical space

Interaction spacefx€
known drug
Compounds with similar structures
are close to each other

Genomic spacefz€
known interaction
Interacting drugs and targets
are close to each otherknown target
Proteins with similar sequences
are close to each otherStep 3. Predicting unknown interactions involving
new compounds/proteins after the projection
Chemical space

Interaction spacefx€

Genomic spacefz€

new compound

new protein

New compounds are mappedwith fx

New proteins are mappedwith fz

Step 3. Predicting unknown interactions involving
new compounds/proteins after the projection
Chemical space

Interaction spacefx€

new compound
New compounds are mappedwith fx

Genomic spacefz€
predicted interaction
Connect compound-protein pairs
which are closer than a thresholdnew protein
New proteins are mappedwith fz

Mappting to a unified space	Let us consider two functions to map each compound x
and each protein z onto a unified Euclidian space#% f (x) = ( f (1) (x),, f (d ) (x))T ∈ Rdx
xx$%& f z (z) = ( f z(1) (z),, f z(d ) (z))T ∈ Rd
We find f x and fz which minimize
R( f x , fz ) =∑(xi ,z j )∈E( f x (x i ) − fz (z j ))2 + λ1 || f x ||2 +λ2 || fz ||2∑(xi ,z j )∈Vx ×Vz( f x (x i ) − fz (z j ))2

where Vx (resp. Vz ) is a set of drugs (resp. target proteins),
E is a set of interactions, and λ1 and λ2 are regularization parameters(Yamanishi, Adv Neural Inf Process Syst 21, 2009)Extraction of multiple features	
Succesive features f x(q ) and f z(q ) (q = 1,2,,d) are obtained by∑(x i ,z j )∈E ( f x (x i ) − f z (z j ))2 + λ1 || f x ||2 + λ2 || f z ||2" f x(q ) %$ (q ) ' = argmin# fz &∑(x ,z )∈V ×V ( f x (x i ) − f z (z j ))2
i

j

x

z

under the following orthogonality constraints :f x ⊥f x(1),, f x(q−1),

f z⊥f z(1),, f z(q−1)

Prediction score for a given pair of compound x" and protein z" :dg( x", z") = ∑ f x (q ) ( x")⋅ f z(q ) ( z")q =1

Algorithm 	
By the representer theorem, features can be expanded asnx

f x (x) = ∑α j kx (x j , x),
j=1nz

fz (z) = ∑ β j kz (z j , z)j=1

Kernel Gram matrices:(K x )ij = k x (x i , x j ), i, j = 1, 2,, nx(K z )ij = kz (z i , z j ), i, j = 1, 2,, nz

The solution can be obtained by finding α q and βq which minimizesT! α $ ! K x Dx K x −K x AK z $! α $
T
T
&#
##
&& #
&
+
λ
α
K
α
+
λ
β
Kzβ
1
x
2
T
#
&
" β % #" −K z A K x K z Dz K z &%" β %R(α, β ) =T! α $ ! K x2 0 $! α $
&#
##
&& #
&&
#
2
" β % #" 0 K z &%" β %under the following constraints:

α T K xα1 = = α T K xα q−1 = 0, β T K z β1 = = β T K z βq−1 = 0
where
Dx (resp. Dz ) : degree matrix of drugs (resp. target proteins),
A : adjacency matrix of drug-target interactions

It is reduced to the generalized eigenvalue problem:" 2" K D K +λ K
%"
%
−K
AK
x
z
$ x x x 1 x'$ α ' = ρ $ K x
T
$ β '
$ 0
$
'
−K
A
K
K
D
K
+
λ
K
#
&
x
x
z
z
z
2
z
#
&
#

%
0 '" α %$'2 '$ β 'K z &#&

Drug-target interaction data forhuman: Gold standard data
Statistics

Number of drugs

1874

Number of target proteins(Total in human genome)

436(23196)

Number of drug-target
interactions

6769

KEGG DRUG (December, 2011)

Cross-validation (CV)i) Pairwise CV(Missing interaction detection)ii) Blockwise CV I
(new drug identification)iii) Blockwise CV II(new target identification)i)Target protein
D
r
u
g

?

?

?

?
?

?

?

?

?
?

?
?

?
?
?

?

?

?
?

?

Target protein

ii)
D
r
u
g

iii)

D
r
u
g

Training set? Test set	
Target protein?
Training
set	

Test
set	

Performance evaluation by 3-fold crossvalidation: AUC (Area under the ROC curve)
CV
Pairwise CV(Missing interaction
detection)

Blockwise CV I
(New drug identification)

Blockwise CV II(New target identification)

MethodNearest neighbor
Pairwise SVM
Proposed method

AUC (S.D)
0.894 (0.009)
0.968 (0.006)
0.972 (0.007)

Nearest neighbor
Pairwise SVM
Proposed method

0.808 (0.010)
0.855 (0.013)
0.869 (0.006)

Nearest neighbor
Pairwise SVM
Proposed method

0.711 (0.009)
0.805 (0.003)
0.811 (0.009)

Computational cost

NN: nearest neighbor
P-SVM: pairwise SVM
DL: distance learning(proposed method)

Comprehensive prediction of
unknown drug-target interactions
Test drugs: all compounds in KEGG LIGAND and all
drugs in KEGG DRUGn Test target proteins: all human proteins in KEGG
GENESn All gold standard interaction data are used in the
training 	nSummaryn The proposed method can predict unknown

drug-target interactions on a large scale.

n The prediction is performed based on the

integration of chemical and genomic data.
n It does not need 3D structure information of

the target proteins.

