Automatic method and system for analyzing nuclear magnetic resonance structure of compound

Abstract

The invention provides an automatic method and system for analyzing a nuclear magnetic resonance structure of a compound. The method comprises the following steps: acquiring a molecular formula and nuclear magnetic resonance original data of a target compound; based on the molecular formula, searching a matched candidate compound structure in a PubChem database; performing automatic Fourier transform by using nuclear magnetic resonance original data; automatically analyzing and extracting information of chemical shift, generating a fragment number constraint condition, and screening candidate compound structures to obtain a candidate structure set; calling a MestReNova software package to carry out 13C nuclear magnetic resonance spectrum prediction on the candidate structure set, comparing prediction data with actually measured nuclear magnetic resonance data, and calculating a matching score; and selecting the candidate structure with the minimum matching score as a final analysis result of the target compound. According to the method, high efficiency, low consumption and easy use of nuclear magnetic resonance structure analysis are realized, and the technical bottlenecks that the traditional method is large in calculation amount, long in time consumption and seriously depends on experience are thoroughly solved.

Description

Technical Field

[0001] The present invention relates to the technical field of automatic structure analysis, and in particular to an automated method and system for compound nuclear magnetic resonance structure analysis. Background Art

[0002] Nuclear magnetic resonance (NMR) is a key technology for the structural elucidation of organic compounds. Manual analysis requires years of experience in NMR structure analysis and the analysis of multiple spectra, often taking hours or even days.

[0003] The process of automatic structure analysis is usually based on the structural fragments obtained by NMR spectra (such as CH3, CH2, CH, C=C, C=O, etc.), combined with 1 H- 1 H COSY, 1 H- 13 C HMBC, 1 H- 1 H NOESY and other two-dimensional spectrum information, these fragments are reasonably spliced ​​so that the results meet all the nuclear magnetic resonance information, such as chemical shift, coupling constant, long-range correlation, etc.

[0004] However, a major problem with this approach is that as the number of carbon atoms in a molecule increases, the number of possible molecules generated increases exponentially, requiring enormous computational effort and a very long time, and preventing effective manual intervention to improve efficiency. Furthermore, the structure elucidation tools in the MestReNova NMR analysis software package are difficult to use, making it difficult to successfully obtain correct results. Third, our long-term work experience has shown that the vast majority of structure elucidation work is performed on known compounds, with the discovery and elucidation of entirely new compounds being rare. Summary of the Invention

[0005] In order to overcome the shortcomings of the existing technology, the purpose of the present invention is to provide an automated method and system for compound nuclear magnetic resonance structure analysis, so that non-professionals can also handle more complex structural analysis work. At the same time, it should meet the characteristics of short time and manual intervention.

[0006] To achieve the above object, the present invention provides the following solutions:

[0007] An automated method for nuclear magnetic resonance structure analysis of a compound, comprising:

[0008] S1. Obtain the molecular formula and raw NMR data of the target compound;

[0009] S2. searching the PubChem database for matching candidate compound structures based on the molecular formula;

[0010] S3, using the automatic Fourier transformation of the raw NMR data to automatically extract information such as chemical shift and generate a fragment quantity constraint;

[0011] S4, screening the selected compound structures according to the fragment number constraint to obtain a filtered candidate structure set;

[0012] S5, calling the MestReNova software package to perform 13 C NMR spectrum prediction to obtain prediction data;

[0013] S6. Comparing the predicted data with the measured NMR data to calculate the matching score of each candidate structure;

[0014] S7. Select the candidate structure with the smallest matching score as the final analysis result of the target compound.

[0015] Preferably, the step of generating the fragment quantity constraint condition comprises:

[0016] Performing Fourier transformation on the raw nuclear magnetic resonance data, 1 H and 13 C spectrum for multiplicity analysis, DEPT-135 and HSQC spectrum for peak detection, and extraction of chemical shift values, signal intensity, overlap and related relationship information;

[0017] Automatically map chemical shift values ​​and other information into molecular fragment information through preset rules.

[0018] Preferably, the molecular fragment information includes the number of CH3, the number of CH2, the number of CH, the number of CO carbonyls, the number of CHO aldehyde groups, the number of LB active sites and the number of AR aromatic rings.

