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Multi-source information medicine screening method based on knowledge graph

A knowledge graph and multi-source information technology, applied in the field of drug screening, can solve the problems of ignoring non-structural information and ligand expansion information, reducing the accuracy and reliability of docking results, and weak correlation in subsequent steps. The effect of improving the degree and reliability, being conducive to screening, and improving the utilization of computing resources

Active Publication Date: 2020-02-28
OCEAN UNIV OF CHINA
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AI Technical Summary

Problems solved by technology

[0006] 1. Traditional docking methods based on receptors or ligands only consider the structural information of the target and the compound, ignoring the non-structural information and the extended information between the ligands, resulting in the accuracy of the docking results and reliability is greatly reduced
[0007] 2. When there are many molecules, it is difficult to select molecules according to the docking score
For high-throughput virtual screening, it is often possible to generate a large number of screening results. Using a single feature for screening, the corresponding time cost is too high and the accuracy is not high. Therefore, for a large number of screening results obtained, how to further screen efficiently becomes a question
[0008] 3. The existing virtual screening method only generates a scoring result, which is only used in this screening stage and is not strongly related to the subsequent steps of drug development

Method used

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  • Multi-source information medicine screening method based on knowledge graph
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Embodiment Construction

[0045] The present invention will be further described below in conjunction with the accompanying drawings and specific embodiments.

[0046] Such asfigure 1 As shown, the multi-source information drug screening method based on knowledge graph provided in this embodiment includes the following steps:

[0047] 1) Fusion of multi-source heterogeneous data sources

[0048] According to the characteristics of different data, artificial screening and semi-supervised machine learning methods are used to extract structured and semi-structured data downloaded from various pharmacy-related websites through knowledge extraction technology from a large number of pharmacy-related literature. Various types of entity information and information of various types of relationships between entities are established and integrated with each other.

[0049] 2) Optimize molecular structure

[0050] Transform the compound molecules in the knowledge graph system from two-dimensional structure to th...

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Abstract

The invention belongs to the technical field of medicine screening, and discloses a multi-source information medicine screening method based on a knowledge graph, which comprises the following steps of: fusing multisource heterogeneous data sources; optimizing a molecular structure; optimizing a scoring process architecture; carrying out molecular docking and scoring; constructing a knowledge graph entity; constructing a knowledge graph entity association relationship; screening retrieval. According to the method, optimized three-dimensional molecular structures, scored and screened molecularinformation and other information are organized into the knowledge graph, and loop retrieval is performed in the knowledge graph, so that drug screening is more efficient and higher in accuracy.

Description

technical field [0001] The invention belongs to the technical field of drug screening, in particular to a multi-source information drug screening method based on a knowledge graph. Background technique [0002] The knowledge graph is essentially a semantic network, a graph-based data structure consisting of nodes (Point) and edges (Edge). In the knowledge graph, each node represents an "entity" that exists in the real world, and each edge is a "relationship" between entities. Knowledge graphs are the most effective representation of relationships. In layman's terms, a knowledge graph is a relational network obtained by connecting all different types of information (Heterogeneous Information). The knowledge graph provides the ability to analyze problems from the perspective of "relationship". The application field of knowledge graph is constantly expanding and the effect achieved is amazing. [0003] Multi-source heterogeneous data means that the data describing the same ...

Claims

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Application Information

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IPC IPC(8): G06F16/36G16C20/50G16C20/64
CPCG16C20/50G16C20/64G06F16/367
Inventor 刘昊高春晓魏志强
Owner OCEAN UNIV OF CHINA
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