Outlinen Background¨ Chemical, genomic and pharmacological spacesn Methods for pharmaceutical applications¨ Drug target prediction from chemical andgenomic data¨ Side-effect prediction from biological datan Resultsn Concluding remarks

Heterogeneous omics data	
Genomic spaceetc.	

Target proteinsetc.	

Drug chemical structures
Chemical spaceheadache, nausea,
dizziness, aortic stenosis,
cardiac infarction,
urinary retention
etc. 	
Phenotypic effects
Pharmacological space

Objective
Molecularinteraction	

Phenotypicinteraction	

Two interaction data	

Target Proteins	

Extraction of correlated setsof target proteins and side-effects

Set 1	

Drugs	

Set 2	

Mizutani et al, Bioinformatics, 28, i522-i528, 2012. (ECCB2012)

Side-effects	

Set 3	

Correlation analysis of target
proteins and side-effects
Target protein profile for each drug: x=(x1,x2 ,...,x p )T
indicating presence/absence of 1368 target proteins (DrugBank, Matador)
Side-effect profile for each drug: y=(y1,y2 ,...,yq )Tindicating presence/absence of 1339 side-effects (SIDER)

Goal: extraction of target proteins and side-effects
which share similar sets of drugs in terms of occurence.

Ordinary canonical correlation analysis(OCCA)

1For each drug, set two binary vectors indicating presence/absence of interactions;Target protein profile: x = [x1 , . . . , xp ]T ,2

Side-e↵ect profile: y = [y1 , . . . , yq ]TConsider two linear combinations for drug i with weight vectors ↵ andui = ↵ T x i
Find ↵ and

and

vi =

T

yi

( u and v are centered )that maximize the canonical correlation coefficient;
Pn
T
T
yi
i=1 ↵ xi ·qP
⇢ = corr(u, v) = pP
n
n
T
T
2
yi )2
i=1 (↵ xi )
i=1 (

;

Weight vectors in OCCA	

For each drug, set two binary vectors indicating presence/absence of interactions;
For each drug i, set two binary vectors indicating presence/absence of features;

Sparse canonical correlation analysis (SCCA)	

Target protein profile: x = [x1 , . . . , xp ]TT, Side-e↵ect profile: y = [y1 , . . . , yq ]T T
Target protein profile: x = [x1 , . . . , xp ] , Side-e↵ect profile: y = [y1 , . . . , yq ]

2

SCCA	
3

4

Consider two linear combinations for drug i with weight vectors ↵ and ;Consider two linear combinations for drug i with weight vectors ↵ and ;ui = ↵TT xi and vi = TTyi ( u and v are centered )ui = ↵ xi and vi =yi ( u and v are centered )Find ↵ and that maximize the canonical correlation coefficient;
Find ↵ and that maximize the canonical correlation coefficient;
Pn
T
T
↵
x
·
yi
i
Pn
i=1
T
T
q
⇢ = corr(u, v) = pP
↵
x
·
y
i
i
Pn
2
i=1
n q
T
T x )2
2 to ||↵||2  1,
⇢ = corr(u, v) = pP,
subject
(↵
(
y
)
i
i
i=1 Pni=1
nTT x )2
2
(↵
(
y
)
i
i
i=1
i=1
In order to impose sparsity on ↵ and we give an L1 penalty;
In order to impose sparsity on ↵ and we give an L1 penalty;pp||↵||1  c1 pp, || ||1  c2 pq,||↵||  c1 p, || ||1  c2 q,
0 < c1  1 and 0 < c2  1 are1 sparsity
parameters.
0 < c1  1 and 0 < c2  1 are sparsity parameters.
For n drugs, given X = [x1 , . . . , xn ]T , Y = [y1 , . . . , yn ]T , the weight vectors↵ and
can givenbe optimized
theFor
n drugs,
X = [x1by, . .solving. , xn ]T ,penalized
Y = [y1 ,matrix. . . , yndecomposition]T , the weight ofvectors
TmatrixY (Witten
et al., by
Biostatistics
2009). matrix decomposition of the↵and X canbe optimized
solving penalizedTmatrix X Y (Witten et al., Biostatistics 2009).
Given known xnew (protein profile), predict unknown ynew (side-e↵ect profile),
based onthe obtained
weightprofile),
vectorspredict{↵1 , . . .unknown, ↵m }, { y1new, . . . ,(side-e↵ectm }, and canonGivenknown
xnew (protein
profile),|| ||22  1

Weight vectors in SCCA	Extracted protein-effect association network
Relating
drug-protein interaction network with dru
across 80 components in
SCCA	
cataract

TNFA
PPARA

DRD2

rhabdomyolysis
flatulence

striaeSC6A2

skin atrophy

SC6A4

CCG1
cataract

CAC1B

CAC1F

AGTR1 ANGT

striae

CAC1D

SCN3B

CACB2

skin.atrophy
HMDH
muscle.cramps

PA24A

latent.diabetes
IRK1

ANXA1

pseudotumor.cerebri

VEGFA

CBG

SHBG

SCN7A

angioedema
eosinophilia

GCR
KCMA1

bronchospasm

ANDR
arthralgia
hirsutism

PA24A

hemolytic.anemia
LX12B

hepatitis

dermatitis
amenorrhea

ESR1erythema

Pharyngitis
rhinitisvertigotelangiectasia

LOXE3
LOX12

ANXA1

thrombocytopenia
osteoporosis
diabetes.mellitus diarrhea
gastroenteritis

PRGR
peptic.ulcer

GCR

erythema.multiforme

CBG

Associated drugs in CC15
Triamcinolone (DB00620)
Amcinonide (DB00288)

