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 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.