CN110322945B - Intelligent screening method for plant compounding - Google Patents
Intelligent screening method for plant compounding Download PDFInfo
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Abstract
The invention discloses an intelligent screening method for plant compounding, which comprises the steps of compatibility of traditional Chinese medicine components of plants, high-throughput combination of western medicines, composite compatibility, primary screening and secondary screening by utilizing big data processing. The intelligent screening method for plant compounding organically integrates the compatibility rule of traditional Chinese medicine formulas and the concept essence of western medicine combined high-throughput screening platform technology, technically creates a breakthrough in a multi-level innovation manner, accurately positions the use dosage of each plant extract in the formula through multiple rounds of screening, and deeply researches the mechanism of the plant extract to the molecular and cell biology level.
Description
Technical Field
The invention relates to the technical field of plant compounding, in particular to an intelligent screening method for plant compounding.
Background
In traditional Chinese medicine, when one medicine is not good in curative effect, other medicines need to be selected for reasonable compatibility or two effective medicines need to be matched together, so that the effect of 1+1 >. Compatibility means that more than two herbs are selectively combined for use according to the disease condition and property of herb.
Two or more kinds of herbs are selectively combined according to the disease condition and property of herbs. The disease is treated primarily with a single drug. With the increasing variety of medicines, the characteristics of the medicine properties are constantly and clearly defined, the medicine is used from simple to complex, and a method for matching and applying a plurality of medicines appears. The compatibility of medicines can not only take care of complex disease conditions, but also enhance curative effect and reduce toxic and side effects, so the medicines are widely adopted. The "Shen nong Ben Cao Jing" in the West Han dynasty summarized the rule of the compatibility of Chinese herbs, and pointed out that: those who have single file, mutual reinforcement, mutual assistance, mutual aversion, mutual antagonism, mutual killing, so that they are in harmony with each other. Except for single action, the actions of mutual reinforcement, mutual induction, mutual restraint, mutual killing, mutual aversion and contraindications belong to the category of compatibility and application of herbs.
However, in Western medicine, in order to achieve the drug effect, reasonable combination of high-flux drugs is generally carried out to achieve the effect of 1+1 >.
However, the screening method for compounding plant medicines in the prior art is not scientific and efficient, and has the problem that the optimal matching or proportion cannot be found. In view of this, how to provide an intelligent screening method for plant compounding is a technical problem that needs to be solved by those skilled in the art.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides an intelligent screening method for plant compounding, and aims to solve the problems that the screening method for plant medicine compounding in the prior art is not scientific and efficient enough and the optimal matching or proportion cannot be found all the time.
The intelligent screening method for plant compounding provided by the invention comprises the following steps:
the method comprises the following steps: the compatibility of the traditional Chinese medicine components of the plants is as follows: firstly, respectively measuring the using dosage range of the biological effect of a single compound of each component of the plant traditional Chinese medicine to be compatible; then, according to the measured single biological effect dosage of each component of the Chinese herbal medicine of the plant to be matched, the components of the Chinese herbal medicine of the plant to be matched are combined randomly to form a mixed solution, the mixed solution is prepared into a mixed solution with an equal effect ratio, and the biological effect concentration of the mixed solution is measured; then, calculating a combined biological effect index TU 1: 1 at an equal effect ratio according to the biological effect concentration of the mixed solution; finally, the optimal biological effect point of the combined biological effect is determined when the effect ratios are equal;
step two: western medicine high-throughput combination: respectively detecting the flux of western medicines to be combined by adopting a fluorescence quenching method;
step three: compounding and matching: firstly, the combination obtained in the first step and the second step takes the mass percentage of each component as a variable factor, and a uniform design method is adopted to construct a difference sample with different feeding proportions of each component; secondly, acquiring pharmacodynamic data of each difference combination through pharmacodynamic data, analyzing the relevance between the content of each component of each difference sample and the pharmacodynamic effect, and determining the contribution of each component to the pharmacodynamic effect and the primary and secondary relations of each component;
step four: screening for the first time: selecting components which have large contribution ratio to the drug effect and are in main relation in each combination in the third step, and screening out a combination scheme with low activity of not less than half of flux by using seven standard strains and a fluorescence detection method or a flux detection method with sterilization or bacteriostasis lag according to flux data;
the fifth step: big data processing: and constructing a big data processing model, and finding out an optimal compatibility result from a plurality of combinations by using the big data processing model to determine the activity range of the medicament.