[0019] Preferably, the steps of calling the MestReNova software package include:

[0020] Drive the MestReNova API interface through Python scripts to automatically perform Fourier transform of raw data, automatically perform spectrum analysis and obtain fragment quantity constraints, perform compound spectrum prediction and complete structure analysis tasks;

[0021] The following automation functions are realized by using the Qt Script script function of MestReNova: using the transformation template refined by the present invention to automatically perform Fourier transformation and multiplicity analysis or peak detection on the raw data; automatically analyzing the spectrum and extracting information, and then automatically converting the chemical shift value and other information into the fragment number constraint condition according to the preset rules; automatically performing the filtering of the candidate structure set. 13C NMR data are predicted and compared with experimental data; the comparison results are automatically sorted and output for user review and GUI display.

[0022] Preferably, the matching score is calculated as follows:

[0023] For compounds with the same number of predicted signals as the measured signals, the mean absolute error (MAE) of the chemical shift difference between the predicted and measured NMR data was calculated. For compounds with different numbers of predicted signals as the measured signals, zero-filling was performed, which would result in a very large final MAE and therefore excluded them.

[0024] Preferably, a manual run option is provided in all steps, which can be used to manually adjust the MestReNova file and / or the fragment number constraints for structure elucidation to obtain better fragment number constraints and / or filtered candidate structure sets.

[0025] Preferably, the NMR raw data should include at least 1 H NMR, 13 C NMR, DEPT-135 NMR and 2D HSQC NMR spectral data.

[0026] An automated system for nuclear magnetic resonance structure analysis of a compound, comprising:

[0027] A data acquisition unit, used to obtain the molecular formula and raw nuclear magnetic resonance data of the target compound;

[0028] A structure search unit, for searching for matching candidate compound structures in the PubChem database based on the molecular formula;

[0029] a condition generation unit, configured to automatically extract information such as chemical shifts from the raw NMR data and generate a fragment quantity constraint condition;

[0030] a structure screening unit, configured to screen the candidate compound structures according to the fragment quantity constraint to obtain a filtered candidate structure set;

[0031] The atlas prediction unit is used to call the MestReNova software package to perform the filtering on the candidate structure set. 13 C NMR prediction to obtain prediction data;

[0032] a score calculation unit, for comparing the prediction with the measured NMR data and calculating a matching score for each candidate structure;

[0033] The result parsing unit is used to select the candidate structure with the smallest matching score as the final parsing result of the target compound.

[0034] According to the specific embodiments provided by the present invention, the present invention discloses the following technical effects:

[0035] The present invention provides an automated method and system for analyzing the nuclear magnetic resonance structure of a compound, the method comprising: obtaining a molecular formula and raw nuclear magnetic resonance data of a target compound; searching for matching candidate compound structures in a PubChem database based on the molecular formula; automatically extracting information such as chemical shifts from the raw nuclear magnetic resonance data, and automatically generating a fragment number constraint; screening the candidate compound structures according to the fragment number constraint to obtain a filtered candidate structure set; calling the MestReNova software package to analyze the filtered candidate structure set. 13 The method uses a C NMR spectrum to predict and obtain predicted data; compares the predicted data with the measured NMR data to calculate the matching score of each candidate structure; and selects the candidate structure with the smallest matching score as the final elucidation result for the target compound. Through database retrieval, automated screening, and spectrum comparison, the present invention achieves efficient, low-cost, and easy-to-use NMR structure elucidation, completely overcoming the technical bottlenecks of traditional methods, which are computationally intensive, time-consuming, and dependent on experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0037] Figure 1 A flow chart of a method provided by an embodiment of the present invention;

[0038] Figure 2 A schematic diagram of the structure analysis workflow interface provided by an embodiment of the present invention;

[0039] Figure 3 A schematic diagram of the technical route provided for an embodiment of the present invention. DETAILED DESCRIPTION

[0040] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0041] The purpose of the present invention is to provide an automated method and system for nuclear magnetic resonance structure analysis of compounds, which achieves high efficiency, low consumption and ease of use in nuclear magnetic resonance structure analysis, and completely solves the technical bottlenecks of traditional methods such as large computational complexity, long time consumption and reliance on experience.

