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Natural product active ingredient computation and recognition method based compound characteristic

A natural product and active ingredient technology, applied in the field of identification of natural product biological activity, can solve the problem of not finding a fast and effective method to test whether natural products have biological activity

Inactive Publication Date: 2009-07-08
ZHEJIANG UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0009] Unfortunately, the results of the literature search showed that before the completion of the present invention, no rapid and effective methods for testing whether natural products have biological activity were found in the existing natural product research

Method used

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  • Natural product active ingredient computation and recognition method based compound characteristic
  • Natural product active ingredient computation and recognition method based compound characteristic
  • Natural product active ingredient computation and recognition method based compound characteristic

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0110] Example 1: Ginsenoside series molecules

[0111] 1. Background introduction

[0112] Ginsenosides are one of the main active ingredients of ginseng. So far, more than 40 kinds of ginsenoside monomers have been isolated from ginseng plants, which are named R0, Ra, Rb1, Rb2, Rb3, Rc according to the Rf value of ginsenosides in TLC. , Rd, Re, Rf, Rg1, Rg2, Rg3…. Ginsenosides belong to triterpenoid saponins and can be divided into three categories: one is diol type, such as ginsenosides Rb1, Rb2, Rc, Rd, Rh2, etc.; the other is triol type, such as ginsenosides Re, Rf, Rg1, Rg2 , Rh1 and so on; the third is oleanolic acid type, such as ginsenoside R0, Rh3 and so on. Among them, diol and triol saponins account for the majority of ginsenosides and are considered to be the most important active components of ginseng. In addition to ginsenosides, there are many other components in ginseng: such as sugars, alkaloids, amino acids, polypeptides, vitamins, trace elements, enzyme...

Embodiment 2

[0150] Example 2: Ginkgo biloba extract

[0151] 1. Background introduction:

[0152] The results of modern medical research show that there are two kinds of important physiologically active substances in Ginkgo biloba leaves—flavonoids and ginkgolides and bilobalide, which have the functions of trapping free radicals, inhibiting platelet activating factor (PAF), Promote blood circulation and brain metabolism and other functions, and have a unique effect on middle-aged and elderly coronary heart disease, hypertension and other cardiovascular and cerebrovascular diseases. The molecules with molecular structures in the Ginkgo biloba extract we collected are shown in Table 5:

[0153] Table 5: Names of Ginkgo Biloba Extract Molecules

[0154] Ginkgolide C Ginkgolide C Pinitol Pinitol Ginkgolide B Ginkgolide B proanthocyanidins PROCYANIDIN Ginkgolide J Ginkgolide J Quercetin Quercet in Ginkgo biloba Ginkgetin quinic acid Quinic ac...

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Abstract

The invention discloses a method with a strong discrimination power, which is used for calculating and recognizing active ingredient of a natural product on the basis of compound characteristics. The method is characterized in that whether a molecule has biological activity or not can be calculated and recognized in an extractive molecule of the natural product with a known structure by utilizing descriptors of compound characteristics. The method comprises the following steps: step A, a training set and a predicting set of natural product extractive molecules used for model building are structured; step B, molecular structure files of the training set and the predicting set are collected; step C, the descriptors of the compound characteristics can be figured out by utilizing the molecular structure files; step D, classification modeling can be performed on the training set by using a machine learning software according to the descriptors of the compound characteristics; and step E, the molecule of the predicting set has biological activity or not can be identified by the machine learning software with the combination of step D. The method of the invention has a good application prospect in high flux virtual screening and the study of synergistic effect of natural products.

Description

technical field [0001] The present invention belongs to the technical field of identification methods for natural product biological activity, and in particular relates to a method for calculating whether the molecule has biological activity or not by using compound feature descriptors from natural product extract molecules of known structure. Background technique [0002] Natural products, especially the active components of natural medicinal plants, are one of the most important sources of lead compounds in the modern drug development process. Derivatives of natural products also occupy a considerable part of the drugs with the largest sales in the world drug market. So far, active components of natural products from many sources have been widely used in screening experiments for potential drug efficacy. Scientists have discovered some new mechanisms of action, which they have subsequently expanded into new therapeutic strategies. However, the ability to discover a clear...

Claims

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Application Information

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IPC IPC(8): G06F19/00G06F17/50G06K9/62G06N1/00G06F19/24
Inventor 陈新周犀胡彬
Owner ZHEJIANG UNIV
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