Prediction method for protoporphyrinogen oxidase (PPO) inhibitors

Abstract

The present invention provides a technology for predicting the level of PPO inhibition objectively and with high accuracy using LUMO distribution as a "molecular descriptor," and a technology for selecting a PPO inhibitor based on the predicted level of PPO inhibition. [Solution] The present invention provides a method for predicting PPO inhibitory activity based on the correlation between LUMO distribution and PPO inhibitory activity for each compound, predicting the level of PPO inhibitory activity based on the correlation between the difference in variation in PPO inhibitory activity between different plant species and the level of PPO inhibitory activity, and selecting PPO inhibitors using the predicted PPO inhibitory activity as an index.

Description

[Technical Field]

[0001] The present invention relates to a method for predicting PPO inhibitors, and more specifically to a method for predicting PPO inhibitors using LUMO (Lowest Unoccupied Molecular Orbital) distributions as "molecular descriptors," and a method for selecting PPO inhibitors using the prediction method. [Background technology]

[0002] In recent years, in the research and development of herbicides, it has become extremely expensive to identify promising lead compounds that have excellent herbicidal activity, reduced human toxicity and crop damage, and ensure safety for the natural environment, and then to further optimize the lead compounds and bring them to market as new herbicides.

[0003] One method for predicting the herbicidal activity of unmeasured compounds is to statistically process the measurement data of numerous measured compounds (reference compounds) and their structural information using in silico methods, and then predict the biological activity of the unmeasured compound based on the degree of structural similarity between the reference compounds and the unmeasured compound. This inductive method is based on the hypothesis that "structural similarity between compounds should lead to similar biological activity, and therefore biological activity can be predicted by displaying the structural 'closeness' of compounds." Compound structural information uses "molecular descriptors," which are quantified versions of a compound's structural features and physicochemical properties (e.g., hydrophobicity and steric parameters) for easy computerization. This method, based on the Hansh-Fujita method (Non-Patent Document 1), has served as the starting point for various methods for quantitatively analyzing the relationship between chemical structure and physiological activity. Molecular descriptors include hydrophobicity (LogP, π) and steric parameters (MR, STERIMOL, etc.).

[0004] The inductive method described above can use various measurements, such as cell-based measurements, enzyme-based measurements, and animal tests, from databases such as PubChem (https: / / pubchem.ncbi.nlm.nih.gov / ), for herbicide research. However, it is extremely difficult to prepare a quantitative database of similar compounds containing various measurements for studying the correlation between the structure of the required compound and its herbicidal activity in a short period of time. Therefore, objective prediction methods are still insufficient, and the development of such technology has been strongly desired.

[0005] Therefore, in order to efficiently and effectively research and develop candidate herbicide compounds, it is necessary to create significant "molecular descriptors" that predict the structural and electronic complementarity between target enzymes such as PPO, whose three-dimensional structures are known, and each inhibitor compound, and to use these in a quantitative prediction method for enzyme inhibitors.

[0006] Plant PPO inhibitors, also known as "photobleaching herbicides," require light for their herbicidal activity. Research into the mechanism of action of "photobleaching herbicides" has been ongoing for over half a century (Non-Patent Document 2), and they are inhibitors with a long history. Subsequent enzyme-level research has revealed that plant PPO is an enzyme in the pathway that synthesizes porphyrin from aminolevulinic acid (ALA) and is located in plant chloroplasts. When plant PPO is inhibited, protoporphyrinogen IX leaks from the chloroplast into the cytoplasm, where it undergoes autoxidation to become protoporphyrin IX, which accumulates intracellularly (Non-Patent Document 3). In the presence of light and oxygen, protoporphyrin IX generates reactive oxygen species, which then cause light-induced peroxidative damage to biomembranes, resulting in rapid cell death and plant withering (Non-Patent Document 4).

[0007] Whether or not a plant PPO is the site of action is experimentally detected by a method using isolated plant PPO, and it is preferable to obtain the enzyme material from chloroplasts (etioplasts) that are not exposed to light.

