Artificial intelligence generates novel 3D printing formulations

Formulation development is a critical step in the development of medicines. The process requires human creativity, ingenuity and in-depth knowledge of formulation development and processing optimization, which can be time-consuming. Herein, we tested the ability of artificial intelligence (AI) to create de novo formulations for three-dimensional (3D) printing. Specifically, conditional generative adversarial networks (cGANs), which are generative models known for their creativity, were trained on a dataset consisting of 1437 fused deposition modelling (FDM) printed formulations that were extracted from both the literature and in-house data. In total, 27 different cGANs architectures were explored with varying learning rate, batch size and number of hidden layers parameters to generate 270 formulations.

Highlights

Artificial intelligence (AI) was able to create de novo formulations for three-dimensional (3D) printing.

Conditional generative adversarial networks (cGANs) were trained on a dataset consisting of 1437 fused deposition modelling (FDM) printed formulations.

27 different cGANs architectures were explored with varying learning rate, batch size and number of hidden layers parameters to generate 270 formulations.

cGANs with a medium learning rate (10⁻⁴) could strike a balance in generating formulations that were both novel and realistic.

Four of the formulations were fabricated using an FDM printer.

This is the first time an AI-generated formulation has been successfully printed.

After a comparison between the characteristics of AI-generated and human-generated formulations, it was discovered that cGANs with a medium learning rate (10⁻⁴) could strike a balance in generating formulations that are both novel and realistic. Four of these formulations were fabricated using an FDM printer, of which the first AI-generated formulation was successfully printed. Our study represents a milestone, highlighting the capacity of AI to undertake creative tasks and its potential to revolutionize the drug development process.

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Moe Elbadawi, Hanxiang Li, Siyuan Sun, Manal E. Alkahtani, Abdul W. Basit, Simon Gaisford, Artificial intelligence generates novel 3D printing formulations, Applied Materials Today, Volume 36, 2024, 102061, ISSN 2352-9407, https://doi.org/10.1016/j.apmt.2024.102061.