=Paper=
{{Paper
|id=Vol-1636/paper-06
|storemode=property
|title=Extracting the Common Structure of Compounds to Induce Plant Immunity Activation using ILP
|pdfUrl=https://ceur-ws.org/Vol-1636/paper-06.pdf
|volume=Vol-1636
|authors=Atsushi Matsumoto,Katsutoshi Kanamori,Kazuyuki Kuchitsu,Hayato Ohwada
|dblpUrl=https://dblp.org/rec/conf/ilp/MatsumotoKKO15
}}
==Extracting the Common Structure of Compounds to Induce Plant Immunity Activation using ILP==
https://ceur-ws.org/Vol-1636/paper-06.pdf
Extracting the Common Structure of Compounds to
Induce Plant Immunity Activation using ILP
Atsushi matsumoto,1, 2 Katsutoshi Kanamori,1 Kazuyuki Kuchitsu,3
and Hayato Ohwada1
1. Department of Industrial Administration, Faculty of Science and Technology,
Tokyo University of Science
2. 7415617@ed.tus.ac.jp
3. Department of Applied Biological Science, Faculty of Science and Technology,
Tokyo University of Science
Abstract. While recent studies have referred to plant immunity activators, it is
difficult to find a compound to use for the immunity activation of plants. In this
study, we seek to determine compounds that enable plant immunity activity using
ILP. With the proposed method, it is possible to predict compounds that induce
plant immunity activity, based on the structural features of the compounds. The
predicted structure rule also includes structures of known plant immunity activa-
tors. However, further investigation is needed regarding the relationship between
plant immunity and structure rules.
Keywords: ILP, Machine learning, Plant immunity activation, Virtual screening
1. INTRODUCTION
Virtual screening is an important approach in the drug discovery process. Especial-
ly, machine learning has recently received broad attention. This paper picks up two
method, Support Vector Machine (SVM) [1] and Inductive Logic Programming
(ILP). Both method are often used in drug discovery field [2], [3].
On the other hand, decreased production of agricultural crops due to pathogenic
bacteria and pests is a serious problem that has not yet been solved. To address this
problem, grower have made a deal with fungicides and pesticides, however, it is
difficult to act selectively on the target (e.g., pests and pathogens). There is a pos-
sibility that the cause of health damage in humans and destruction of biota. In addi-
tion, long-term use of the same drug may cause the emergence of resistant bacteria;
thus, the effect of the drug gradually decreases. In recent years, plant immunity ac-
tivators have attracted attention, based on the idea of increasing the immunity of
the plant rather than directly killing pathogens and pests. However, only three
types of plant immunity activator are currently marketed in Japan (Fig. 1). In addi-
tion, the mechanism of plant-immunity activation is still largely unknown [4].
69
Fig. 1. Known plant-immunity activators
The development of plant immunity activators has been slow, due to the time re-
quired and the high cost of screening candidate compounds. Cause of this problem
is the kind of candidate compounds is enormous and each of the compounds were
reacted to the cells to confirm the effect of immunity activation.
In this study, we predict compounds that induce plant-immunity activation using
ILP to study compound structures. ILP can be used to determine relationship pat-
terns between data; therefore, it is suitable to represent the structure of compounds.
Additionally, we obtained the structure of the predicted compound as a rule, which
is one of the excellent points of ILP. A recent study that was conducted to predict
the structure of compounds using ILP exhibited high performance [5]. In those
cases, the target of compound bonds was known. However, in the present study,
the target of compound bonds is not known. Additionally, we also tried SVM for
comparison with ILP. SVM also exhibited high performance [2].
2. PLANT IMMUNITY
Plant immunity is a defense system to protect plants from various enemies. A
plant-immunity activator is a drug that activates plant immunity. The Kuchitu
group constructed a screening system to find a candidate using the amount of ROS
(reactive oxygen species) generation as an index [6]. Experiment results indi-
cated that if the ROS value is high, the compound is likely to be a plant-immunity
activator.