Preferably, the compatibility of the Chinese herbal medicine ingredients of the plants comprises the following steps:
step (1): determination of the concentration of the biological Effect of a Single Compound
According to a test method of GB/T15441-1995, using photobacterium as an indicator organism to respectively determine single toxicity of Chinese herbal medicine components of plants such as A, B and C;
step (2): determination of isoeffect time-combining biological effects
According to the single biological effect concentration of the plant traditional Chinese medicine components such as A, B and C.the like obtained by determination, the plant traditional Chinese medicine components are combined randomly and respectively prepared into a mixed solution with equal effect ratio; the testing method is that according to the testing method of GB/T15441-1995, the biological effect concentration of the mixed solution is respectively measured, and then the combined biological effect index TU 1: 1 when the effect ratio is equal is respectively calculated;
and (3): determination of optimal biological Effect Point
And (3) respectively measuring the combined biological effect change rule of the mixed system according to the measuring result TU 1: 1 of the combined biological effect at the equal effect ratio obtained by calculation in the step (2), and obtaining a plurality of optimal biological effect points.
8. The intelligent screening method for plant compounding of claim 1, wherein the compound recipe comprises the following steps:
step (1): solution preparation: the method comprises the steps of preparing a sample solution, preparing a positive control solution, preparing a negative control solution and preparing a fluorescence generation reaction solution;
step (2): preparation of microarray chips: spotting the reference substance solution and the sample solution on a thin-layer prefabricated silica gel plate;
and (3): acquisition of fluorescence quenching image: atomizing the microarray chip by using a fluorescence generation reaction solution, placing the microarray chip in a thin-layer image viewer, exposing and collecting an image;
and (4): data processing: and (4) introducing the collected image into TLC analysis software for pixel analysis, and screening out antioxidant activity.
Preferably, the optical density of the suspension is used as an index in the fourth step.
Preferably, the screening of step five is mainly performed by linear testing and analysis of 3-6 dose points.
Preferably, the seven standard strains in the fourth step include seven standard mold strains or seven standard fungal cell strains.
Preferably, the big data processing model comprises the following steps:
step S1: classifying the excavation target;
step S2: performing rapid feature selection on the whole data set by using information gain;
and step S3: extracting a small amount of data from the data set to establish a training set and a verification set;
and step S4: primarily selecting an effective data mining algorithm and a parameter combination thereof;
step S5: expanding the samples of the training set, and selecting effective parameter combinations of each algorithm by using a Bayesian optimization method;
step S6: expanding the samples of the training set to the whole data set, and selecting an optimal data mining algorithm K;
step S7: selecting and determining a parameter value combination of a data mining algorithm K by utilizing cross validation to obtain a final model;
step S8: if the result obtained by the model is poor, repeating the steps S2-S7, and reselecting the optimal model until the model result is satisfactory; and if the result obtained by the model is satisfactory, outputting the model and ending the process of automatically selecting the model.
The invention has the following beneficial effects:
(1) The intelligent screening method for plant compounding organically integrates the compatibility rule of traditional Chinese medicine formulas and the concept essence of western medicine combined high-throughput screening platform technology, creates a breakthrough in technology in a multi-layer mode, accurately positions the use dosage of each plant extract in the formula through multiple rounds of screening, and deeply researches the mechanism of the plant extract to the molecular and cell biology level.
(2) The intelligent screening method for plant compounding disclosed by the invention is based on the compatibility rule data of the traditional Chinese medicine formulas, and continuously verifies, supplements and integrates random combination data screened by a research and development platform to form a world-initiated brand-new plant compounding big database; and then an algorithm model is established by applying an artificial intelligence method, and the database is analyzed, managed and applied, so that the intelligent screening efficiency is higher and higher, and the biological function is optimized more and more until the optimal formula is found.