[0008] For example, the PPO inhibitory activity of commercially available herbicides is measured as the pI50 (the logarithm of the reciprocal of the molar concentration of a compound that inhibits PPO enzyme activity by 50%) in corn etioplasts, and examples reported include the phenylpyrazole herbicide Pyraflufen Ethyl (ethyl 2-chloro-5-(4-chloro-5-difluoromethoxy-1-methylpyrazol-3-yl)-4-fluorophenoxyacetate), represented by the following structural formula; the diphenyl ether herbicide Oxyfluorofen (2-chloro-4-(trifluoromethyl)phenyl(3-ethoxy-4-nitrophenyl)ether); and the cyclic imide herbicide Chlorophthalim (N-(4-chlorophenyl)-1-cyclohexene-1,2-dicarboximide) (Non-Patent Document 5).

[0009] [ka]

[0010] [ka]

[0011] [ka]

[0012] Other examples of pI50 using corn etioplasts include examples of various cyclic imide compounds including chlorophthalim (Non-Patent Document 6).

[0013] On the other hand, in other plant species, examples of pI50 using barley etioplasts have been reported for various diphenyl ethers including oxyfluorofen (Non-Patent Document 7).

[0014] On the other hand, a pyrazole compound B (1-(5-hydroxy-1,3-dimethyl-1H-pyrazol-4-yl)ethanone) represented by the following structural formula has been reported as a control compound that shows no herbicidal activity (Non-Patent Document 8).

[0015] [ka]

[0016] Crystal structure data for PPO derived from tobacco plants (Nicotiana tabacum) has been reported to contain a three-dimensional structure containing a phenylpyrazole PPO inhibitor A (4-bromo-3-(5'-carboxy-4'-chloro-2'-fluorophenyl)-1-methyl-5-trifluoromethyl-pyrazole) shown in the following structural formula (Non-Patent Document 9), and the crystal structure data can be obtained from the Protein Data Bank (PDB) under the accession number 1SEZ.

[0017] [ka]

[0018] The crystal structure data of PPO derived from myxobacterium (Myxococcus xanthus) (Non-Patent Document 10) and human (Homo sapiens) (Non-Patent Document 11), which contain the diphenyl ether PPO inhibitor acifluorfen (5-[2-chloro-4-(trifluoromethyl)phenoxy]-2-nitrobenzoic acid), shown by the structural formula below, can be obtained from the PDB under accession numbers 12IVD and 3NKS, respectively.

[0019] [ka]

[0020] First, to create a "molecular descriptor" that quantifies the PPO enzyme inhibition reaction, we focused on oxidized flavin adenine dinucleotide (FAD), a PPO coenzyme represented by the following structural formula: Specifically, we hypothesized that oxidized FAD, which normally functions as an electrophilic electron acceptor, has its electrophilicity inhibited by PPO inhibitors.

[0021] [ka]

[0022] However, there have been no previous studies that have demonstrated in detail, based on the three-dimensional structure, how PPO controls the electron transfer reaction using oxidized FAD as an electron acceptor. Therefore, we used frontier orbital theory (Non-Patent Document 12) to predict the chemical reaction based on the interaction between the highest occupied molecular orbital (HOMO) of the electron donor molecule and the lowest unoccupied molecular orbital (LUMO) of the electron acceptor molecule. Specifically, electrons localized in the HOMO of the PPO inhibitor interact with oxidized FAD, resulting in delocalization and mixing with the LUMO of the oxidized FAD molecule. We hypothesized that a correlation with PPO inhibitor activity could be established by quantifying and displaying this change in the LUMO distribution of oxidized FAD as a "molecular descriptor."

[0023] It was not known at all that the LUMO distribution of a bimolecular complex of oxidized FAD and a PPO inhibitor in the PPO inhibitory active site could be used as a "molecular descriptor" to accurately predict the level of PPO inhibition, nor was it known at all that a method for selecting PPO inhibitors could be provided based on the level of PPO inhibition predicted in this way. [Prior art documents] [Non-patent literature]

[0024] [Non-Patent Document 1] C. Hansch and T. Fujita, J. Am. Chem. Soc., (1964) 86, 1616-1626 [Non-licensed document 2] S. Matsunaka, J. Agric. Food Chem, (1969) 17(2), 171-175 [Non-licensed document 3] M. Matringe et al., The Biochemical Journal, (1989) 60, 231-235

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Non-licensed literature 9

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[0025] An objective of the present invention is to provide a technique for predicting the level of PPO inhibition objectively, accurately, and precisely using LUMO distribution as a "molecular descriptor," and a technique for selecting a PPO inhibitor based on the predicted level of PPO inhibition. [Means for solving the problem]