vaginal.hemorrhage
breast.pain
metrorrhagia
testicular.swelling
hypertension

A1AG2

syncopedelirium

dehydration

priapism

SCN3B
sweating
urinary.frequency
ACM2

tachycardia

ACM1

AOFB

Parkinson

lupus

ADA1A

SCN7A

tardive.dyskinesia
urinary.retention

fecal.impaction

albuminuria
sleep.disordernervousness
blurred.visionswelling weakness

ischemic.colitis
agitation

glossitis
dysuriaACH10

somnambulism

nocturia
hyperacusis
hyperesthesia
arrhythmia

ADA2B

GBRD
PSMD9

personality.disorderataxiaAV.blockhallucinations
increased.salivation

neutropenia
CP27B

urinary.urgency

RYR2
OPRK

PSA6

chest.pain
pain

phlebitis
diplopia

palpitations

leukocytosis
comanystagmus
vaginal.moniliasis

GBRB2

EF1B

GBRA1

SCN4A SCN4B

failure.to.thrive
SCNNG

OPRD

bilateral.hearing.loss
aspergillosis

GABR2rhinorrhea

cardiac.arrest
drug.dependence

SCNNB
CP51A
PTGIS

DB124
ACCN3
CP8B1

SCNND

GBRD

PRS6A

apraxia
acidosisloss.of.consciousness

CAC1A
CAC1G

CAC1H

ACES
SCN1B

hallucinations

ACCN4dysphagia

PSB6

KCNH2

pneumonia
weight.loss

Associated drugs in CC5
Diclofenac (DB00586)
Indomethacin (DB00328)
Leflunomide (DB01097)

GBRA2

Dyspepsia
CP2R1

apnea
PSME3
GBRR1

TOP2A

urinary.incontinence

spasm

congestive.heart.failure

ATP4A

bigeminy

stomatitis

memory.loss
ventricular.tachycardia

ADA1B

psychosis

GBRA6

PGH2

ADA2A

polyuria

GBRA2
GBRA1

GBRA5

PGH1

bradycardia

abnormal.gait
dry.mouth
paralysis
jaundice

RYR3

GBRA4

agranulocytosis

sore.throat

SCN5A
RYR1

OPRM
seizuresamnesiaheadachepulmonary.edema

elevated.blood.pressure
ADRB2
meningitis
angina.pectoris
ADRB1
hyponatremia

SIADH

anxietychills

ADA1D

flushing
hypoglycemia
cold.extremities blepharitis
dry.eyes
leukopenia
postural.hypotension
psoriasis ADRB3
hyperprolactinemia

tremorimpotence

vomiting
abdominal.pain hemorrhage

dysarthria

CAH1
DRD2

SC6A2
SC6A4

PGH2

TOP2A

DCK

insomnia

HRH1

agranulocytosis PGH1
stomatitis

neuropathy

LOX5

Associated drugs in CC1
Flecainide (DB01195)
Lamotrigine (DB00555)
Venlafaxine (DB00285)
Bupropion (DB01156)

DRD1
DRD3

Stevens...Johnson.syndrome
LOX5

TOP2B
TBB1

SC6A3
constitutional.symptoms

Stevens-Johnson syndrome

laryngitis
wheezing

thrombophlebitis
constipationskin.ulcer ACHA6

shock

TYSY

TTHY

CAH4

DRD5

urticaria

GBRA3

hyperuricemia

ACE

LKHA4
TTHY

NMD3A

convulsions
DRD4

S12A1

hepatic.failure

connective.tissue.disordersPPIA
cyst
A1AG1

impotence tremor

S12A3

CAC1C
eczema hypercholesterolemia
tinnitus
hypothyroidism

anemiapancreatitis
myocardial.infarction
alopecia

ACHA2

hypercalcemia
ulcermelenaCAH2

somnolence

numbness
pancytopenia
breast.enlargement
proteinuria
nephrotic.syndrome

heartburnglaucomarectal.hemorrhage

hyperglycemia

HRH4
HRH3

purpuraESR2

urinary.retention

photosensitivity
pemphigus
renal.insufficiency

infection

increased salivationarachnoiditis

CAC1I

CHLE
amblyopia
ecchymosis

GBRB2

apnea
TSPOA

atrial.fibrillationaphasia

GBRR2

Associated drugs in CC2
Midazolam (DB00683)
Baclofen (DB00181)
Gabapentin (DB00996)

GBRR1

stration of the network of extracted targeted proteins and side-effects in the obtained 80 canonical components (CCsside-effects (green diamonds) are connected if they appear in the same canonical component.
The highlighted
CCs, 1 (red),Protein-effectassociation
Protein
Side-effect
5 (purple) are
discussed in 5. CC1: DRD2 (Dopamine
D2 receptor), SC6A2 (Sodium-dependent
noradrenalinetransporter), S
in the same
component
onin transporter), SCNs (Sodium channel protein subunits); CC2: GBRs (Gamma-aminobutyric acid receptor subunits)

Biological evaluation of extracted components	
n Evaluation of the components by pathway

enrichment analysis¨ How each of the correlated sets is enriched withproteins that are involved in a specific pathway?
n Pathway databases¨ KEGG : pathway maps
¨ GO (Gene Ontology) : biological process terms	

Pathway enrichment of proteins in components	
Frequency of components v.s. Number of enriched pathways	

# Components	

KEGG pathways
20

GO Biological process
20
15

10

10
5

0

0
1 2 3 4 5 6 7 8 9 1011121314151617181920

1 2 3 4 5 6 7 8 9 1011121314151617181920# Enriched pathways	

The components were enriched with a small number of pathways.	