(3) The intelligent screening method for plant compounding provided by the invention subverts the traditional mode of plant compounding research and development, firstly uses modern scientific means such as molecular biology, big data artificial intelligence and the like, completes the work of screening the optimal plant formula from thousands of plants in a laboratory, provides a brand new cubic methodology for modern plant compounding, improves the efficiency of the plant formula by hundreds of times, creates a new mode of plant compounding research and development, and has extremely high academic and application values.
Additional aspects and advantages of the invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention.
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In order to more clearly illustrate the detailed description of the invention or the technical solutions in the prior art, the drawings that are needed in the detailed description of the invention or the prior art will be briefly described below. Throughout the drawings, like elements or portions are generally identified by like reference numerals. In the drawings, elements or portions are not necessarily drawn to scale.
FIG. 1 is a flow chart of an intelligent screening method for plant compounding according to an embodiment of the present invention;
FIG. 2 is a schematic flow chart of a method for preparing herbal constituents of a plant according to an embodiment of the present invention;
fig. 3 is a schematic flow chart of a plant composite compatibility method according to an embodiment of the present invention.
Detailed Description
Embodiments of the present invention will be described in detail below with reference to the accompanying drawings. The following examples are only for illustrating the technical solutions of the present invention more clearly, and therefore are only examples, and the protection scope of the present invention is not limited thereby.
It is to be noted that, unless otherwise specified, technical or scientific terms used herein shall have the ordinary meaning as understood by those skilled in the art to which the present invention belongs.
A VR based drawing and printing system according to an embodiment of the present invention is described below with reference to fig. 1.
As shown in fig. 1, the intelligent screening method for plant compounding of the embodiment of the invention comprises the following steps:
the method comprises the following steps: the compatibility of the traditional Chinese medicine components of the plants is as follows: firstly, respectively measuring the using dosage range of the biological effect of a single compound of each component of the plant traditional Chinese medicine to be compatible; then, according to the single biological effect use dosage in each component of the Chinese herbal medicine of the plant to be matched, the random combination of each component of the Chinese herbal medicine of the plant to be matched forms a mixed solution, the mixed solution is respectively prepared into mixed solutions with equal effect ratio, and the biological effect concentration of the mixed solution is respectively measured; then, calculating a combined biological effect index TU 1: 1 at an equal effect ratio according to the biological effect concentration of the mixed solution; finally, the optimal biological effect point of the combined biological effect is determined when the effect ratios are equal;
step two: western medicine high-flux combination: respectively detecting the flux of western medicines to be combined by adopting a fluorescence quenching method;
step three: compounding and matching: firstly, the combination obtained in the first step and the second step takes the mass percentage of each component as a variable factor, and a uniform design method is adopted to construct a difference sample with different feeding proportions of each component; secondly, acquiring pharmacodynamic data of each difference combination through pharmacodynamic data, analyzing the relevance between the content of each component of each difference sample and the pharmacodynamic effect, and determining the contribution of each component to the pharmacodynamic effect and the primary and secondary relations of each component;
step four: screening for the first time: selecting components which have large contribution ratio to the drug effect and are in main relation in each combination in the third step, and screening out a combination scheme with low activity of not less than half of flux by using seven standard strains and a fluorescence detection method or a flux detection method with sterilization or bacteriostasis lag according to flux data; and, preferably, the optical density of the suspension can be used as an index.
The fifth step: big data processing: constructing a big data processing model, finding out the optimal compatibility result from a plurality of combinations by using the big data processing model to determine the activity range of the medicament, and preferably, screening a linear test and analysis with 3-6 dosage points as a main method.
Specifically, referring to fig. 2, the following two herbal medicines a and B are taken as examples, in the intelligent screening method for herbal compounding according to the embodiment of the present invention, the compatibility of herbal medicines may include a determination test method of biological effect concentration of a single compound, according to GB/T15441-1995, using leucinobacillus luminescens as an indicator organism, and determining single toxicity of the two types of compounds a and B, the biological effect of the single compound is represented by EC50, and the biological effect concentrations of the two single compounds are EC50-a and EC50-B, respectively; preparing a mixed solution with equal effect ratio according to single biological effect concentrations EC50-A and EC50-B of the A and B compounds obtained by determination, namely the concentrations of the A and B compounds in the mixed solution are respectively EC50-A and EC50-B, determining the biological effect concentration of the mixed solution according to GB/T15441-1995 by a test method, obtaining the concentrations CA and CB of the A and B in the mixed system when the biological effect inhibition of the mixed system is 50%, and then calculating to obtain a combined biological effect index TU 1: 1 when the equal effect ratio is obtained; and (4) determining the combined biological effect change rule of the binary mixed system according to the determination result TU 1: 1 of the combined biological effect at the equal effect ratio obtained by calculation in the step, and obtaining the optimal biological effect point.