[0026] In view of these circumstances, the present inventors have made extensive research efforts in search of a technique for objectively, accurately, and precisely predicting the level of PPO inhibition, and as a result, have found that the level of PPO inhibition can be predicted objectively and accurately based on the correlation between the LUMO distribution and the level of PPO inhibition for each compound, thereby completing the present invention. 。 (1 ) PPO inhibitory activity against one plant species For each compound as "molecular descriptors" LUMO distribution and The aforementioned Based on the correlation with PPO inhibitory activity A first regression equation is created, and the LUMO distribution as a "molecular descriptor" of the PPO inhibitor to be predicted is interpolated into the first regression equation to obtain the PPO inhibitor to be predicted for the one plant species. Predicting PPO inhibitory activity A method for predicting a PPO inhibitor, comprising: The LUMO distribution as the "molecular descriptor" indicates the degree to which the LUMO distribution of the conformation of the bimolecular complex of oxidized FAD and the PPO inhibitor to be predicted has disappeared in stages, compared with the LUMO distribution of oxidized FAD alone. Characterized by 、P Prediction method for PO inhibitors. ( 2 ) The first regression equation Prediction of PPO inhibitory activity in corn as the one plant species , which is calculated by the following formula: 1 to The method for predicting PPO inhibitors described herein. <Corn> PPO inhibitory activity = 0.662 * LUMO distribution index + 5.795 (3) The method for predicting a PPO inhibitor according to claim 1, wherein the prediction of the PPO inhibitory activity using the first regression equation is calculated using the following equation, with barley as the one plant species: <Barley> PPO inhibitory activity = 0.987 * LUMO distribution index + 2.363 (4) The correlation between each PPO inhibitory activity and the LUMO distribution as a "molecular descriptor" among different plant species was investigated using the above method. Between different plant species each Difference in PPO inhibitory activity A second regression equation is created using a dummy variable to indicate that the difference in the variation between the different plant species is constant, and the difference in the variation between the different plant species is calculated according to the second regression equation.PPO inhibitory activity Difference Predict the extent A method for predicting a PPO inhibitor, comprising: The LUMO distribution as a "molecular descriptor" indicates the degree to which the LUMO distribution of the conformation of the bimolecular complex of oxidized FAD and the PPO inhibitor has disappeared in stages compared to the LUMO distribution of oxidized FAD alone. Characterized by 、P Prediction method for PO inhibitors. (5) The plant species are corn and barley; The second regression equation is the following equation: The method for predicting a PPO inhibitor according to claim 4, wherein I "barley" in the formula is a dummy variable, and the I "barley" is 1 for barley. PPO inhibitory activity = 0.768 * LUMO distribution index - 2.238 * I [barley] + 5.465 ( 6 ) A method for selecting a PPO inhibitor, comprising selecting a PPO inhibitor from candidate compounds having PPO inhibitory activity against a single plant species, the method comprising: (1)~( 3 ) The method for predicting a PPO inhibitor according to any one of the preceding claims is used to predict the candidate compound as the PPO inhibitor to be predicted using the first regression equation. The predicted PPO inhibitory activity was used as an index. Then, by comparing the PPO inhibitory activity of existing PPO inhibitors with the index, a PPO inhibitor is selected from the candidate compounds. A method for selecting a PPO inhibitor, comprising: (7) The method for selecting a PPO inhibitor according to (6), wherein the LUMO distribution of the conformation of a bimolecular complex of oxidized FAD and the existing PPO inhibitor is lost by 50% or more to 100% compared to the LUMO distribution of oxidized FAD alone. (8) The method for selecting a PPO inhibitor according to (6), wherein the existing PPO inhibitor is any one PPO inhibitor selected from pyraflufen ethyl, oxyfluorofen, chlorophthalim, and acifluorfen. ( 9 a method for selecting a PPO inhibitor, comprising selecting a PPO inhibitor from candidate compounds having PPO inhibitory activity against a single plant species, (1)~( 3 ) The method for predicting a PPO inhibitor according to any one of the preceding claims is used to predict the candidate compound as the PPO inhibitor to be predicted using the first regression equation. The predicted PPO inhibitory activity was used as an index. Then, a PPO inhibitor is selected from the candidate compounds by comparing the PPO inhibitory activity of the candidate compounds measured by an experimental method with the index. A method for selecting a PPO inhibitor, comprising: [Effects of the Invention]

