PromptSMILES: prompting for scaffold decoration and fragment linking in chemical language models

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• dc.contributor.author Thomas, Morgan
• dc.contributor.author Ahmad, Mazen
• dc.contributor.author Tresadern, Gary
• dc.contributor.author De Fabritiis, Gianni
• dc.date.accessioned 2024-09-17T06:23:43Z
• dc.date.available 2024-09-17T06:23:43Z
• dc.date.issued 2024
• dc.description.abstract SMILES-based generative models are amongst the most robust and successful recent methods used to augment drug design. They are typically used for complete de novo generation, however, scaffold decoration and fragment linking applications are sometimes desirable which requires a different grammar, architecture, training dataset and therefore, re-training of a new model. In this work, we describe a simple procedure to conduct constrained molecule generation with a SMILES-based generative model to extend applicability to scaffold decoration and fragment linking by providing SMILES prompts, without the need for re-training. In combination with reinforcement learning, we show that pre-trained, decoder-only models adapt to these applications quickly and can further optimize molecule generation towards a specified objective. We compare the performance of this approach to a variety of orthogonal approaches and show that performance is comparable or better. For convenience, we provide an easy-to-use python package to facilitate model sampling which can be found on GitHub and the Python Package Index.Scientific contributionThis novel method extends an autoregressive chemical language model to scaffold decoration and fragment linking scenarios. This doesn't require re-training, the use of a bespoke grammar, or curation of a custom dataset, as commonly required by other approaches.
• dc.format.mimetype application/pdf
• dc.identifier.citation Thomas M, Ahmad M, Tresadern G, de Fabritiis G. PromptSMILES: prompting for scaffold decoration and fragment linking in chemical language models. J Cheminform. 2024 Jul 4;16(1):77. DOI: 10.1186/s13321-024-00866-5
• dc.identifier.doi http://dx.doi.org/10.1186/s13321-024-00866-5
• dc.identifier.issn 1758-2946
• dc.identifier.uri http://hdl.handle.net/10230/61109
• dc.language.iso eng
• dc.publisher BioMed Central
• dc.relation.ispartof J Cheminform. 2024 Jul 4;16(1):77
• dc.rights © The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.rights.uri http://creativecommons.org/licenses/by/4.0/
• dc.subject.keyword Artificial intelligence
• dc.subject.keyword Chemical language models
• dc.subject.keyword De novo molecule generation
• dc.subject.keyword Drug design
• dc.subject.keyword Fragment linking
• dc.subject.keyword Reinforcement learning
• dc.subject.keyword Scaffold decoration
• dc.subject.keyword Scaffold hopping
• dc.title PromptSMILES: prompting for scaffold decoration and fragment linking in chemical language models
• dc.type info:eu-repo/semantics/article
• dc.type.version info:eu-repo/semantics/publishedVersion