A drug target multi-label classification method based on a dual mode

By employing a bimodal drug target multi-label classification method, utilizing a Transformer encoder and a graph isomorphic network (GIN), combined with community detection and a multi-label classifier, the method addresses the problem of insufficient accuracy in drug target classification in existing technologies, and achieves effective identification of the interactions between multiple drugs and multiple targets.

CN116469484BActive Publication Date: 2026-04-10NORTHEASTERN UNIV CHINA
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
NORTHEASTERN UNIV CHINA
Filing Date
2023-04-17
Publication Date
2026-04-10

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Abstract

The application provides a drug target multi-label classification method based on a dual mode, and relates to the technical field of drug target classification. The method first obtains a drug target data set, trains a Transformer encoder, and obtains a vector representation of the drug as a whole; a target label co-occurrence graph of the drug target data set is constructed, and a plurality of label subspaces are generated; then the drug is divided into a plurality of drug substructure graphs to form a drug graph package; for a plurality of target labels in each label subspace, all drugs interacting with the target in the drug target data set are obtained, and a feature vector of the entire label subspace is calculated; in each label subspace, the representative substructure feature vector of the drug graph package is obtained; the feature vector of the drug graph package and the feature vector of the drug SMILES sequence are spliced, and then classified by a multi-label classifier; and the classification results on all label subspaces are integrated as the classification result of the drug target.
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Citation Information

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