[0027] According to the present invention, a technique for predicting the level of PPO inhibition objectively and with high accuracy can be provided. BEST MODE FOR CARRYING OUT THE INVENTION

[0028] (1) Method for predicting PPO inhibitors according to the present invention The method for predicting a PPO inhibitor of the present invention is characterized by using LUMO distribution as a "molecular descriptor" to predict the level of PPO inhibition. To make this prediction, the relationship between the level of PPO inhibition and the LUMO distribution is clarified, and a regression equation or the like showing this relationship is prepared and used for the prediction. Details of this method are described below.

[0029] The general inhibitory activity of PPO inhibitors is measured by pI50, with a higher value indicating stronger activity. The pI50s for pyraflufen ethyl were obtained from Non-Patent Document 5, for cyclic imide compounds from Non-Patent Document 6, for corn from Non-Patent Documents 5 and 6, for chlorophthalim, and for barley from Non-Patent Document 7, and were used for analysis. The pI50 for corn of oxyfluorofen was obtained from Non-Patent Document 5, and the pI50 for barley was obtained from Non-Patent Document 7, and were used for analysis.

[0030] The LUMO of the conformation of the bimolecular complex of oxidized FAD and the inhibitor to be predicted is calculated using the ab initio molecular orbital / density functional theory calculation program GAMESS, and it is desirable to perform the calculation using the 6-31G(2d,p) basis set with the B3LYP functional of the DFT (Density Functional Theory) method.

[0031] The LUMO distribution can be expressed as a "molecular descriptor" according to the following qualitative standard method: 5 = 75% or more to 100% loss, 4 = 50% or more to less than 75% loss, 3 = 25% or more to less than 50% loss, 2 = 1% or more to less than 25% loss, and 1 = no effect, compared to the LUMO distribution of oxidized FAD alone.

[0032] To clarify this relationship, correlation analysis and regression analysis of corn PPO inhibitory activity and LUMO distribution were performed on the corn pI50 of pyraflufen ethyl and oxyfluorofen (Non-Patent Document 5) and the corn pI50 of 41 compounds, including chlorophthalim extracted from cyclic imide systems (Non-Patent Document 6). A significant correlation (correlation coefficient = 0.90) was observed (see Figure 1). It was found that by using the following regression equation calculated in this way, corn PPO inhibitory activity can be predicted with high accuracy from the LUMO distribution. <Corn> pI50=0.662*LUMO distribution+5.795 (Correlation coefficient = 0.90)

[0033] Furthermore, when correlation analysis and regression analysis of barley PPO inhibitory activity and LUMO distribution were performed by extracting 16 diphenyl ether compounds including oxyfluorophen and performing correlation analysis and regression analysis of barley pI50 (Non-Patent Document 7) and LUMO distribution, a significant correlation (correlation coefficient = 0.96) was observed (see Figure 2). It was found that by using the regression equation calculated in this way, PPO inhibitory activity can be predicted with high accuracy from the LUMO distribution. <Barley> pI50=0.987*LUMO distribution+2.363 (Correlation coefficient = 0.96)

[0034] The regression equation for the difference in pI50 variation between the different plant species, corn and barley, and the LUMO distribution, also showed a significant correlation (see Figure 3) when a dummy variable (I [barley]) that assigns a value of 1 to the pI50 of barley was added. It can be seen that the level of PPO inhibitory activity between different plant species can be predicted with high accuracy using the following regression equation calculated in this way. pI50=0.768*LUMO distribution-2.238*I[barley]+5.465 (Correlation coefficient = 0.94)

[0035] The correlation analysis, regression analysis, etc. were performed using MS Office Excel, but can also be performed using free software or commercially available general-purpose multivariate analysis software.

[0036] As described above, it is clear that the use of LUMO distribution as a "molecular descriptor" enables the level of PPO inhibitory activity to be predicted with high accuracy. However, other characteristic values ​​related to PPO inhibitors, such as hydrophobicity (LogP) and steric parameters (MR, STERIMOL, etc.), can also be appropriately added as "molecular descriptors." That is, as with the "molecular descriptor" of LUMO distribution, incorporating these into "molecular descriptors" to create a multiple regression analysis formula can contribute to improving the accuracy of predicting PPO inhibitory activity. Such a range also falls within the technical scope of the present invention.

