Chemical language models for de novo drug design: Challenges and opportunities

30 December 2022, Version 1
This content is a preprint and has not undergone peer review at the time of posting.


Generative deep learning is accelerating de novo drug design, by allowing the construction of molecules with desired properties on demand. Chemical language models – which generate new molecules in the form of strings – have been particularly successful in this endeavour. Thanks to advances in natural language processing methods and interdisciplinary collaborations, chemical language models are expected to become increasingly relevant in drug discovery. This minireview provides an overview of the current state-of-the-art of chemical language models for de novo design, and analyses current limitations, challenges, and advantages. Finally, a perspective on future opportunities is provided.


Artificial intelligence
Chemical language models
De novo design
Drug discovery
Molecule discovery
Medicinal Chemistry


Comments are not moderated before they are posted, but they can be removed by the site moderators if they are found to be in contravention of our Commenting Policy [opens in a new tab] - please read this policy before you post. Comments should be used for scholarly discussion of the content in question. You can find more information about how to use the commenting feature here [opens in a new tab] .
This site is protected by reCAPTCHA and the Google Privacy Policy [opens in a new tab] and Terms of Service [opens in a new tab] apply.