3. DATASET
In the present study, the datasets are experiment data about the plant immunity activa-
tor in Arabidopsis thaliana, compiled by the Kuchitu group. This dataset includes
10000 compounds. Positive examples are 271 high-ROS compounds, and negative
examples are the other 9729 compounds. However, negative examples were reduced
to 813 compounds by random sampling for two reasons. First, imbalanced data dete-
riorates learning accuracy. Second, if there are many compounds, calculation takes a
long time. Therefore, 1084 compounds were used in this study.
70
4. METHOD
This chapter describes our method. We had two approaches. Fig. 2 shows the over-
view of our method.
Fig. 2. Method overview
The two approaches are described as follow.
4.1 ILP Approach
With the ILP approach, structural features and some numerical features of the com-
pound were used as background knowledge. In this study, we used GKS [7], which
is an ILP system. We defined seven predicates to represent the features of the com-
pounds. In parentheses, there are argument of predicates.
・atom (compound_name, atom_id, element)
Types of atoms present in the compound
・bond (compound_name, atom_id, atom_id, bondtype)
Bonding state between atoms and bond type in the compound
・Num_AromaticRings (compound_name,Num_AromaticRing)
The number of aromatic rings in the compound
・Num_Rings (compound_name, Num_Ring)
The number of rings in the compound
・LogP98 (compound_name, value)
Lipid solubility of the compound
・LogD (compound_name, value)
Indication of a change in lipid solubility by a change in Ph value
71
・ring (compound_name,ring_id,atom_id,ringsize,ringtype)
Type of ring structure that is composed of each atom. It can represent the
connection of the ring structure and other structures by using this predicate.
By selecting several predicates as background knowledge, we can obtain the structure
of the compound as a learning result (Table 1). Background knowledge is a set of
atomic formulas of each predicate. Atom and bond are always necessary. The reason
why selecting LogP98 and LogD is result of importance calculation using the average
Gini coefficient.
Table 1. Predicates selected for background knowledge
Setting name Predicate
ILP1 atom,bond
ILP2 atom,bond,Num_AromaticRings
ILP3 atom,bond,Num_AromaticRings,Num_rings
ILP4 atom,bond,ALogP98
ILP5 atom,bond,Num_AromaticRings,Num_rings,ALogP98,LogD
ILP6 atom,bond,Num_AromaticRings,Num_rings,LogD
ILP7 atom,bond,LogD,ring
ILP8 atom,bond,ring
Mode declaration as input is shown in Fig. 3. A rule selected if it was covered more
than 10 positive examples and less than 10 negative examples.
@dock,+molecular
@atom,+molecular,+atomid,#atomtype
@atom,+molecular,-atomid,#atomtype
@bond,+molecular,+atomid,+atomid,#bondtype
@bond,+molecular,-atomid,+atomid,#bondtype
@bond,+molecular,+atomid,-atomid,#bondtype
@bond,+molecular,-atomid,-atomid,#bondtype
@Num_Rings,+molecular,#Num_Ring
@Num_AromaticRings,+molecular,#Num_AromaticRing
@LogD,+molecular,#value
@ALogP98,+molecular,#value
@ring,+molecular,+ringid,+atomid,#ringsize,#ringtype
@ring,+molecular,-ringid,+atomid,#ringsize,#ringtype
@ring,+molecular,+ringid,-atomid,#ringsize,#ringtype
@ring,+molecular,-ringid,-atomid,#ringsize,#ringtype
Fig. 3. Mode declaration
72
4.2 SVM Approach
We also tried SVM for comparison with ILP, using 77 features for learning (Table 2).
Detail information is shown in Appendix A.
Table 2. Attributes used for SVM
Types of features The number of features
Related to structure 39
Related to ALogP 6
Related to size or weight 14
Related to energy 12
Other 6
Total 77
Cost parameters and gamma parameters were determined using a grid search for 20
split from 0.0001 to 10,000. The kernel used RBF.