[0037] (2) Method for selecting a PPO inhibitor according to the present invention The method for selecting a PPO inhibitor of the present invention is to select a PPO inhibitor based on the degree of PPO inhibitory activity predicted as described above. choice The rank of the LUMO distribution is not particularly limited, but a rank of 4 or 5 is preferred.

[0038] The present invention will be described in more detail below with reference to examples, but it goes without saying that the present invention is not limited to these examples. [Example] Example 1

[0039] <LUMO calculation of oxidized FAD alone> The PPO crystal structure data, identified by the accession number 1SEZ, was obtained from the PDB. Only the conformation of oxidized FAD was extracted from this conformation data, and hydrogen atoms were added to this conformation using the open-source modeling software Molby. This conformation was designated as oxidized FAD alone. The input file for this oxidized FAD alone in GAMESS was created using Molby. First, a structural optimization of all interatomic distances, bond angles, and dihedral angles was performed using the semi-empirical molecular orbital calculation method PM3 in GAMESS. After this, the optimized structure was used to further optimize all interatomic distances, bond angles, and dihedral angles using the Hartree Fock (HF) method with a 3-21G basis set.

[0040] Using the optimized conformation, single-point calculations were performed using the 6-31G(2d,p) basis set calculation with the B3LYP functional of the DFT method to determine the LUMO.

[0041] The calculation results were obtained using the open-source modeling software Avogadro, with the isosuraface value set to 0.05, and the LUMO of oxidized FAD alone was calculated. Figure 4 shows the region within the molecular structure where the LUMO is distributed on the isoalloxazine ring. At the same time, the region where the LUMO exists indicates that no electrons are present, meaning that no electrons are present on the isoalloxazine ring and that an electrophilic reaction can occur. This is considered to be the LUMO of oxidized FAD alone, acting as an electron acceptor when PPO is normal and not inhibited by PPO inhibitors. Example 2

[0042] <LUMO calculation of inhibitor A complex> The PPO crystal structure data with accession number 1SEZ is obtained from the PDB in the same manner as in Example 1. From this conformational data, only the conformation of the bimolecular complex of oxidized FAD and inhibitor A is extracted, and this conformation is designated as the inhibitor A complex.

[0043] The LUMO of the inhibitor A complex was calculated in the same manner as in Example 1 and is shown in Figure 5. In the inhibitor A complex, the LUMO on the isoalloxazine ring that existed in oxidized FAD alone almost completely disappeared. This means that the electrons localized in the HOMO of inhibitor A interacted with oxidized FAD and became mixed, resulting in delocalization of the electrons in the oxidized FAD molecule, which may inhibit the electrophilic reaction of oxidized FAD. Example 3

[0044] <LUMO calculation of compound B complex> As a control compound with no herbicidal activity, a compound B complex consisting of a two-molecule complex of compound B and oxidized FAD was prepared using molecular modeling software Winmostar64Bit(FREE)V11.6.1, by modifying the structure of the inhibitor A in the inhibitor A complex of Example 2, such as by changing the substituents.

[0045] The LUMO was calculated in the same manner as in Examples 1 and 2 and shown in Fig. 6. The region where the LUMO is distributed on the isoalloxazine ring in the Compound B complex coincided with the LUMO of the oxidized FAD monomer of Example 1. It was considered that Compound B does not change the LUMO on the isoalloxazine ring of oxidized FAD and a normal electrophilic reaction similar to that of the oxidized FAD monomer in the normal state can occur, so it does not cause PPO inhibitory activity.

Example 4

[0046] <Calculation of LUMO of Pyraflufen Ethyl Complex> The LUMO of the two-molecule complex of Pyraflufen Ethyl and oxidized FAD (Pyraflufen Ethyl complex) was calculated in the same manner as in Examples 1 to 3 and shown in Fig. 7.