Molecular function enrichment of proteins incomponents	

# Components	

Frequency of components v.s. molecular functions	
KEGG BRITE terms

GO Molecular function

20

20

15

15

10

10

5

5

0

0
1 2 3 4 5 6 7 8 9 1011121314151617181920

1 2 3 4 5 6 7 8 9 1011121314151617181920# Enriched molecular functions	The components were enriched with a small number of pathways, but withvarious number of different molecular functions.	

Biologically relevant interpretation of correlated sets	

Interactions between
drugs with proteins ofdifferent molecularfunctions tend to
induce the regulationof the whole protein
pathway, which leadto certain side-effects.	

ligands	

receptor	
channel	cell	Component	cytosolic proteins 	An example of extracted components	
U)J(%&'$()""(#(%&'$();G<>9:@B:3B040
C3<F?325E2

Flecainide	..R+%&'$()
E<F<;<EF.(.+K%&'$().(.+J%&'$()(RU#+%&'$()()RU%&'$()

0F;4<2.(.+I%&'$().(.KA%&'$()

0=458<F;:7GB&$I&%&'$()"(AQ(%&'$()

3<F25F894<>2F20
H#[+%&'$()()Y(+%&'$()

702?9:F?@:;8E2;2>;4[.$(+%&'$()@?0E328E;<@70
ZTRJ(%&'$()<564:292@<

R.#%&'$().KR%&'$()-&KR%&'$()

2:045:7G434<<;FG;<364<
G27<F4F40

92;@<F4F40

G2@:3BF4E8<52@4<<@25:;;G2<
T-#+%&'$()XY+AK%&'$() 2;BFG2@<

X!Y+A%&'$()

FG;:@>:EBF:7254<:0F2:7:;:040
94<>2F208@2334F?0 94<;;G2<
6<0F;:25F2;4F40"#R#%&'$()
727F4E8?3E2;

7G:F:02504F4M4FB
72@7G46?0;25<38450?CC4E425EB

T-#A%&'$()

5?@>5200
7<5EBF:7254<>;2<0F8253<;62@25F
7;:F245?;4<527G;:F4E80B59;:@2<52@4<
7<5E;2<F4F40@B:E<;94<3845C<;EF4:5(.&(A%&'$()<3:72E4<
G27<F4E8C<43?;2

GB72;E<3E2@4<?3E2;@2325<

E:552EF4M28F400?28940:;92;0""H(%&'$()
EB0F(+(R+%&'$()

X[&(Q%&'$()
UU&*%&'$()(+(RA%&'$()

0B5E:72

92GB9;<F4:5-F2M250888]:G50:580B59;:@2

0G:E=
U!"AK%&'$()#*#+%&'$()<@5204<

RK#(A%&'$()
RK#(+%&'$()

RK#(P%&'$()<752<"-$T,%&'$()>;<9BE<;94<<>5:;@<386<4F
9;B8@:?FG
7<;<3B040
O<?594E2
5:EF?;4<
GB72;<E?040
GB72;20FG204<<;;GBFG@4<70BEG:040

63:004F40
9B0?;4<"-(P%&'$()(I(A(%&'$()

7:3B?;4<@2@:;B83:00
M25F;4E?3<;8F<EGBE<;94<(I(+K%&'$()

07<0@?;45<;B845E:5F4525E2(I(AK%&'$()

52?F;:7254<."ALK%&'$()?;45<;B8?;625EB!"#[%&'$()!"#I%&'$()
R(K#A%&'$()

9B07G<64<

IK+AQ%&'$()(..),%&'$()[.)&A%&'$()

752?@:54<
1246GF83:00<7;<N4<.(.+(%&'$().(.+R%&'$()

Dopamine
receptor	

(..)Q%&'$()-.))I%&'$()"#-P(%&'$()(.T-%&'$()-.)+K%&'$().&XT%&'$()

Sodiumchannelproteins	-.)Q(%&'$()-.)QK%&'$()-.))R%&'$()-.))K%&'$()."/+(%&'$() ."\K+%&'$()"URH-%&'$()"-KP%&'$()

Serotonin
noradrenalintransporters 	

TJ+K%&'$()

C<43?;28F:8FG;4M2

E<;94<E8<;;20F
9;?6892725925E2

EG20F87<45
7<45

7G32>4F40
9473:74<;G45:;;G2<

7<374F<F4:50

32?=:EBF:040
E:@<

5B0F<6@?0
M<645<38@:5434<040#*#A%&'$()

RK##+%&'$()>43<F2;<38G2<;45683:00<072;6433:040

0:;28FG;:<F

72;0:5<34FB8940:;92;<F<N4<(Z8>3:E=

IB072704<."A#+%&'$()

RK#KA%&'$()>462@45B(I(+I%&'$()
G2<9<EG2
7?3@:5<;B8292@<

232M<F298>3::987;200?;2(I#KA%&'$()@254564F40<5645<872EF:;40(I#K+%&'$()
GB7:5<F;2@4<

E:5620F4M28G2<;F8C<43?;2(U"Q(%&'$()

G<33?E45<F4:50
RK#I%&'$()
45E;2<02980<34M<F4:5"-$IS%&'$()

C3?0G456
GB7:63BE2@4<
E:3982NF;2@4F420 >327G<;4F40
9;B82B20
32?=:7254<
7:0F?;<38GB7:F2504:5
70:;4<040 (I#K,%&'$()
GB72;7;:3<EF452@4<

