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Drug-target interaction prediction method based on graph convolution and word vector

A prediction method and word vector technology, applied in chemical process analysis/design, chemical statistics, molecular design, etc., can solve problems such as limited prediction ability, not well covered interaction spectrum, difficult drug and protein feature construction, etc. , to achieve the effect of reducing time, high accuracy, and speeding up training time

Active Publication Date: 2019-09-27
HUNAN UNIV
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Problems solved by technology

Among them, the method based on feature and similarity defines this task as a binary classification problem, which does not cover the entire interaction spectrum well, while the method using gradient boosting is difficult to construct drug and protein features and has limited predictive ability

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  • Drug-target interaction prediction method based on graph convolution and word vector
  • Drug-target interaction prediction method based on graph convolution and word vector
  • Drug-target interaction prediction method based on graph convolution and word vector

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Embodiment Construction

[0060] 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.

[0061] see figure 1As shown, the present invention provides a drug-target interaction prediction method based on graph convolution and word vectors, which can be applied to the fields of drug discovery, molecular structure, computational geometry, etc., including the following steps:

[0062] Step 1. Construct a data set, split the data set, and generate 80% of the training set and 20% of the test set;

[0063] Specifically, the data set adopts the Davis data...

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Abstract

The invention provides a drug-target interaction prediction method based on graph convolution and word vectors. The method comprises the steps of: extracting molecular fingerprint features and adjacency matrix features from the medicine; training the features by utilizing graph convolution, cutting a protein molecular expression into groups, expressing the groups by utilizing a 100-dimensional vector, training word vector characteristics of the target by utilizing the CNN, and finally, combining the trained drug and the target together to carry out final result prediction. The method has the beneficial effects that more characteristics related to the medicine can be provided, so that higher accuracy is achieved; protein features are constructed by utilizing the word vectors, so that the feature construction time is greatly shortened; related information of a drug molecular diagram can be completely stored without losing characteristics; training time can be greatly shortened.

Description

【Technical field】 [0001] The invention relates to the technical field of drug-target interaction prediction, in particular to a drug-target interaction prediction method based on graph convolution and word vectors. 【Background technique】 [0002] The key to modern new drug development is to find, determine and prepare drug molecular targets. One of the important prerequisites for a compound to become a drug is that the binding force with the target protein is strong enough and the action time is long enough. However, in recent years, the rate of research and development of new drugs has been declining, and the cost of research and development has been rising. The reasons are: 1. The early stage of drug research and development relies on time-consuming and labor-intensive experimental methods; 2. Most human diseases are complex diseases caused by multiple factors, while biological systems have certain redundancy and robustness. Drug interference with a single target cannot ...

Claims

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Application Information

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IPC IPC(8): G16C20/10G16C20/50G16C20/70
CPCG16C20/10G16C20/50G16C20/70Y02A90/10
Inventor 全哲郭燕林轩何楠王梓旭
Owner HUNAN UNIV
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