[0047] Compared with the normal LUMO distributions shown in Examples 1 and 3, the LUMO on the isoalloxazine ring of the Pyraflufen Ethyl complex disappeared by 100%, and the electrons localized in the HOMO of Pyraflufen Ethyl were mixed by the interaction with oxidized FAD and delocalized to the oxidized FAD molecule, meaning that the electrophilic reaction of oxidized FAD is inhibited.

Example 5

[0048] <Method for Calculating LUMO Distribution of Acifluorfen Complex> PPO derived from myxobacteria of Acifluorfen was obtained from the PDB accession number 2IVD. Only the conformation of the two-molecule complex of oxidized FAD and the PPO inhibitor Acifluorfen was extracted from this conformational data, and hydrogen was added using Molby. The conformation was designated as the 2IVD complex.

[0049] The inhibitor A complex obtained in Example 2 and the 2IVD complex were superimposed by Winmostar based on the isoalloxazine rings of oxidized FAD. The inhibitor A of the inhibitor A complex was replaced with Aciflyorfen of the 2IVD complex to create an Acifluorfen complex composed of two molecules, oxidized FAD of the inhibitor A complex and Acifluorfen of the 2IVD complex. The LUMO of the Acifluorfen complex was calculated in the same manner as in Examples 1 to 4 and is shown in FIG. 8.

[0050] Similar to the inhibitor A complex of Example 2, in the Acifluorfen complex, the LUMO existing on the isoalloxazine ring, which existed as a single oxidized FAD, almost disappeared. As a result of the electrons localized in the HOMO of Acifluorfen being mixed by the interaction with oxidized FAD and delocalized to the oxidized FAD molecule, it means that the electrophilic reaction of oxidized FAD is inhibited by the electron reaction with Acifluorfen.

Example 6

[0051] <Calculation of LUMO of Oxyfluorofen Complex> The structure of Acifluorfen in the Acifluorfen complex of Example 5 was changed, such as substituents, using Winmostar to create an Oxyfluorofen complex.

[0052] The LUMO of the Oxyfluorofen complex was calculated in the same manner as in Example 5 and is shown in FIG. 9. Similar to the 4 Pyraflufen Ethyl complex of the example, the LUMO on the isoalloxazine ring disappeared by 100%. Similar to the inhibitor A, as a result of the electrons localized in the HOMO of Oxyfluorofen being mixed by the interaction with oxidized FAD and delocalized to the oxidized FAD molecule, it means that the electrophilic reaction of oxidized FAD is inhibited.

Example 7

[0053] <Calculation of LUMO Distribution in the Correlation Analysis of Maize><0In addition to the Pyraflufen Ethyl complex of Example 4 and the Oxyfluorofen complex of Example 5, the LUMOs of the complexes of oxidized FAD and chlorophthalimide compounds No. 1 to No. 40, which specify the substituents in the following general formula (I), were determined by the methods of Examples 1 to 6, respectively, and the LUMO distributions for each complex No. were ranked as follows and listed in Table 1 below. Compared to the LUMO distribution of oxidized FAD alone, 5 = 75% or more to 100% loss, 4 = 50% or more to less than 75% loss, 3 = 25% or more to less than 50% loss, 2 = 1% or more to less than 25% loss, 1 = no effect

[0054] [ka] [Table 1] Example 8

[0055] <Calculation of LUMO distribution in correlation analysis of barley> In addition to the Acifluorfen complex of Example 5 and the Oxyfluorofen complex of Example 6, the LUMO distributions of the complexes with oxidized FAD Nos. 44 to 57, which specify the substituents in the following general formula (II), were calculated in the same manner as in Examples 5 to 7, and the LUMO distributions for each complex No. are listed in Table 2.

[0056] [ka] [Table 2] [Industrial Applicability]

[0057] The present invention provides a technique for objectively and highly accurately predicting PPO inhibitory activity using LUMO distributions as "molecular descriptors." As a result, synthetic organic chemists can use this technique in herbicide research to predict the basic skeletons and substituent selection of compounds that inhibit PPO. [Brief explanation of the drawings] [Figure 1] FIG. 1 shows changes in LUMO distribution at pI50 of maize. [Figure 2] FIG. 1 shows changes in LUMO distribution at pI50 of barley. [Figure 3] FIG. 1 shows the correlation between the difference in variation in PPO inhibitory activity among different plant species and the level of PPO inhibitory activity. [Figure 4] FIG. 1 shows the LUMO of oxidized FAD alone. [Figure 5] FIG. 1 shows the LUMO of the inhibitor A complex. [Figure 6] FIG. 1 shows the LUMO of the compound B complex. [Figure 7] FIG. 1 shows the LUMO of the pyraflufen ethyl complex. [Figure 8] FIG. 1 shows the LUMO of Acifluorfen complexes. [Figure 9] FIG. 1 shows the LUMO of oxyfluorophenone conjugates.