4.3 Evaluation
Ten-fold cross-validation was used in both approaches. True Positive (tp) , False
Negative (fn) , True Negative (tn) , False Positive (fp) , Accuracy , Precision , Recall
and F value were used for Evaluation. Especially, this paper focuses on tp and F val-
ue.
5. RESULTS
Table 3 shows the ILP results.
Table 3. ILP results
Setting name tp fn tn fp Accuracy Precision Recall F value
ILP1 92 179 699 114 0.73 0.447 0.339 0.386
ILP2 116 155 644 169 0.701 0.407 0.428 0.417
ILP3 127 144 605 208 0.675 0.379 0.469 0.419
ILP4 88 183 712 101 0.738 0.466 0.325 0.383
ILP5 131 140 572 241 0.649 0.352 0.483 0.407
ILP6 139 132 568 245 0.652 0.362 0.513 0.424
ILP7 165 106 523 290 0.635 0.363 0.609 0.455
ILP8 165 106 542 271 0.652 0.378 0.609 0.467
Table 4 shows comparison of the best of SVM and the best of ILP
73
Table 4. Comparison of the best of SVM and the best of ILP
Approach tp fn tn fp Accuracy Precision Recall F value
SVM 123 148 703 110 0.762 0.528 0.454 0.488
ILP8 165 106 542 271 0.652 0.378 0.609 0.467
Table 5 shows the best rules obtained by ILP8. A good rule has many positive exam-
ples and few negative examples. All the output list of rules obtained by ILP8 are
shown in Appendix B
Table 5. Rules for compound structure
Rule number Interpretation Positive Negative
Rule1 Atom C has a single bond with the aromatic ring. 27 10
Rule2 There is an aromatic ring containing an atom S 20 8
and atom C has a double bond with something.
Rule3 Two aromatic rings bond to each other 22 10
and each aromatic ring have a single bond.
Rule4 An aromatic ring containing an atom N 15 3
and An aromatic ring consisted of 5 atoms bond to each other
Rule5 An aromatic ring containing an atom S 14 2
and another aromatic ring bond to each other
6. CONCLUSION
Although SVM F values slightly exceeded those of ILP, ILP tp values greatly exceed-
ed those of SVM. For virtual screening, it is very important to reduce the positive
example of misclassification. Results of this study indicate that structural features of
the compounds are useful in predicting immunity activation.
Using the ring structure as background knowledge yielded better results than not us-
ing ring structure. Therefore, the ring structure is considered an important factor in
plant immunity activation.
When analyzing rules using ILP, comparison of known plant immunity activators
indicated that Rule 2 was true for all three compounds. For rule showing a structure
that is different from the known plant immunity activator, there is a need for further
investigation.
In this study, it was possible to predict the partial structure that exists in all com-
pounds of known plant-immunity activators. In addition, the rule that is unknown the
relationship between immunity activity has been predicted. In order to improve pre-
diction accuracy, it is essential to improve background knowledge in the future.
74
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75
Appendix: A
Table 6 shows feature list in SVM approach. Feature name depends on Discovery
Studio.