[0042] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0043] Figure 1 A flow chart of the method provided in the embodiment of the present invention is shown in FIG. Figure 1 As shown, the present invention provides an automated method for analyzing the structure of a compound by nuclear magnetic resonance, comprising:

[0044] S1. Obtain the molecular formula and raw NMR data of the target compound;

[0045] S2. Based on the molecular formula, search for matching candidate compound structures in the PubChem database;

[0046] S3, using automatic Fourier transformation of the original NMR data to automatically extract information including chemical shifts and generate fragment number constraints;

[0047] S4. Screening the selected compound structures according to the fragment number constraint to obtain a filtered candidate structure set;

[0048] S5. Call the MestReNova software package to perform the filtering on the candidate structure set. 13 C NMR spectrum prediction to obtain prediction data;

[0049] S6. Compare the predicted data with the measured NMR data and calculate the matching score of each candidate structure;

[0050] S7. Select the candidate structure with the smallest matching score as the final analysis result of the target compound.

[0051] like Figure 2 and Figure 3 As shown, this embodiment provides an automated solution for chemical structure elucidation based on NMR based on the characteristics of chemical structure elucidation (most of the time, compounds encountered are of known structures, and unknown structures are very rare). It utilizes the huge and constantly updated PubChem chemical substance information database, combined with the chemical shift prediction and scripting functions of the MestReNova software package, and uses Python to write a GUI interface for the entire workflow.

[0052] In this example, instead of using de novo structural fragment assembly, the molecular formula was used to directly search for known structures in the PubChem database, and the NMR data were used to automatically generate partial or complete fragment quantity information, and the search results were filtered. The filtered structures were then analyzed using the MestReNova software. 13 The predicted C spectrum is then compared with the measured results to generate a score for each structure. The structure with the lowest score is considered to be the correct structure corresponding to the molecular formula.

[0053] Preferably, it also includes:

[0054] The solution of this embodiment does not use de novo structural fragment splicing, but directly searches the PubChem database for known structures using molecular formulas, which greatly reduces the computational complexity of structural splicing and the number of possible molecules. This solution also uses the script programming capability of MestReNova software to write the automatic generation of fragment number constraint information for NMR data, filters the search results, and then uses MestReNova software to perform the structure retrieval. 13 The prediction of C spectra greatly reduces the workload of chemical shift prediction calculations. At the same time, the solution will automatically perform Fourier transform on the original data and automatically analyze all spectra to obtain the number of partial (or even complete) fragments contained in the molecule, which is very friendly to non-professionals. Unlike traditional analytical solutions, the molecular probability increases exponentially with the increase of the number of carbon atoms in the molecule. In this solution, as the number of carbon atoms in the molecule increases, the number of compounds obtained from PubChem decreases. The above greatly accelerates the time for structural analysis. This embodiment also supports manual processing at each stage, making effective manual intervention possible and improving the usability of the solution.

[0055] Preferably, the step of generating the fragment quantity constraint condition comprises:

[0056] Performing Fourier transformation on the raw nuclear magnetic resonance data, 1 H and 13 C spectrum for multiplicity analysis, DEPT-135 and HSQC spectrum for peak detection, and extraction of chemical shift values, signal intensity, overlap and related relationship information;

[0057] Information such as chemical shift values ​​is automatically mapped to molecular fragment quantity information through preset rules.

[0058] Preferably, the molecular fragment information includes the number of CH3, the number of CH2, the number of CH, the number of CO carbonyls, the number of CHO aldehyde groups, the number of LB active sites and the number of AR aromatic rings.

[0059] Optionally, the MestReNova API interface is driven by a Python script. In the automated method of this embodiment, the generation of the fragment number constraint is achieved by the following steps:

[0060] NMR data processing:

[0061] Use the script to automatically perform Fourier transformation on all the time domain data in the input NMR raw data folder according to the transformation template provided by the present invention, convert the time domain signal into a frequency domain spectrogram, and 1 H and 13 C spectra were used for multiplet analysis, and DEPT-135 and HSQC spectra were used for peak detection.