Claims

1. A method for predicting a PPO inhibitor, comprising: creating a first regression equation based on the correlation between a LUMO distribution as a "molecular descriptor" for each compound having PPO inhibitory activity against a single plant species and said PPO inhibitory activity; and interpolating the LUMO distribution as a "molecular descriptor" of a PPO inhibitor to be predicted into said first regression equation, thereby predicting the PPO inhibitory activity of said PPO inhibitor to be predicted against said single plant species, A method for predicting PPO inhibitors, characterized in that the LUMO distribution as the "molecular descriptor" gradually indicates the extent to which the LUMO distribution of the conformation of a bimolecular complex of oxidized FAD and the PPO inhibitor to be predicted has disappeared, compared to the LUMO distribution of oxidized FAD alone.

2. A method for predicting a PPO inhibitor as described in claim 1, characterized in that the prediction of the PPO inhibitor activity using the first regression equation is calculated using the following equation with corn as the one plant species. <Corn> PPO inhibitory activity = 0.662 * LUMO distribution index + 5.795

3. A method for predicting a PPO inhibitor as described in claim 1, characterized in that the prediction of the PPO inhibitor activity using the first regression equation is calculated using the following equation with barley as the one plant species. <Barley> PPO inhibitory activity = 0.987 * LUMO distribution index + 2.363

4. A method for predicting a PPO inhibitor, which comprises creating a second regression equation that uses a dummy variable to indicate a correlation between each PPO inhibitor activity between different plant species and a LUMO distribution as a "molecular descriptor" and that indicates that the difference in variation in each PPO inhibitor activity between said different plant species is constant, and predicting the degree of difference in PPO inhibitor activity between said different plant species according to the difference in variation in said created second regression equation, A method for predicting PPO inhibitors, characterized in that the LUMO distribution as the "molecular descriptor" gradually indicates the extent to which the LUMO distribution of the conformation of a two-molecular complex of oxidized FAD and a PPO inhibitor has disappeared compared to the LUMO distribution of oxidized FAD alone.

5. The plant species are corn and barley, The second regression equation is the following equation: The method for predicting a PPO inhibitor according to claim 4, wherein I "barley" in the formula is a dummy variable, and said I "barley" is 1 for barley. PPO inhibitory activity = 0.768 * LUMO distribution index - 2.238 * I [barley] + 5.465

6. A method for selecting a PPO inhibitor, comprising selecting a PPO inhibitor from candidate compounds having PPO inhibitory activity against a plant species, the method comprising: A method for selecting a PPO inhibitor, characterized in that the PPO inhibitor to be predicted by the PPO inhibitor prediction method described in any one of claims 1 to 3 is selected from among the candidate compounds by comparing the PPO inhibitor activity of existing PPO inhibitors with the index, using the PPO inhibitor activity predicted using the first regression equation as the index.

7. The method for selecting a PPO inhibitor described in claim 6, wherein the LUMO distribution of the conformation of a bimolecular complex of oxidized FAD and the existing PPO inhibitor has disappeared by between 50% and 100% compared to the LUMO distribution of oxidized FAD alone.

8. The method for selecting a PPO inhibitor described in claim 6, wherein the existing PPO inhibitor is any one PPO inhibitor selected from pyraflufen ethyl, oxyfluorofen, chlorophthalim, and aciflofen.

9. A method for selecting a PPO inhibitor, comprising selecting a PPO inhibitor from candidate compounds having PPO inhibitory activity against a plant species, the method comprising: A method for selecting a PPO inhibitor, characterized in that the PPO inhibitor to be predicted by the PPO inhibitor prediction method described in any one of claims 1 to 3 is used as an index for the PPO inhibitor activity predicted using the first regression equation to select a PPO inhibitor from among the candidate compounds by comparing the PPO inhibitor activity of the candidate compounds measured by an experimental method with the index.

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