Published November 10, 2022 | Version v1.0.3
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pban-91/long-acting-injectables: Machine Learning Models to Accelerate the Design of Polymeric Long-Acting Injectables


Long-acting injectables are considered one of the most promising therapeutic strategies for the treatment of chronic diseases as they can afford improved therapeutic efficacy, safety, and patient compliance. The use of polymer materials in such a drug formulation strategy can offer unparalleled diversity owing to the ability to synthesize materials with a wide range of properties. However, the interplay between multiple parameters, including the physicochemical properties of the drug and polymer, make it very difficult to intuitively predict the performance of these systems. This necessitates the development and characterization of a wide array of formulation candidates through extensive and time-consuming in vitro experimentation. Machine learning is enabling leap-step advances in a number of fields including drug discovery and materials science. The current study takes a critical step towards data-driven drug formulation development with an emphasis on long-acting injectables. A series of machine learning algorithms were trained and refined for accurate prediction of experimental drug release profiles. Analysis of the best performing model uncovered the properties of the drug and polymer that were identified to be key determinants of drug release. This information can be used to identify promising drug-polymer combinations that result in long-acting injectables with specific drug release behaviour. The implementation of this data-driven approach has the potential to reduce the time and cost associated with formulation development. Datasets and relevant codes used to train the machine learning models have been made openly available to encourage usage in future drug formulation efforts.

The dataset and results that support the findings of this study are available on zenodo ( and ChemRxiv (

The code that supports the findings of this study are available at the Aspuru-Guzik Group's GitHub page (



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