[0062] Obtaining the fragment number constraint:

[0063] Extract all experiments with the script ( 1 H, 13 C, DEPT-135, HSQC) are as follows: 1 Extract chemical shift, integral value, signal multiplicity, regularized H number, uuid, kurtosis, and chemical shift span from H spectrum; 13 Extract chemical shift, integral value, and uuid from C spectrum; extract chemical shift, intensity, and uuid from DEPT-135 spectrum; extract chemical shift, intensity, and uuid from HSQC spectrum;

[0064] Further, based on the obtained atlas information, the script is used to optimize and organize the information, as follows: 1. In addition to removing impurity information, a comprehensive analysis is also performed 13 C, DEPT-135, HSQC information, in 1 Add overlapping parameters to H information; 2. In addition to removing impurity information, through comprehensive analysis 13 C, DEPT-135, HSQC information, in 13 Add overlapping parameters to C information; 3. In addition to removing impurity information, through comprehensive analysis 13 C. DEPT-135, HSQC information, add DEPT-135 information 13 C index, symbol, and fragment type parameters; 4. In addition to removing impurity information, comprehensive analysis is also performed 13 C. DEPT-135, HSQC information, add 1 H index, 13 C index, symbol, fragment type parameters;

[0065] Furthermore, complex preset rules are compiled through scripts to achieve the statistics of the number of fragments, and finally the fragment number constraint conditions are output as a JSON format file for subsequent candidate structure screening. The specific preset rules are as follows:

[0066] 1.According to the previous step, obtain all the basic information of the map;

[0067] 2. CH3 criterion a: 1 The H chemical shift is less than 4.2 ppm and the regular H number is divisible by 3 and the peak shape is one of (s, d, t, dd) and the corresponding 13C chemical shift is less than 65.0 ppm;

[0068] 3. CH3 criterion b: 1 The H chemical shift is less than 1.0 ppm and the number of regular H is greater than 3 and the corresponding 13 C chemical shift is less than 20.0 ppm;

[0069] 4. Criteria for aromatic double bond CH: 1H chemical shift is greater than or equal to 6.5 ppm and the number of regular H is greater than or equal to 1 and there is a corresponding signal on HSQC;

[0070] 5. Other CH criteria: the regular H number is equal to 1 and there is a corresponding signal on HSQC;

[0071] 6. Determine the CH2 group with two degenerate H signals: the number of regular H groups can be divided by 2 and 1 The chemical shift of H is less than 6.5 ppm and there is only one corresponding H on HSQC. 13 C signal;

[0072] 7. Find some CH3, CH2, and CH through the above steps, assign values ​​to the corresponding HSQC information and DEPT-135 information fragment type parameters, adjust the symbol parameters, and return the new information for use;

[0073] 8. Using the above steps, reassign the fragment type parameters of the DEPT-135 information: a. If the sign parameter is -1, the fragment type parameter corresponds to CH2; b. If the sign parameter is +1 and the chemical shift is less than 18.0 ppm, the fragment type parameter corresponds to CH3; c. If the sign parameter is +1 and the chemical shift is greater than 80.0 ppm, the fragment type parameter corresponds to CH;

[0074] The specific statistical rules are as follows:

[0075] 1. Get updated information of all graphs as described in the previous step;

[0076] 2. Based on the DEPT-135 information, count the number of fragments according to the fragment type parameter;

[0077] 3. CH3 quantity: a. When 1 H. 13 When the overlap parameters of C are all 1, the number of CH3 can be calculated; b. 1 H or 13 When one of the overlap parameters of C is not 1, CH3 is identified as only one;

[0078] 4. CH2 quantity: a. When 1 H. 13 When the overlap parameters of C are all 1, the number of CH2 can be calculated; b. 1 H or 13 When one of the overlap parameters of C is not 1, CH2 is only identified as 1;

[0079] 5. CH quantity: a. When 1 H. 13 When the overlap parameters of C are all 1, the number of CH can be calculated; b. 1 H or 13 When one of the overlap parameters of C is not 1, CH is only identified as 1;

[0080] 6. Number of LBs: a. When a signal has a regular H number equal to 1 and an overlap parameter equal to 0 and contains N elements, the number of LBs is increased by 1. b. If two signals simultaneously meet the conditions in a, and the distance between them is less than 1.0 ppm and the difference in their kurtosis parameters is less than 5%, the number of LBs is reduced by 1 (in the case of NH2). c. If a signal meets the conditions in a and has a regular H number greater than or equal to 2, the number of LBs is increased by 1. d. If a signal does not contain N and its overlap parameter is equal to 0, the number of LBs is increased by the regular H number of the signal.

