Method for predicting hepatotoxicity caused by drug interaction based on graph neural network model

A neural network model and neural network technology are applied in the field of predicting hepatotoxicity caused by drug interaction based on graph neural network model, so as to reduce pain, improve success rate and reduce investment.

Active Publication Date: 2022-07-26
PRECISION SCI BIOMEDICINE (SUZHOU) CO LTD +2
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AI Technical Summary

Problems solved by technology

So far, there is no effective combination of deep learning methods in drug development and biological experiments to solve the problem of liver toxicity caused by drug interactions

Method used

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  • Method for predicting hepatotoxicity caused by drug interaction based on graph neural network model
  • Method for predicting hepatotoxicity caused by drug interaction based on graph neural network model

Examples

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Embodiment 1

[0043] like figure 1 As shown, this example provides a method for predicting hepatotoxicity caused by drug interaction based on a graph neural network model, specifically predicting whether there is drug interaction in the combination of Levofloxacin (levofloxacin) and Eliglustat (eliglustat). To cause liver toxicity, further, the software used in this example depends on the environment python3.8, pytorch1.4.0, rdkit2021.03.5, including the following steps:

[0044] S1, establish a deep learning model for predicting hepatotoxicity caused by drug interaction based on a graph neural network.

[0045] Specifically, S1 includes:

[0046] S11, obtain a sample for establishing the deep learning model, and perform preprocessing on the sample to obtain sample data, including:

[0047] S111, obtain drug data from DrugBank;

[0048] S112: Process the drug data, and delete non-small molecule drug data and drug data that cannot be read by rdkit in the drug data, as the sample data.

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Abstract

The invention discloses a method for predicting hepatotoxicity caused by drug interaction based on a graph neural network model, which comprises the following steps: establishing a deep learning model for predicting hepatotoxicity caused by drug interaction based on a graph neural network, and predicting hepatotoxicity caused by drug interaction based on the deep learning model, the method comprises the following steps: encoding two drug molecules by using a drug molecule encoder constructed based on a graph neural network, predicting a hepatotoxicity score caused by interaction of the two drugs through a full-connection neural network, and judging whether the combination of the two drugs can cause hepatotoxicity according to the predicted hepatotoxicity score. The method not only reduces the fund and time investment of drug combination preclinical toxicity research and development to a certain extent, but also can accurately predict the drug interaction hepatotoxicity during drug combination, and avoids the injury to the liver of a patient due to combination of a plurality of drugs. Unnecessary medicine combination clinical experiments during development of a new treatment scheme are reduced, and the success rate of the clinical experiments is increased.

Description

technical field [0001] The invention belongs to the technical field of data processing for prediction purposes, and in particular relates to a method for predicting hepatotoxicity caused by drug interaction based on a graph neural network model. Background technique [0002] Currently, a large number of drugs are approved for marketing around the world. As of 2020, the US FDA (US Food and Drug Administration) has approved about 3,000 drugs for the treatment of various diseases. With the increasing number of available drugs, drug combination strategies have also become new therapeutic strategies. Many studies have shown that an appropriate drug combination strategy can produce a 1+1>2 therapeutic effect. The drug combination strategy can achieve the purpose of treating a certain disease while avoiding excessive investment in new drug research and development costs. But at the same time, the combination of drugs may also produce toxic side effects that do not occur when ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G16H70/40G16H50/30G06N3/04G06N3/08
CPCG16H70/40G16H50/30G06N3/08G06N3/045Y02A90/10
Inventor 季序我彭鑫鑫余丹阳郭雪娇
Owner PRECISION SCI BIOMEDICINE (SUZHOU) CO LTD
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