Referring to fig. 3, for the composite compatibility, the present embodiment may include the following steps: step (1): solution preparation: the method comprises the steps of preparing a sample solution, preparing a positive control solution, preparing a negative control solution and preparing a fluorescence generation reaction solution; step (2): preparation of microarray chips: spotting the reference substance solution and the sample solution on a thin-layer prefabricated silica gel plate; and (3): acquisition of fluorescence quenching image: atomizing the microarray chip with the reaction solution generated by fluorescence, placing the microarray chip in a thin-layer image viewer, exposing and collecting images; and (4): data processing: and (4) introducing the collected image into TLC analysis software for pixel analysis, and screening out antioxidant activity.
Specifically, the big data processing model in the embodiment of the present invention includes the following steps:
step S1: classifying the excavation target;
step S2: performing rapid feature selection on the whole data set by using information gain;
and step S3: extracting a small amount of data from the data set to establish a training set and a verification set;
and step S4: primarily selecting an effective data mining algorithm and a parameter combination thereof;
step S5: expanding the samples of the training set, and selecting effective parameter combinations of each algorithm by using a Bayesian optimization method;
step S6: expanding the samples of the training set to the whole data set, and selecting an optimal data mining algorithm K;
step S7: selecting and determining a parameter value combination of a data mining algorithm K by using cross validation to obtain a final model;
step S8: if the result obtained by the model is poor, repeating the steps S2-S7, and reselecting the optimal model until the model result is satisfactory; and if the result obtained by the model is satisfactory, outputting the model and ending the process of automatically selecting the model.
The intelligent screening method for plant compounding organically integrates the compatibility rule of traditional Chinese medicine formulas and the concept essence of western medicine combined high-throughput screening platform technology, creates a breakthrough in technology in a multi-layer mode, accurately positions the use dosage of each plant extract in the formula through multiple rounds of screening, and deeply researches the mechanism of the plant extract to the molecular and cell biology level. The intelligent screening method for plant compounding is based on the compatibility rule data of the traditional Chinese medicine formulas, and the random combination data screened by the research and development platform are continuously verified, supplemented and integrated to form a world-initiated brand-new large plant compounding database; and then an algorithm model is established by applying an artificial intelligence method, and the database is analyzed, managed and applied, so that the intelligent screening efficiency is higher and higher, and the biological function is optimized more and more until the optimal formula is found. The intelligent screening method for plant compounding subverts the traditional mode of plant compounding research and development, firstly uses modern scientific means such as molecular biology, big data artificial intelligence and the like to complete the work of screening the optimal plant formula from thousands of plants in a laboratory, provides a brand new cubic methodology for modern plant compounding, improves the efficiency of the plant formula by hundreds of times, creates a new mode of plant compounding research and development, and has extremely high academic and application values.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; such modifications and substitutions do not depart from the spirit and scope of the embodiments of the present invention, and they should be construed as being covered by the appended claims and their equivalents.