[0081] 7. CO Count: a. If the chemical shift is greater than 170 ppm and less than 220 ppm and does not appear in DEPT-135, the CO count is increased by 1. b. If the CO count is greater than the number of oxygen elements in the molecular formula, the CO count is equal to the number of oxygen elements. c. If the CO count is greater than 1 in the final count, the CO count is reduced by 1 (to prevent non-carbonyl carbons of α,β unsaturated carbonyl compounds from entering the statistical range).

[0082] 8. Number of CHO: If the chemical shift is greater than 190 ppm and it appears in DEPT-135, the number of CHO is increased by 1;

[0083] 9. Number of ARs: First, count the characteristic 13C signals (ar): a. If the 13C chemical shift of the signal is greater than 110 ppm and less than 150 ppm and the overlap parameter is equal to 0, the number of ARs is increased by 1. b. If the 13C chemical shift of the signal is greater than 110 ppm and less than 150 ppm and the corresponding 1H chemical shift is greater than 5.77 ppm, the number of ARs is increased by 1. c. If the ar is greater than 6 and the difference between the molecular unsaturation and the number of carbonyl groups is greater than or equal to 7, the number of ARs is 2. d. If the ar is greater than 3 and the difference between the molecular unsaturation and the number of carbonyl groups is greater than or equal to 4, the number of ARs is 1.

[0084] This embodiment generates the following JSON mapping according to preset rules: {"CH3":2,"CH2":0,"CH":3,"AR":1,"CO":0,"LB":1,"CHO":0,"mf":"C8H11N","unsaturations":4,"consistence":{"13C and DEPT information conflict":[],"CH3,CH2,CH identification error":[]}}.

[0085] In this example, a Python script (se_workflow_gui.py) is used to automatically match the fragment quantity constraint with the compound set searched by PubChem to generate a filtered candidate molecule set.

[0086] Optionally, the constraint optimization process of this embodiment is as follows (this embodiment is fully automatic and no optimization is performed):

[0087] If the result set cannot be filtered out, or the final MAE value is large (the minimum MAE value of the candidate compound is >5.0), it indicates that the automatic program may have failed. At this time, the user can manually intervene. The specific process is as follows: 1. Review the specific structure analysis MestReNova file, correct possible errors (such as peak picking and integration problems), manually run step 2 to regain the molecular fragment number constraint, and re-screen. Alternatively, more experienced users can analyze the MestReNova file, directly modify the fragment number constraint JSON file, and re-screen.

[0088] Preferably, a Python script is used to automatically perform chemical shift prediction tasks through the API interface of MestReNova. This process includes the following steps:

[0089] (1) The Python program searches the PubChem library for the molecular formula to obtain an initial set of candidate compounds and saves the relevant information for reference. During this process, the data format is appropriately converted to ensure that the data format remains consistent throughout the program.

[0090] (2) The Python program uses the fragment number constraint to screen the initial set of candidate compounds, obtains a significantly reduced set of filtered candidate compounds, and saves the results in text and sdf file formats.

[0091] (3) Use Python code to construct a request to call the MestReNova API, including setting prediction parameters (such as function selection, function parameter transmission, etc.). After sending the request, MestReNova will read the sdf file of the filtered compound set and generate prediction data one by one.

[0092] (4) Receive and store the prediction results (chemical shift information) returned by MestReNova for subsequent comparison and analysis.

[0093] In this way, the efficiency of chemical shift prediction can be significantly improved, and unnecessary time consumption and possible errors caused by manual operations can be reduced.

[0094] Preferably, the matching score is calculated as follows:

[0095] For compounds with the same number of predicted signals as the measured signals, the mean absolute error (MAE) of the chemical shift difference between the predicted and measured NMR data was calculated. For compounds with different numbers of predicted signals as the measured signals, zero-filling was performed, which would result in a very large final MAE and thus excluded them.