Table 6. Feature list in SVM
Category Feature name Category Feature name
C HBA_Count A ALogP
HBD_Count ALogP_MR
NPlusO_Count ALogP98
Num_AromaticBonds ALogP98_Unknown
Num_AromaticRings Apol
Num_AtomClasses LogD
Num_Atoms W Molecular_3D_PolarSASA
Num_Bonds Molecular_3D_SASA
Num_BridgeBonds Molecular_3D_SAVol
Num_BridgeHeadAtoms Molecular_FractionalPolarSASA
Num_ChainAssemblies Molecular_FractionalPolarSurfaceArea
Num_Chains Molecular_Mass
Num_ExplicitAtoms Molecular_PolarSASA
Num_ExplicitBonds Molecular_PolarSurfaceArea
Num_ExplicitHydrogens Molecular_SASA
Num_H_Acceptors Molecular_SAVol
Num_H_Acceptors_Lipinski Molecular_SurfaceArea
Num_H_Donors Molecular_Volume
Num_H_Donors_Lipinski Molecular_Weight
Num_Hydrogens VSA_TotalArea
Num_NegativeAtoms E Angle Energy
Num_PositiveAtoms Bond Energy
Num_RingAssemblies CHARMm Energy
Num_RingBonds Dihedral Energy
Num_Rings Electrostatic Energy
Num_Rings3 Energy
Num_Rings5 Improper Energy
Num_Rings6 Initial Potential Energy
Num_Rings7 Minimized_Energy
Num_Rings8 Potential Energy
Num_RotatableBonds Strain_Energy
Num_SpiroAtoms Van der Waals Energy
Num_StereoAtoms O AverageBondLength
Num_StereoBonds FormalCharge
Num_TerminalRotomers Initial RMS Gradient
Num_TrueStereoAtoms Molecular_Solubility
Num_UnknownPseudoStereoAtoms RadOfGyration
Num_UnknownTrueStereoAtoms RMS Gradient
Organic_Count
C: Related to structure A: Related to AlogP W: Related to size or weight
E: Related to energy O: Other
76
Appendix: B
Fig.4 show all the output list of rules obtained by ILP8.
Rule Positive Negative
dock(A) :- atom#1(A, B, s), atom#1(A, C, c), bond#1(A, D, C, 2), bond#2(A, B, E, ar), bond#2(A,
20 8
E, F, ar)
dock(A) :- bond#3(A, B, C, 2), bond#1(A, D, C, 1), bond#2(A, B, E, 1), bond#2(A, E, F, 1) 10 2
dock(A) :- bond#3(A, B, C, ar), bond#1(A, D, C, 1), bond#1(A, E, B, ar), bond#1(A, F, D, 3) 10 8
dock(A) :- bond#3(A, B, C, 1), bond#1(A, D, B, ar), bond#1(A, E, D, 1), ring#2(A, F, E, 6, ar) 17 6
dock(A) :- bond#3(A, B, C, ar), atom(A, B, n), bond#1(A, D, B, 1), bond#2(A, C, E, ar) 14 10
dock(A) :- bond#3(A, B, C, ar), atom(A, B, s), bond#1(A, D, C, 1), bond#1(A, E, D, ar) 14 2
dock(A) :- atom#1(A, B, c), bond#1(A, C, B, ar), bond#1(A, D, C, ar), bond#2(A, B, E, 1),
27 10
ring#2(A, F, E, 6, ar)
dock(A) :- atom#1(A, B, n), atom#1(A, C, n), bond#1(A, D, B, 2), bond#1(A, E, C, 1), ring#2(A, F,
10 9
D, 6, not_ar)
dock(A) :- bond#3(A, B, C, ar), atom(A, C, n), bond#1(A, D, B, 1), bond#1(A, E, B, ar), bond#1(A,
11 3
