Graph convolution drug pair interaction prediction method and system based on knowledge graph

A knowledge map and prediction method technology, applied in the field of drug pair interaction prediction, can solve problems such as the scarcity of multi-type label data, ignoring the potential relationship between drugs and other entities, and the difficulty of obtaining batch training of data samples with reasonable distribution. Short-term, high-accuracy effects

CN111564186APending Publication Date: 2020-08-21HUNAN UNIV
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Publication Date
2020-08-21

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Abstract

The invention provides a graph convolution drug pair interaction prediction method based on a knowledge graph. The method comprises the following steps: extracting drug pair data samples, and generating a data set comprising a training set, a verification set and a test set; constructing a knowledge graph corresponding to the data set; establishing a GCN drug pair interaction prediction model, andlearning feature information of drugs contained in the drug pair and neighborhoods of the drugs; inputting the drug pair data samples of the training set into the GCN drug pair interaction predictionmodel, and training the GCN drug pair interaction prediction model; optimizing a loss function of a training result, and then sending the training result to the GCN drug pair interaction prediction model for training; conducting iterative computation to complete the training of the GCN drug pair interaction prediction model; inputting the drug pair data samples in the test set into the GCN drug pair interaction prediction model to obtain a test result; and analyzing the test result to obtain a prediction result. The knowledge graph-based graph convolution drug pair interaction prediction method and system provided by the invention are high in accuracy and short in training time.
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Description

Technical field

[0001] The present invention relates to the technical field of drug pair interaction prediction, in particular to a knowledge map-based graph convolution drug pair interaction prediction method and system.

Background technique

[0002] Drug R&D is a high-investment and high-risk field. It takes an average of 10-15 years for a drug to go from R&D to marketing, with an average investment of about 2.6 billion US dollars, and on average, only one of every 5,000-10,000 molecules entering the R&D pipeline can Successfully developed. The use of computing methods can greatly reduce costs. Relying on data-driven machine learning methods, training models through a large number of open source experimental data and predicting downstream tasks has become a hot spot in the field of drug discovery as an auxiliary computing.

[0003] In related technologies, machine learning algorithms are widely used in the prediction of drug-drug interactions. Most of the existing ar...

Examples

Embodiment Construction

[0040] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some, not all, embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

[0041] see figure 1 , the present invention provides a graph convolution drug pair interaction prediction method based on knowledge graph, comprising the following steps:

[0042] S1: Extract medicine pair data sample, generate data set, described data set comprises training set, verification set and test set.

[0043] The proportion of the training set in the data set is 80%, the proportion of the verification set is 10%, and the proportion of the test set i...