Machine learning models to accelerate the design of polymeric long-acting injectables

HIGHLIGHTS

SUMMARY

This necessitates the development and characterization of a wide array of formulation candidates through extensive and time-consuming in_vitro experimentation. The application of these empirical models is limited to post hoc analysis of the in_vitro drug release profiles of LAIs, and they do not offer information on in_vitro drug release from LAIs a priori. Past efforts to predict in_vitro drug release from LAIs using ML have exclusively considered neural_network (NN) based models, and have examined narrow application domains. In each research article selected for dataset construction, the in_vitro release of the drug from the . . .

 



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