Component 1	
#*#,%&'$()(.&+V%&'$()

RK#(/%&'$()

F;2@:;

40EG2@4E8E:34F40<64F<F4:5

024W?;20

RK#(Q%&'$()(I(+(%&'$()
F<;94M289B0=45204<?;45<;B8;2F25F4:5

4@7:F25E2-.)/(%&'$()!"#$%&'$()

RK#(,%&'$()"<;=450:5-.)L(%&'$()-H(I&<5N42FBEG4330.(&+%&'$()

7;4<740@

3?7?0(!JK%&'$()(.$+%&'$()
C2E<384@7<EF4:5(.$A%&'$()

M:@4F456<>9:@45<387<45 G2@:;;G<62

9B0<;FG;4<

I#I+%&'$()

I#IA%&'$()<3>?@45?;4<032278940:;92;52;M:?05200>3?;;298M404:501233456 12<=5200

52?;:7<FGB

0:@5<@>?340@-+A(+%&'$()

3<;B564F40
1G22W456-.P(A%&'$()-.P(Q%&'$()

012<F456?;45<;B8C;2D?25EB.(&Q%&'$().(.+.%&'$()
2EW2@< GB72;EG:320F2;:32@4<F4554F?0
GB7:FGB;:4940@-.),K%&'$()

U!"A(%&'$()-.P(,%&'$()F<EGBE<;94<
E:50F4F?F4:5<380B@7F:@0

U*-*%&'$()

GB72;?;4E2@4<(.T%&'$()

I#I,%&'$()
FG;:@>:7G32>4F40
E:50F47<F4:5
0=458?3E2; (.&(P%&'$()
I#I/%&'$()
9234;4?@450:@54<"R&A%&'$()

0F:@<F4F40

UKK+%&'$()
I.[%&'$()

I#IQ%&'$()

Carbamazepine	
 Lamotrigine	-+A(,%&'$()

E:5M?304:50&#&+%&'$()

X!Y/%&'$()<6;<5?3:EBF:040 "R&+%&'$()?;F4E<;4<)$I,(%&'$().(&A%&'$()&#&,%&'$()
M<645<38G2@:;;G<62>;2<0F87<45@2F;:;;G<64<
F20F4E?3<;801233456
GB72;F2504:5
0:@5:325E2

7?;7?;<

G2<;F>?;5
63<?E:@<;2EF<38G2@:;;G<62

GB72;63BE2@4<&#&Q%&'$()

45C2EF4:5

2;BFG2@<8@?3F4C:;@2

Ropinirole	
 Pramipexole	

"G<;B564F40;G454F40
M2;F46:

F23<5642EF<04<

X!YT,%&'$()

Bupropion	
 Venlafaxine	

>;:5EG:07<0@()I#%&'$()
G4;0?F40@<E49:040
3:008:C8E:50E4:?05200.(.+&%&'$().(.+H%&'$()<;<EG5:494F40

Neurotransmitting roles	

<@>3B:74<
2EEGB@:040

U-"!(%&'$()
RK##A%&'$()<F;4<38C4>;433<F4:5<7G<04<

Tremor
Impotence
Urinary retention	

An example of extracted components	
anti-anxiety, anesthesia adjuvants, …	

U)J(%&'$()""(#(%&'$();G<>9:@B:3B040
C3<F?325E2..R+%&'$()
E<F<;<EF.(.+K%&'$().(.+J%&'$()(RU#+%&'$()()RU%&'$()

0F;4<2.(.+I%&'$().(.KA%&'$()

0=458<F;:7GB&$I&%&'$()"(AQ(%&'$()

3<F25F894<>2F20
H#[+%&'$()()Y(+%&'$()

702?9:F?@:;8E2;2>;4[.$(+%&'$()@?0E328E;<@70
ZTRJ(%&'$()<564:292@<

R.#%&'$().KR%&'$()-&KR%&'$()

2:045:7G434<>;:5EG:07<0@()I#%&'$()<;FG;<364<

G4;0?F40@

G27<F4F40

92;@<F4F40

G2@:3BF4E8<52@4<<@25:;;G2<
T-#+%&'$()XY+AK%&'$() 2;BFG2@<"G<;B564F40;G454F40
M2;F46:

F23<5642EF<04<

X!YT,%&'$()
X!Y+A%&'$()

FG;:@>:EBF:7254<:0F2:7:;:040
94<>2F208@2334F?0 94<;;G2<
6<0F;:25F2;4F40"#R#%&'$()
727F4E8?3E2;

7G:F:02504F4M4FB
72@7G46?0;25<38450?CC4E425EB

2;BFG2@<8@?3F4C:;@2@2325<

E:552EF4M28F400?28940:;92;0""H(%&'$()
EB0F(+(R+%&'$()

X[&(Q%&'$()
UU&*%&'$()(+(RA%&'$()

0B5E:72

0G:E=
U!"AK%&'$()

012<F456?;45<;B8C;2D?25EB

7;4<740@"<;=450:5

3?7?0(I(+(%&'$()-.)L(%&'$()

F<;94M289B0=45204<?;45<;B8;2F25F4:5
F;2@:;

4@7:F25E2

C3?0G456
GB7:63BE2@4<
E:3982NF;2@4F420 >327G<;4F40
9;B82B20
32?=:7254<
7:0F?;<38GB7:F2504:5
70:;4<040 (I#K,%&'$()
GB72;7;:3<EF452@4<
232M<F298>3::987;200?;2(I#KA%&'$()@254564F40<5645<872EF:;40(I#K+%&'$()
GB7:5<F;2@4<-H(I&<5N42FBEG4330.(&+%&'$()

