INTRODUCTION

Virtual screening is an important approach in the drug discovery process. Especially, 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 possibility that the cause of health damage in humans and destruction of biota. In addition, 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 activators 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 addition, the mechanism of plant-immunity activation is still largely unknown [4].

Fig. 1. Known plant-immunity activators

The development of plant immunity activators has been slow, due to the time required 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 patterns 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].

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 indicated that if the ROS value is high, the compound is likely to be a plant-immunity activator.

DATASET

In

METHOD

This chapter describes our method. We had two approaches. Fig. 2 shows the overview of our method.

Fig. 2. Method overview

The two approaches are described as follow.

ILP Approach

With the ILP approach, structural features and some numerical features of the compound 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 compounds. In parentheses, there are argument of predicates. 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. atom,bond,ring @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

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

Cost parameters and gamma parameters were determined using a grid search for 20 split from 0.0001 to 10,000. The kernel used

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 value.

RESULTS

Table 3 shows the ILP results.

CONCLUSION

Although SVM F values slightly exceeded those of ILP, ILP tp values greatly exceeded 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 using 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 compounds of known plant-immunity activators. In addition, the rule that is unknown the relationship between immunity activity has been predicted. In order to improve prediction accuracy, it is essential to improve background knowledge in the future.

Approach