Research on drug repositioning based on generative AI

In view of the problem of fixed drug indication prediction quantity and inability to fully reveal potential indication of drug in current drug repositioning research, this article proposes a generative AI drug repositioning model GenDrugShifter, which consists of a graph attention neural network and...

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Main Authors: GONG Houwu, JIN Min
Format: Article
Language:zho
Published: China InfoCom Media Group 2025-03-01
Series:大数据
Subjects:
Online Access:http://www.j-bigdataresearch.com.cn/thesisDetails#10.11959/j.issn.2096-0271.2025030
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author GONG Houwu
JIN Min
author_facet GONG Houwu
JIN Min
author_sort GONG Houwu
collection DOAJ
description In view of the problem of fixed drug indication prediction quantity and inability to fully reveal potential indication of drug in current drug repositioning research, this article proposes a generative AI drug repositioning model GenDrugShifter, which consists of a graph attention neural network and a transformer decoder module. The model uses the drug molecular structure represented in the InChI format as input, learns the potential relationship between the drug active molecular structure and indication through self-supervision, and outputs the indication of the drug using autoregressive methods. The results of the Western medicine repositioning experiment show that GenDrugShifter is superior to the other four advanced drug repositioning methods in terms of predictive performance. The GenDrugShifter model can more comprehensively reveal the potential indication of drug, with superior performance and reliability. The validation of clinical data further proves its effectiveness in practical applications.
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record_format Article
series 大数据
spelling doaj-art-2087fe7b0acd45358f8648d1f7dfdfa62025-08-20T03:08:28ZzhoChina InfoCom Media Group大数据2096-02712025-03-0111557286967734Research on drug repositioning based on generative AIGONG HouwuJIN MinIn view of the problem of fixed drug indication prediction quantity and inability to fully reveal potential indication of drug in current drug repositioning research, this article proposes a generative AI drug repositioning model GenDrugShifter, which consists of a graph attention neural network and a transformer decoder module. The model uses the drug molecular structure represented in the InChI format as input, learns the potential relationship between the drug active molecular structure and indication through self-supervision, and outputs the indication of the drug using autoregressive methods. The results of the Western medicine repositioning experiment show that GenDrugShifter is superior to the other four advanced drug repositioning methods in terms of predictive performance. The GenDrugShifter model can more comprehensively reveal the potential indication of drug, with superior performance and reliability. The validation of clinical data further proves its effectiveness in practical applications.http://www.j-bigdataresearch.com.cn/thesisDetails#10.11959/j.issn.2096-0271.2025030drug repositioninglarge language modelartificial intelligencedrug discovery
spellingShingle GONG Houwu
JIN Min
Research on drug repositioning based on generative AI
大数据
drug repositioning
large language model
artificial intelligence
drug discovery
title Research on drug repositioning based on generative AI
title_full Research on drug repositioning based on generative AI
title_fullStr Research on drug repositioning based on generative AI
title_full_unstemmed Research on drug repositioning based on generative AI
title_short Research on drug repositioning based on generative AI
title_sort research on drug repositioning based on generative ai
topic drug repositioning
large language model
artificial intelligence
drug discovery
url http://www.j-bigdataresearch.com.cn/thesisDetails#10.11959/j.issn.2096-0271.2025030
work_keys_str_mv AT gonghouwu researchondrugrepositioningbasedongenerativeai
AT jinmin researchondrugrepositioningbasedongenerativeai