I#IA%&'$()(!JK%&'$()(.$+%&'$()
C2E<384@7<EF4:5(.$A%&'$()

M:@4F456<>9:@45<387<45 G2@:;;G<62-.)/(%&'$()!"#$%&'$()#*#+%&'$()>;<9BE<;94<<>5:;@<386<4F
9;B8@:?FG
7<;<3B040
O<?594E2#*#,%&'$()

63:004F40
9B0?;4<(.&+V%&'$()(I(A(%&'$()

7:3B?;4<0:;28FG;:<F@2@:;B83:00
M25F;4E?3<;8F<EGBE<;94<(I(+K%&'$()

5:EF?;4<
GB72;<E?040
GB72;20FG204<<;;GBFG@4<?;45<;B845E:5F4525E2

07<0@70BEG:040<@5204<(I(+I%&'$()
G2<9<EG2
7?3@:5<;B8292@<

40EG2@4E8E:34F40<64F<F4:5

024W?;20

0:@5<@>?340@
RK#(,%&'$()

I#I+%&'$()-.P(A%&'$()-.P(Q%&'$()<3>?@45?;4<032278940:;92;52;M:?05200>3?;;298M404:501233456 12<=5200

52?;:7<FGB

9B0<;FG;4<

3<;B564F40
1G22W456-.),K%&'$()

U!"A(%&'$()-.P(,%&'$()F<EGBE<;94<
E:50F4F?F4:5<380B@7F:@0

U*-*%&'$().(&Q%&'$()

I#I,%&'$()
FG;:@>:7G32>4F40
E:50F47<F4:5
0=458?3E2; (.&(P%&'$()
I#I/%&'$()
9234;4?@450:@54<"R&A%&'$()

0F:@<F4F40

UKK+%&'$()
I.[%&'$()

GB72;?;4E2@4<(.T%&'$().(.+.%&'$()
2EW2@< GB72;EG:320F2;:32@4<F4554F?0
GB7:FGB;:4940@&#&+%&'$()

X!Y/%&'$()<6;<5?3:EBF:040 "R&+%&'$()?;F4E<;4<)$I,(%&'$()

92GB9;<F4:5-F2M250888]:G50:580B59;:@2(I(AK%&'$()

RK#(A%&'$()

RK#(Q%&'$()

RK#(+%&'$()

RK#(/%&'$()

E:5620F4M28G2<;F8C<43?;2(U"Q(%&'$()

72;0:5<34FB8940:;92;<F<N4<(Z8>3:E=

G<33?E45<F4:50
RK#I%&'$()
45E;2<02980<34M<F4:5

IB072704<."A#+%&'$()"-$IS%&'$()
RK#(P%&'$()<752<"-$T,%&'$()

52?F;:7254<."ALK%&'$()?;45<;B8?;625EB#*#A%&'$()

RK##+%&'$()"-(P%&'$()!"#[%&'$()!"#I%&'$()
R(K#A%&'$();G45:;;G2<

TJ+K%&'$()-.)Q(%&'$()-.)QK%&'$()-.))R%&'$()-.))K%&'$()IK+AQ%&'$()(..),%&'$()."/+(%&'$() ."\K+%&'$()"URH-%&'$()

E<;94<E8<;;20F
9;?6892725925E2

Component 2	
9B07G<64<"-KP%&'$()

EG20F87<45
7<45

7G32>4F40
9473:74<

C<43?;28F:8FG;4M2>43<F2;<38G2<;45683:00<072;6433:040

7<374F<F4:50

32?=:EBF:040
E:@<

5B0F<6@?0
M<645<38@:5434<040

RK#KA%&'$()>462@45B

Gabapentin	

-+A(+%&'$()<52@4<
7<5E;2<F4F40@B:E<;94<3845C<;EF4:5(.&(A%&'$()<3:72E4<
G27<F4E8C<43?;2

GB72;E<3E2@4<?3E2;

E:5M?304:50
I#IQ%&'$()

Baclofen	-+A(,%&'$()

0:@5:325E2

5?@>5200
7<5EBF:7254<>;2<0F8253<;62@25F
7;:F245?;4<527G;:F4E80B59;:@2

G2<;F>?;5
63<?E:@<;2EF<38G2@:;;G<62.(&A%&'$()&#&,%&'$()
M<645<38G2@:;;G<62>;2<0F87<45@2F;:;;G<64<
F20F4E?3<;801233456
GB72;F2504:5

7?;7?;<

T-#A%&'$()Midazolam	

GB72;63BE2@4<&#&Q%&'$()

45C2EF4:5

GABA
receptor
subunits 	

[.)&A%&'$()(..)Q%&'$()-.))I%&'$()"#-P(%&'$()

752?@:54<
1246GF83:00.(.+(%&'$().(.+R%&'$()(.T-%&'$()-.)+K%&'$().&XT%&'$()<@>3B:74<
2EEGB@:040<7;<N4<<E49:040
3:008:C8E:50E4:?05200.(.+&%&'$().(.+H%&'$()<;<EG5:494F40

GABA receptor	

U-"!(%&'$()
RK##A%&'$()<F;4<38C4>;433<F4:5<7G<04<

Increased salivation
Hallucinations
apnea	

An example of extracted components	
U)J(%&'$()""(#(%&'$();G<>9:@B:3B040
C3<F?325E2

