{"id":35149,"date":"2026-04-13T14:10:46","date_gmt":"2026-04-13T14:10:46","guid":{"rendered":"https:\/\/lamarr-institute.org\/publication\/chemical-language-models-for-generating-compounds-with-triple-target-activity\/"},"modified":"2026-09-17T11:20:03","modified_gmt":"2026-09-17T11:20:03","slug":"chemical-language-models-for-generating-compounds-with-triple-target-activity","status":"publish","type":"publication","link":"https:\/\/lamarr-institute.org\/de\/publication\/chemical-language-models-for-generating-compounds-with-triple-target-activity\/","title":{"rendered":"Chemical language models for generating compounds with triple-target activity"},"content":{"rendered":"<p>Polypharmacology-based drug discovery relies on compounds with multi-target activity that are identified in screening and target profiling assays or using computational methods. Contemporary design of multi-target compounds is advanced by deep generative modeling. Dual-target compounds (DT-CPDs) are known for having a large number of target combinations, providing a sound basis for machine learning. By contrast, only comparably small numbers of triple-target compounds (TT-CPDs) are available, covering a very limited target space. Here, we investigate how this data restriction might be overcome to enable generative design of new TT-CPDs. Therefore, a transformer model is pre-trained to generate DT-CPDs from corresponding single-target compounds and used as a base model for triple-target fine-tuning. For different target combinations, the resulting models correctly reproduce known TT-CPDs not encountered during fine-tuning. Feature importance analysis explains the predictions and reveals structural motifs implicated in target selectivity or triple-target activity, thus providing a chemically intuitive rationale for the approach.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Polypharmacology-based drug discovery relies on compounds with multi-target activity that are identified in screening and target profiling assays or using computational methods. Contemporary design of multi-target compounds is advanced by deep generative modeling. Dual-target compounds (DT-CPDs) are known for having a large number of target combinations, providing a sound basis for machine learning. By contrast, only comparably small numbers of triple-target compounds (TT-CPDs) are available, covering a very limited target [&hellip;]<\/p>\n","protected":false},"author":14,"featured_media":0,"template":"","meta":{"_acf_changed":false,"footnotes":""},"publication-type":[30],"class_list":["post-35149","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\/35149","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\/35149\/revisions"}],"wp:attachment":[{"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/media?parent=35149"}],"wp:term":[{"taxonomy":"publication-type","embeddable":true,"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/publication-type?post=35149"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}