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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.<\/jats:p>\n                  <jats:p>\n                    <jats:bold>Scientific contribution<\/jats:bold>\n                  <\/jats:p>\n                  <jats:p>This novel method extends an autoregressive chemical language model to scaffold decoration and fragment linking scenarios. This doesn\u2019t require re-training, the use of a bespoke grammar, or curation of a custom dataset, as commonly required by other approaches.<\/jats:p>","DOI":"10.1186\/s13321-024-00866-5","type":"journal-article","created":{"date-parts":[[2024,7,4]],"date-time":"2024-07-04T09:02:17Z","timestamp":1720083737000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["PromptSMILES: prompting for scaffold decoration and fragment linking in chemical language models"],"prefix":"10.1186","volume":"16","author":[{"given":"Morgan","family":"Thomas","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mazen","family":"Ahmad","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gary","family":"Tresadern","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gianni","family":"de Fabritiis","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,7,4]]},"reference":[{"key":"866_CR1","doi-asserted-by":"publisher","first-page":"660","DOI":"10.1038\/nrd1467","volume":"3","author":"DC Ress","year":"2004","unstructured":"Ress DC, Congreve M, Murray CW, Carr R (2004) Fragment-based lead discovery. 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