Component 5	

Component 15	

Anti-inflammatory	Anti-inflammatory	

Fenoprofen	
 Ketoprofen	

Triamcinolone	
 Dexamethazone	

..R+%&'$()
E<F<;<EF.(.+K%&'$().(.+J%&'$()(RU#+%&'$()()RU%&'$()

0F;4<2.(.+I%&'$().(.KA%&'$()

0=458<F;:7GB&$I&%&'$()"(AQ(%&'$()

3<F25F894<>2F20
H#[+%&'$()()Y(+%&'$()

702?9:F?@:;8E2;2>;4[.$(+%&'$()@?0E328E;<@70
ZTRJ(%&'$()<564:292@<

R.#%&'$().KR%&'$()-&KR%&'$()

2:045:7G434<>;:5EG:07<0@()I#%&'$()<;FG;<364<

G4;0?F40@

G27<F4F40

92;@<F4F40

G2@:3BF4E8<52@4<

Component 15	
<@25:;;G2<
T-#+%&'$()XY+AK%&'$() 2;BFG2@<"G<;B564F40;G454F40
M2;F46:

F23<5642EF<04<

X!YT,%&'$()

X!Y+A%&'$()

FG;:@>:EBF:7254<:0F2:7:;:040
94<>2F208@2334F?0 94<;;G2<
6<0F;:25F2;4F40"#R#%&'$()

727F4E8?3E2;

72@7G46?0;25<38450?CC4E425EB

2;BFG2@<8@?3F4C:;@2
T-#A%&'$()

5?@>5200
7<5EBF:7254<>;2<0F8253<;62@25F
7;:F245?;4<527G;:F4E80B59;:@2<52@4<
7<5E;2<F4F40@B:E<;94<3845C<;EF4:5(.&(A%&'$()<3:72E4<
G27<F4E8C<43?;2

GB72;E<3E2@4<?3E2;@2325<

E:552EF4M28F400?28940:;92;0""H(%&'$()
EB0F(+(R+%&'$()

X[&(Q%&'$()
UU&*%&'$()(+(RA%&'$()

0B5E:72

92GB9;<F4:5-F2M250888]:G50:580B59;:@2

0G:E=
U!"AK%&'$()-+A(+%&'$()

3<;B564F40
1G22W456

7;4<740@"<;=450:5

3?7?0(I(+(%&'$()-.)L(%&'$()

F<;94M289B0=45204<(!JK%&'$()(.$+%&'$()
C2E<384@7<EF4:5(.$A%&'$()?;45<;B8;2F25F4:5
F;2@:;

4@7:F25E2

C3?0G456
GB7:63BE2@4<
E:3982NF;2@4F420 >327G<;4F40
9;B82B20
32?=:7254<
7:0F?;<38GB7:F2504:5
70:;4<040 (I#K,%&'$()
GB72;7;:3<EF452@4<

Component 5	

232M<F298>3::987;200?;2(I#KA%&'$()@254564F40<5645<872EF:;40(I#K+%&'$()
GB7:5<F;2@4<-H(I&M:@4F456<>9:@45<387<45 G2@:;;G<62<5N42FBEG4330-.)/(%&'$()!"#$%&'$()#*#+%&'$()<64F<F4:5#*#,%&'$()

63:004F40
9B0?;4<(.&+V%&'$()

0:@5<@>?340@(I(A(%&'$()

7:3B?;4<0:;28FG;:<F@2@:;B83:00
M25F;4E?3<;8F<EGBE<;94<(I(+K%&'$()

5:EF?;4<
GB72;<E?040
GB72;20FG204<<;;GBFG@4<?;45<;B845E:5F4525E2

07<0@70BEG:040<@5204<(I(+I%&'$()
G2<9<EG2
7?3@:5<;B8292@<

40EG2@4E8E:34F40>;<9BE<;94<<>5:;@<386<4F
9;B8@:?FG

7<;<3B040
O<?594E2

024W?;20

9B0<;FG;4<.(&+%&'$()

I#IA%&'$()<3>?@45?;4<032278940:;92;52;M:?05200>3?;;298M404:501233456 12<=5200

52?;:7<FGB

I#I+%&'$()-.P(A%&'$()-.P(Q%&'$()

012<F456?;45<;B8C;2D?25EB.(&Q%&'$().(.+.%&'$()
2EW2@< GB72;EG:320F2;:32@4<F4554F?0
GB7:FGB;:4940@-.),K%&'$()

U!"A(%&'$()-.P(,%&'$()F<EGBE<;94<
E:50F4F?F4:5<380B@7F:@0

U*-*%&'$()

RK#(,%&'$()

I#IQ%&'$()

GB72;?;4E2@4<(.T%&'$()

I#I,%&'$()
FG;:@>:7G32>4F40
E:50F47<F4:5
0=458?3E2; (.&(P%&'$()
I#I/%&'$()
9234;4?@450:@54<"R&A%&'$()

0F:@<F4F40

UKK+%&'$()
I.[%&'$()-+A(,%&'$()

E:5M?304:50&#&+%&'$()

X!Y/%&'$()<6;<5?3:EBF:040 "R&+%&'$()?;F4E<;4<)$I,(%&'$()

GB72;63BE2@4<.(&A%&'$()&#&,%&'$()

M<645<38G2@:;;G<62>;2<0F87<45@2F;:;;G<64<
F20F4E?3<;801233456
GB72;F2504:5
0:@5:325E2

7?;7?;<

G2<;F>?;5
63<?E:@<;2EF<38G2@:;;G<62

7G:F:02504F4M4FB&#&Q%&'$()