F, D, 2), bond#2(A, E, G, 1)
dock(A) :- atom#1(A, B, n), atom#1(A, C, o), bond#1(A, D, C, 1), bond#1(A, E, D, 1), ring#2(A, F,
18 8
B, 5, ar)
dock(A) :- atom#1(A, B, c), bond#1(A, C, B, 2), bond#1(A, D, B, 1), bond#1(A, E, D, ar),
11 8
bond#2(A, C, F, 1)
dock(A) :- bond#3(A, B, C, ar), bond#1(A, D, B, ar), bond#1(A, E, C, 1), bond#2(A, D, F, 1),
20 10
ring#2(A, G, E, 6, ar)
dock(A) :- atom#1(A, B, n), atom#1(A, C, c), bond#1(A, D, C, ar), bond#1(A, E, D, 1), bond#2(A,
10 9
B, F, ar), ring#2(A, G, E, 5, not_ar)
dock(A) :- atom#1(A, B, n), atom#1(A, C, c), bond#1(A, D, B, ar), bond#1(A, E, D, 1), bond#2(A,
15 10
C, F, ar), ring#2(A, G, F, 6, not_ar)
dock(A) :- bond#3(A, B, C, ar), atom(A, B, n), bond#1(A, D, C, ar), bond#2(A, C, E, ar), bond#2(A,
20 10
E, F, ar), bond#2(A, D, G, 1)
dock(A) :- bond#3(A, B, C, 1), bond#1(A, D, B, 1), bond#2(A, D, E, 1), ring#2(A, F, E, 6, not_ar),
10 5
ring#2(A, G, C, 5, ar)
dock(A) :- atom#1(A, B, n), bond#2(A, B, C, 2), bond#2(A, C, D, 1), ring#2(A, E, D, 5, not_ar) 10 8
dock(A) :- atom#1(A, B, n), atom#1(A, C, n), bond#2(A, C, D, ar), bond#2(A, D, E, ar), bond#2(A,
16 10
E, F, ar), ring#2(A, G, B, 6, not_ar)
dock(A) :- atom#1(A, B, c), bond#1(A, C, B, ar), bond#1(A, D, C, 1), bond#1(A, E, D, ar),
22 10
bond#2(A, B, F, 1), bond#2(A, E, G, 1)
dock(A) :- bond#3(A, B, C, 1), atom(A, B, c), bond#1(A, D, C, 2), bond#2(A, C, E, 1), ring#2(A, F,
15 10
E, 5, ar)
dock(A) :- bond#3(A, B, C, ar), atom(A, B, n), bond#1(A, D, C, ar), bond#1(A, E, C, 1), bond#1(A,
12 10
F, D, 1), bond#1(A, G, F, ar)
dock(A) :- atom#1(A, B, c), bond#1(A, C, B, 2), bond#2(A, B, D, 1), bond#2(A, D, E, ar),
11 10
bond#2(A, C, F, 1)
dock(A) :- atom#1(A, B, n), atom#1(A, C, h), bond#1(A, D, B, 2), bond#1(A, E, D, 1), bond#1(A,
11 10
F, C, 1), ring#2(A, G, F, 6, not_ar)
dock(A) :- atom#1(A, B, c), atom#1(A, C, o), bond#1(A, D, B, ar), bond#1(A, E, D, 1), bond#1(A,
11 10
F, E, ar), bond#2(A, C, G, ar)
dock(A) :- atom#1(A, B, n), bond#2(A, B, C, 2), bond#2(A, B, D, 1) 12 10
dock(A) :- atom#1(A, B, n), atom#1(A, C, o), bond#2(A, B, D, ar), bond#2(A, D, E, ar), ring#2(A,
11 10
F, C, 6, not_ar)
dock(A) :- atom#1(A, B, o), atom#1(A, C, n), bond#1(A, D, C, ar), bond#2(A, B, E, ar), bond#2(A,
10 7
D, F, 1)
dock(A) :- bond#3(A, B, C, ar), atom(A, B, n), bond#2(A, C, D, ar), bond#2(A, D, E, ar), bond#2(A,
15 3
E, F, 1), ring#2(A, G, F, 5, ar)
77
dock(A) :- bond#3(A, B, C, 1), bond#2(A, C, D, 1), bond#2(A, B, E, 2), ring#2(A, F, E, 5, not_ar) 10 5
