{"id":32471,"date":"2026-01-21T17:02:00","date_gmt":"2026-01-21T17:02:00","guid":{"rendered":"https:\/\/lamarr-institute.org\/publication\/extension-of-multi-site-analogue-series-with-potent-compounds-using-a-bidirectional-transformer-based-chemical-language-model\/"},"modified":"2026-09-17T11:24:23","modified_gmt":"2026-09-17T11:24:23","slug":"extension-of-multi-site-analogue-series-with-potent-compounds-using-a-bidirectional-transformer-based-chemical-language-model","status":"publish","type":"publication","link":"https:\/\/lamarr-institute.org\/de\/publication\/extension-of-multi-site-analogue-series-with-potent-compounds-using-a-bidirectional-transformer-based-chemical-language-model\/","title":{"rendered":"Extension of multi-site analogue series with potent compounds using a bidirectional transformer-based chemical language model"},"content":{"rendered":"<p>Generating potent compounds for evolving analogue series ({AS}) is a key challenge in medicinal chemistry. The versatility of chemical language models ({CLMs}) makes it possible to formulate this challenge as an off-the-beaten-path prediction task. In this work, we have devised a coding and tokenization scheme for evolving {AS} with multiple substitution sites (multi-site {AS}) and implemented a bidirectional transformer to predict new potent analogues for such series. Scientific foundations of this approach are discussed and, as a benchmark, the transformer model is compared to a recurrent neural network ({RNN}) for the prediction of analogues of {AS} with single substitution sites. Furthermore, the transformer is shown to successfully predict potent analogues with varying R-group combinations for multi-site {AS} having activity against many different targets. Prediction of R-group combinations for extending {AS} with potent compounds represents a novel approach for compound optimization.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Generating potent compounds for evolving analogue series ({AS}) is a key challenge in medicinal chemistry. The versatility of chemical language models ({CLMs}) makes it possible to formulate this challenge as an off-the-beaten-path prediction task. In this work, we have devised a coding and tokenization scheme for evolving {AS} with multiple substitution sites (multi-site {AS}) and implemented a bidirectional transformer to predict new potent analogues for such series. Scientific foundations of [&hellip;]<\/p>\n","protected":false},"author":14,"featured_media":0,"template":"","meta":{"_acf_changed":false,"footnotes":""},"publication-type":[30],"class_list":["post-32471","publication","type-publication","status-publish","hentry","publication-type-article"],"acf":[],"publishpress_future_workflow_manual_trigger":{"enabledWorkflows":[]},"_links":{"self":[{"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/publication\/32471","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/publication"}],"about":[{"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/types\/publication"}],"author":[{"embeddable":true,"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/users\/14"}],"version-history":[{"count":0,"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/publication\/32471\/revisions"}],"wp:attachment":[{"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/media?parent=32471"}],"wp:term":[{"taxonomy":"publication-type","embeddable":true,"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/publication-type?post=32471"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}