45C2EF4:5(I(AK%&'$()

RK#(A%&'$()

RK#(Q%&'$()

RK#(+%&'$()

RK#(/%&'$()

E:5620F4M28G2<;F8C<43?;2(U"Q(%&'$()

72;0:5<34FB8940:;92;<F<N4<(Z8>3:E=

G<33?E45<F4:50
RK#I%&'$()
45E;2<02980<34M<F4:5

IB072704<."A#+%&'$()"-$IS%&'$()
RK#(P%&'$()<752<"-$T,%&'$()

52?F;:7254<."ALK%&'$()?;45<;B8?;625EB#*#A%&'$()

RK##+%&'$()"-(P%&'$()!"#[%&'$()!"#I%&'$()
R(K#A%&'$();G45:;;G2<

TJ+K%&'$()-.)Q(%&'$()-.)QK%&'$()-.))R%&'$()-.))K%&'$()IK+AQ%&'$()(..),%&'$()."/+(%&'$() ."\K+%&'$()"URH-%&'$()

E<;94<E8<;;20F
9;?6892725925E2

9B07G<64<"-KP%&'$()

EG20F87<45
7<45

7G32>4F40
9473:74<

C<43?;28F:8FG;4M2>43<F2;<38G2<;45683:00<072;6433:040

7<374F<F4:50

32?=:EBF:040
E:@<

5B0F<6@?0
M<645<38@:5434<040

RK#KA%&'$()>462@45B[.)&A%&'$()(..)Q%&'$()-.))I%&'$()"#-P(%&'$()

752?@:54<
1246GF83:00<7;<N4<.(.+(%&'$().(.+R%&'$()(.T-%&'$()-.)+K%&'$().&XT%&'$()<@>3B:74<
2EEGB@:040

Prostaglandin G/H synthase 1&2
Arachidonate 5-lipoxygenase<E49:040
3:008:C8E:50E4:?05200.(.+&%&'$().(.+H%&'$()<;<EG5:494F40

Arachidonic acidmetabolism 	Annexin 1A
Glucocordicoid receptor
corticosteroid-binging
globulin
phospholipase A2 	Glucocorticoid effects

U-"!(%&'$()
RK##A%&'$()<F;4<38C4>;433<F4:5<7G<04<

Stevens-Johnson syndrome
Stomatitis
Agranulocytosis

StriaeSkin atrophy
CataractPerformance evaluation for side-effectprediction by 5-fold cross-validation	
Relating drug-protein interaction network with drug side-effects
Prediction score :y new = B −T AT x new
Tablewhere
3. Performance
evaluation
based on 5-fold cross-validation.
A = [α1,..., αm ], B = [ β1,..., βm ], m : number of components
MethodChemical structure based approach:RandomOCCA
SCCA
Target protein based approach:RandomOCCA
SCCA

AUC ± S.D.

AUPR ± S.D.

0.5000 ± 0.0000
0.8355 ± 0.0010
0.8708 ± 0.0007

0.0556 ± 0.0000
0.3753 ± 0.0016
0.3766 ± 0.0030

0.5000 ± 0.0000
0.8850 ± 0.0007
0.8895 ± 0.0002

0.0556 ± 0.0000
0.4067 ± 0.0006
0.4103 ± 0.0018

Examples of predicted side-effects by SCCA	
• For 730 uncharacterized drugs in DrugBank	
Top 20 predicted side-effects	
Drug	

Score	

Drug	

tremor	1.176339	

11	
 Benzocaine	diarrhea	

0.937409	

2	
 Benzocaine	 diplopia	

1.163882	

12	
 Nisoldipine	

tremor	0.933258	

3	
 Cinnarizine	constipation	
 1.143190	

13	
 Nitrendipine	

tremor	0.933258	

4	
 Bepridil	

tremor	1.046472	

14	
 Lercanidipine	
 tremor	

0.933258	

5	
 Cinnarizine	somnolence	

0.996260	

15	
 Benzocaine	syncope	

0.928905	

6	
 Cinnarizine	

dry.mouth	

0.961833	

16	
 Promazine	tachycardia	

0.926875	

7	
 Cinnarizine	

angioedema	

0.955471	

17	
 Promazine	somnolence	

0.924103	

8	
 Cinnarizine	

insomnia	

0.950757	

18	
 Benzocaine	

vomiting	

0.922091	

9	
 Benzocaine	 nausea	

0.947105	

19	
 Bepridil	

nervousness	

0.918828	

0.943918	

20	
 Cinnarizine	

nervousness	

0.918615	

1	
 Cinnarizine	

10	
 Alprenolol	

Side-effect	dizziness	

Side-effect	• Side-effects reported in the literature are colored in red.	

Score	

Summary	n The proposed method enables us to relatetarget proteins with side-effects of drugs.
n It may be useful to predict unknown side-

effects in the drug development.

Machine learning in chemoinformatics

Lodhi, H. and Yamanishi, Y.,
Chemoinformatics and Advanced Machine Learning Perspectives,
IGI Global, 2010.

Acknowledgementsn

Kyushu University¨ Mikita Suyama, Tetsuya Saton

Kyushu Institute of Technology¨ Hiroto Saigon

Kyoto University¨ Minoru Kanehisa, Susumu Goto, Masaaki Kotera, Masataka

Takarabe, Sayaka Mizutani, Yosuke NishimuranJST¨ Yasuo Tabein

Curie Institute - Inserm U900 - Mines ParisTech¨ Jean-Philippe Vert, Véronique Stoven, Kevin Bleakley, Edouard

Pauwels