dock(A) :- bond#3(A, B, C, ar), atom(A, B, n), bond#1(A, D, C, ar), bond#1(A, E, D, ar), bond#1(A,
15 7
F, B, 1), bond#2(A, E, G, 1)
dock(A) :- atom#1(A, B, n), atom#1(A, C, c), bond#1(A, D, C, 2), bond#2(A, D, E, 1), bond#2(A,
10 6
B, F, 1), ring#2(A, G, F, 5, not_ar)
dock(A) :- bond#3(A, B, C, ar), bond#1(A, D, B, ar), bond#1(A, E, D, ar), bond#2(A, E, F, ar),
11 2
bond#2(A, C, G, ar), ring#2(A, H, F, 5, not_ar)
dock(A) :- atom#1(A, B, s), bond#1(A, C, B, ar), bond#2(A, B, D, ar), bond#2(A, D, E, ar),
21 10
ring#2(A, F, E, 5, ar)
dock(A) :- bond#3(A, B, C, ar), atom(A, C, n), bond#1(A, D, B, ar), bond#1(A, E, B, 1), bond#1(A,
10 3
F, D, ar), bond#1(A, G, E, 2)
dock(A) :- atom#1(A, B, s), atom#1(A, C, n), bond#1(A, D, B, ar), bond#1(A, E, D, ar), bond#2(A,
10 9
C, F, ar), bond#2(A, F, G, ar)
dock(A) :- bond#3(A, B, C, 1), atom(A, B, o), bond#1(A, D, B, 1), bond#1(A, E, C, ar), bond#1(A,
10 9
F, D, 1), bond#1(A, G, E, ar)
dock(A) :- atom#1(A, B, o), atom#1(A, C, h), bond#1(A, D, B, ar), bond#1(A, E, C, 1), bond#2(A,
10 7
E, F, 1), ring#2(A, G, F, 5, ar)
dock(A) :- atom#1(A, B, o), atom#1(A, C, f), bond#1(A, D, B, 1), bond#2(A, C, E, 1), bond#2(A, E,
11 4
F, 1), bond#2(A, F, G, 1)
dock(A) :- bond#3(A, B, C, 1), atom(A, C, n), bond#1(A, D, B, 1), bond#1(A, E, C, 1), bond#1(A,
11 10
F, D, 2), ring#2(A, G, E, 6, not_ar)
dock(A) :- bond#3(A, B, C, ar), atom(A, B, n), bond#1(A, D, C, ar), bond#1(A, E, D, ar), bond#2(A,
18 10
C, F, ar), bond#2(A, F, G, ar)
dock(A) :- atom#1(A, B, n), atom#1(A, C, o), bond#1(A, D, C, ar), bond#1(A, E, B, 1), bond#1(A,
11 10
F, E, ar)
dock(A) :- bond#3(A, B, C, 1), atom(A, B, s), bond#1(A, D, C, ar), bond#1(A, E, D, ar), ring#2(A,
11 8
F, C, 5, ar)
dock(A) :- bond#3(A, B, C, 1), atom(A, C, h), bond#1(A, D, B, ar), bond#1(A, E, D, ar), bond#1(A,
10 8
F, D, 1), ring#2(A, G, E, 5, ar)
dock(A) :- atom#1(A, B, c), atom#1(A, C, h), bond#1(A, D, C, 1), bond#2(A, B, E, 1), ring#2(A, F,
10 9
D, 5, not_ar), ring#2(A, G, E, 5, ar)
dock(A) :- bond#3(A, B, C, 1), atom(A, B, c), bond#1(A, D, C, ar), ring#2(A, E, C, 5, ar), ring#2(A,
10 8
F, D, 6, ar)
dock(A) :- bond#3(A, B, C, 1), atom(A, C, h), bond#1(A, D, B, 1), bond#2(A, D, E, 1), bond#2(A,
12 10
E, F, ar)
dock(A) :- bond#3(A, B, C, 1), atom(A, C, n), bond#1(A, D, B, 1), bond#1(A, E, C, ar), bond#1(A,
10 8
F, E, ar), bond#2(A, F, G, 1)
dock(A) :- atom#1(A, B, n), atom#1(A, C, o), bond#1(A, D, C, ar), bond#2(A, B, E, ar), bond#2(A,
12 9
E, F, ar), ring#2(A, G, F, 5, ar)
Fig. 4 Rule list
78