Drug interaction and event prediction method and model based on attention neural network

A neural network and prediction method technology, applied in the field of drug interaction and event prediction based on attentional neural network, can solve different problems, achieve the effect of improving accuracy and promoting drug safety

Pending Publication Date: 2020-12-04
HUAZHONG AGRI UNIV
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Problems solved by technology

Most of the existing methods are used to predict whether two drugs interact, but drug interactions can lead to different biological consequences or events, so it is necessary to conduct a deeper event analysis of drug interactions

Method used

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  • Drug interaction and event prediction method and model based on attention neural network
  • Drug interaction and event prediction method and model based on attention neural network
  • Drug interaction and event prediction method and model based on attention neural network

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Embodiment Construction

[0052] The present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0053] see figure 1 , the drug interaction and event prediction model based on the attention neural network of the present invention includes a drug feature representation learning module, a drug pair feature representation learning module, a drug interaction and event prediction module; the drug feature representation learning module includes a multi-source feature acquisition module , a variety of feature network building modules and SDNE modules; the drug pair feature representation learning module includes drug comprehensive feature representation splicing module, drug pair feature representation splicing module and ANN module; drug interaction and event prediction module includes DNN module; source feature acquisition module , multiple feature network building blocks, SDNE module, drug comprehensive feature representation splicin...

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Abstract

The invention provides a drug interaction and event prediction method and a model based on attention neural network. According to the method, more comprehensive feature vector representation is obtained by integrating heterogeneous drug feature networks on the basis of a deep attention neural network framework, and a graph representation learning method is combined, the difference of the influenceof drugs on other drugs in different characteristics and dimensions is analyzed, and the accuracy of drug interaction prediction and the robustness of the model are improved under the condition of adopting multiple data sources, so that the model has interpretability, the function of predicting a drug interaction event is realized, the understanding of a mechanism hidden behind adverse drug reactions is facilitated, meanwhile, reasonable combined medication can be guided, and medication safety is promoted.

Description

technical field [0001] The invention belongs to the fields of deep learning, drug design and medical technology, and in particular relates to a drug interaction and event prediction method and model based on an attention neural network. Background technique [0002] A drug interaction is a phenomenon in which the effects of a drug are altered due to recent or simultaneous use of other drugs. With the number of approved drugs rapidly increasing, the prescribing of combination drugs has become a common phenomenon for doctors to treat patients' diseases. However, combination therapy may lead to toxic side effects. Therefore, the detection of drug interactions is an important task for drug safety supervision, which can provide effective and safe combination prescriptions for multiple drugs and avoid adverse reactions and dangerous reactions. Because the traditional clinical trial process is complex, costly, and time-consuming, there is an urgent need to develop computer-aided ...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G16B25/10G16B5/00G06N3/04G06N3/08
CPCG16B25/10G16B5/00G06N3/08G06N3/047G06N3/045
Inventor 刘世超邱洋陈思张杨崔钰鑫章文
Owner HUAZHONG AGRI UNIV
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