[0096] Preferably, a manual run option is provided in all steps to increase the program's friendliness to experienced users, improve the program's flexibility and efficiency, and reduce the difficulty of error detection. It can also be used to manually adjust the MestReNova file and / or the fragment number constraints for structure elucidation to obtain better fragment number constraints and / or filtered candidate structure sets. Specific manual interventions include but are not limited to:

[0097] (1) Open the automatically generated structure analysis MestReNova file in the working directory, check the correctness of the analysis information such as peak detection, and correct or modify it, then rerun S3 and subsequent steps.

[0098] (2) Use a text editor to open the molecular fragment number constraint JSON file in the working directory. Refer to your own analysis of the spectrum and directly edit the molecular fragment number in the file. Then rerun S4 and subsequent steps.

[0099] By automating the above steps, the entire process of spectrum processing and analysis, obtaining fragment number constraints, and chemical shift prediction and processing is simplified, achieving efficient and accurate compound NMR structure elucidation. This approach not only reduces errors caused by human intervention but also improves work efficiency, especially when dealing with complex compounds. Ultimately, the implementation of this automated method can significantly improve researchers' work efficiency and accuracy in compound structure elucidation.

[0100] Preferably, the NMR raw data should include at least 1 H NMR, 13 C NMR, DEPT-135 NMR and 2D HSQC NMR spectral data.

[0101] Corresponding to the above method, this embodiment also provides an automated system for compound nuclear magnetic resonance structure analysis, comprising:

[0102] A data acquisition unit, used to obtain the molecular formula and raw nuclear magnetic resonance data of the target compound;

[0103] A structure search unit, for searching for matching candidate compound structures in the PubChem database based on the molecular formula;

[0104] a condition generation unit, configured to automatically extract chemical shift information using the raw NMR data and generate a fragment quantity constraint condition;

[0105] a structure screening unit, configured to screen the candidate compound structures according to the fragment quantity constraint to obtain a filtered candidate structure set;

[0106] The atlas prediction unit is used to call the MestReNova software package to perform the filtering on the candidate structure set. 13 C NMR spectrum prediction to obtain prediction data;

[0107] a score calculation unit, configured to compare the predicted data with the measured NMR data and calculate a matching score for each candidate structure;

[0108] The result parsing unit is used to select the candidate structure with the smallest matching score as the final parsing result of the target compound.

[0109] The beneficial effects of the present invention are as follows:

[0110] (1) The present invention directly retrieves known compound structures from the PubChem database, avoiding the complex de novo splicing calculations in traditional methods, shortening the analysis time from several hours to several days to minutes, and is particularly suitable for organic compounds with a molecular weight of 100 to 2000 Da.

[0111] (2) The present invention uses NMR data to automatically generate fragment number constraints, and combines them with the spectrum prediction function of MestReNova to exponentially reduce the screening range of candidate structures and reduce more than 90% of the ineffective calculation amount.

[0112] (3) The present invention realizes full-process automation through a GUI interface written in Python, supports one-click analysis by non-professionals, and allows manual intervention in key steps (such as adjustment of the number of fragments and modification and optimization of the MestReNova file to be parsed), taking into account both automation and flexibility.

[0113] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. Reference can be made to the common and similar parts between the various embodiments. For the systems disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple, and the relevant parts can be referred to the method description.

[0114] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The above examples are only intended to help understand the method and core concept of the present invention. At the same time, those skilled in the art will find that the specific implementation methods and application scopes may vary based on the concept of the present invention. In summary, the contents of this specification should not be construed as limiting the present invention.

Claims

1. An automated method for nuclear magnetic resonance structure analysis of a compound, characterized in that: include: S1. Obtain the molecular formula and raw NMR data of the target compound; S2. searching the PubChem database for matching candidate compound structures based on the molecular formula; S3, using the automatic Fourier transformation of the raw NMR data to automatically extract information including chemical shifts and generate a fragment quantity constraint; S4, screening the selected compound structures according to the fragment number constraint to obtain a filtered candidate structure set; S5, calling the MestReNova software package to perform 13 C NMR spectrum prediction to obtain prediction data; S6. Comparing the predicted data with the measured NMR data to calculate the matching score of each candidate structure; S7. Select the candidate structure with the smallest matching score as the final analysis result of the target compound.