Claims (7)
1. An intelligent screening method for plant compounding is characterized by comprising the following steps:
the method comprises the following steps: the compatibility of the traditional Chinese medicine components of the plants is as follows: firstly, respectively measuring the using dose range of the biological effect concentration of a single compound of each component of the traditional Chinese medicine of the plant to be compatible; then, according to the single biological effect use dosage in each component of the Chinese herbal medicine of the plant to be matched, the random combination of each component of the Chinese herbal medicine of the plant to be matched forms a mixed solution, the mixed solution is respectively prepared into mixed solutions with equal effect ratio, and the biological effect concentration of the mixed solution is respectively measured; then, calculating a combined biological effect index TU 1: 1 at an equal effect ratio according to the biological effect concentration of the mixed solution; finally, the optimal biological effect point of the combined biological effect is determined when the equal effect ratio is determined;
step two: western medicine high-flux combination: respectively detecting the flux of western medicines to be combined by adopting a fluorescence quenching method;
step three: compounding and matching: firstly, the combination obtained in the first step and the second step takes the mass percentage of each component as a variable factor, and a uniform design method is adopted to construct a difference sample with different feeding proportions of each component; secondly, acquiring pharmacodynamic data of each difference combination through pharmacodynamic data, analyzing the relevance between the content of each component of each difference sample and the pharmacodynamic effect, and determining the contribution of each component to the pharmacodynamic effect and the primary and secondary relations of each component;
step four: screening for the first time: selecting components which have large contribution ratio to the drug effect and are in main relation in each combination in the third step, and screening out a combination scheme with low activity of not less than half of flux by using seven standard strains and a fluorescence detection method or a flux detection method with sterilization or bacteriostasis lag according to flux data;
the fifth step: big data processing: and constructing a big data processing model, and finding out the optimal compatibility result from the multiple combinations by using the big data processing model to determine the range of the pharmaceutical activity.
2. The intelligent screening method for plant compounding of claim 1, wherein the steps of the compatibility of the plant traditional Chinese medicine components are as follows:
step (1): determination of the concentration of the biological Effect of a Single Compound
According to a test method of GB/T15441-1995, using photobacterium as an indicator organism to respectively determine single toxicity of Chinese herbal medicine components of plants such as A, B and C;
step (2): determination of isoeffect time-combining biological effects
According to the single biological effect concentration of the plant traditional Chinese medicine components such as A, B and C.the like obtained by determination, the plant traditional Chinese medicine components are combined randomly and respectively prepared into a mixed solution with equal effect ratio; the testing method is that according to the testing method of GB/T15441-1995, the biological effect concentration of the mixed solution is respectively measured, then the combined biological effect index TU 1: 1 when the effect ratio is equal is respectively calculated;
and (3): determination of the point of optimum bioeffect
And (3) respectively measuring the combined biological effect change rule of the mixed system according to the measuring result TU 1: 1 of the combined biological effect at the equal effect ratio obtained by calculation in the step (2), and obtaining a plurality of optimal biological effect points.
3. The intelligent screening method for plant compounding of claim 1, wherein the compounding comprises the following steps:
step (1): solution preparation: the method comprises the steps of preparing a sample solution, preparing a positive control solution, preparing a negative control solution and preparing a fluorescence generation reaction solution;
step (2): preparation of microarray chips: spotting the reference substance solution and the sample solution on a thin-layer prefabricated silica gel plate;
and (3): acquisition of fluorescence quenching image: atomizing the microarray chip by using a fluorescence generation reaction solution, placing the microarray chip in a thin-layer image viewer, exposing and collecting an image;
and (4): data processing: and (4) introducing the collected image into TLC analysis software for pixel analysis, and screening out the antioxidant activity.
4. The intelligent screening method for plant compounding of claim 1, wherein the optical density of the suspension is used as an index in the fourth step.
5. The intelligent screening method for plant compounding of claim 1, wherein the screening of step five is mainly based on linear testing and analysis of 3-6 dose points.
6. The intelligent screening method for plant compounding of claim 1, wherein the seven standard strains in the fourth step comprise seven standard mold strains or seven standard fungal cell strains.
7. The intelligent screening method for plant compounding of claim 1, wherein the big data processing model comprises the following steps:
step S1: classifying the excavation target;
step S2: performing rapid feature selection on the whole data set by using information gain;
and step S3: extracting a small amount of data from the data set to establish a training set and a verification set;
and step S4: primarily selecting an effective data mining algorithm and a parameter combination thereof;
step S5: expanding the samples of the training set, and selecting effective parameter combinations of each algorithm by using a Bayesian optimization method;
step S6: expanding the samples of the training set to the whole data set, and selecting an optimal data mining algorithm K;
step S7: selecting and determining a parameter value combination of a data mining algorithm K by using cross validation to obtain a final model;
step S8: if the result obtained by the model is poor, repeating the steps S2-S7, and reselecting the optimal model until the model result is satisfactory; and if the result obtained by the model is satisfactory, outputting the model and ending the process of automatically selecting the model.
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