2. The automated method for compound nuclear magnetic resonance structure analysis according to claim 1, characterized in that Also includes: Storing the intermediate results of the analysis process in a local database to improve the efficiency of subsequent chemical shift predictions for compounds with the same molecular formula; The intermediate result is chemical shift prediction information.

3. The automated method for compound nuclear magnetic resonance structure analysis according to claim 1, characterized in that The step of generating the fragment quantity constraint condition includes: Performing Fourier transform on the raw nuclear magnetic resonance data, 1 H and 13 C spectrum for multiplicity analysis, DEPT-135 and HSQC spectrum for peak detection, and extraction of chemical shift values, signal intensity, overlap and related relationship information; Chemical shift values, signal intensities, overlaps and related relationship information are automatically mapped to molecular fragment quantity information through preset rules.

4. The automated method for compound nuclear magnetic resonance structure analysis according to claim 1, characterized in that The information including the molecular fragment quantity constraint condition is the CH3 quantity, the CH2 quantity, the CH quantity, the CO carbonyl quantity, the CHO aldehyde quantity, the LB active site quantity and the AR aromatic ring quantity.

5. The automated method for compound nuclear magnetic resonance structure analysis according to claim 1, characterized in that: The steps to call the MestReNova package include: Drive the MestReNova API interface through Python scripts to automatically perform Fourier transform of raw data, automatically perform spectrum analysis and obtain fragment quantity constraints, perform compound spectrum prediction and complete structure analysis tasks; Including using the Qt Script script function of MestReNova to realize the following automation functions: using the transformation template to automatically perform Fourier transformation and multiplicity analysis or peak detection on the raw data; automatically analyzing the spectrum and extracting information, and then automatically converting the chemical shift value and other information into the fragment number constraint condition through the preset rules; automatically performing the filtering of the candidate structure set. 13 C NMR data are predicted and compared with experimental data; the comparison results are automatically sorted and output for user review and GUI display.

6. The automated method for compound nuclear magnetic resonance structure analysis according to claim 1, characterized in that: The matching score is calculated as follows: For compounds with the same number of predicted signals and measured signals, the mean absolute error of the chemical shift difference between the predicted data and the measured NMR data was calculated; for compounds with different numbers of predicted signals and measured signals, zero filling was performed.

7. The automated method for compound nuclear magnetic resonance structure analysis according to claim 2, characterized in that: The updating step of the local database includes: The prediction results in the analysis process are automatically associated with part of the data in the PubChem database obtained during the analysis process using Python programs and Qt Scritp scripts, and are inserted into or updated in the local database.

8. The automated method for compound nuclear magnetic resonance structure analysis according to claim 1, characterized in that: A manual run option is provided at all steps to manually adjust the MestReNova file and / or fragment number constraints for structure elucidation to obtain better fragment number constraints and / or filtered candidate structure sets.

9. The automated method for compound nuclear magnetic resonance structure analysis according to claim 1, characterized in that: The nuclear magnetic resonance raw data at least includes 1 H NMR, 13 C NMR, DEPT-135 NMR and 2D HSQC NMR spectral data.

10. An automated system for analyzing compound nuclear magnetic resonance structures, characterized in that: include: A data acquisition unit, used to obtain the molecular formula and raw nuclear magnetic resonance data of the target compound; A structure search unit, for searching for matching candidate compound structures in the PubChem database based on the molecular formula; a condition generation unit, configured to automatically extract information including chemical shifts using the raw NMR data and generate a fragment quantity constraint condition; a structure screening unit, configured to screen the candidate compound structures according to the fragment quantity constraint to obtain a filtered candidate structure set; The atlas prediction unit is used to call the MestReNova software package to perform the filtering on the candidate structure set. 13 C NMR prediction to obtain prediction data; a score calculation unit, for comparing the prediction with the measured NMR data and calculating a matching score for each candidate structure; The result parsing unit is used to select the candidate structure with the smallest matching score as the final parsing result of the target compound.

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