Protocol to generate dual-target compounds using a transformer chemical language model
Summary: Here, we present a protocol to generate dual-target compounds (DT-CPDs) interacting with two distinct target proteins using a transformer-based chemical language model. We describe steps for installing software, preparing data, and pre-training the model on pairs of single-target compounds...
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Elsevier
2025-03-01
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| Series: | STAR Protocols |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666166724007494 |
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| author | Sanjana Srinivasan Jürgen Bajorath |
| author_facet | Sanjana Srinivasan Jürgen Bajorath |
| author_sort | Sanjana Srinivasan |
| collection | DOAJ |
| description | Summary: Here, we present a protocol to generate dual-target compounds (DT-CPDs) interacting with two distinct target proteins using a transformer-based chemical language model. We describe steps for installing software, preparing data, and pre-training the model on pairs of single-target compounds (ST-CPDs), which bind to an individual protein, and DT-CPDs. We then detail procedures for assembling ST- and corresponding DT-CPD data for specific protein pairs and evaluating the model’s performance on hold-out test sets.For complete details on the use and execution of this protocol, please refer to Srinivasan and Bajorath.1 : Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics. |
| format | Article |
| id | doaj-art-1be7826bc1a0466c8973934a178b09f4 |
| institution | Kabale University |
| issn | 2666-1667 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | STAR Protocols |
| spelling | doaj-art-1be7826bc1a0466c8973934a178b09f42025-01-24T04:45:43ZengElsevierSTAR Protocols2666-16672025-03-0161103584Protocol to generate dual-target compounds using a transformer chemical language modelSanjana Srinivasan0Jürgen Bajorath1Department of Life Science Informatics and Data Science, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität, Friedrich-Hirzebruch-Allee 5/6, 53115 Bonn, Germany; Lamarr Institute for Machine Learning and Artificial Intelligence, Friedrich-Hirzebruch-Allee 5/6, 53115 Bonn, Germany; Corresponding authorDepartment of Life Science Informatics and Data Science, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität, Friedrich-Hirzebruch-Allee 5/6, 53115 Bonn, Germany; Lamarr Institute for Machine Learning and Artificial Intelligence, Friedrich-Hirzebruch-Allee 5/6, 53115 Bonn, Germany; Corresponding authorSummary: Here, we present a protocol to generate dual-target compounds (DT-CPDs) interacting with two distinct target proteins using a transformer-based chemical language model. We describe steps for installing software, preparing data, and pre-training the model on pairs of single-target compounds (ST-CPDs), which bind to an individual protein, and DT-CPDs. We then detail procedures for assembling ST- and corresponding DT-CPD data for specific protein pairs and evaluating the model’s performance on hold-out test sets.For complete details on the use and execution of this protocol, please refer to Srinivasan and Bajorath.1 : Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics.http://www.sciencedirect.com/science/article/pii/S2666166724007494Molecular/Chemical ProbesSystems biologyChemistryComputer sciences |
| spellingShingle | Sanjana Srinivasan Jürgen Bajorath Protocol to generate dual-target compounds using a transformer chemical language model STAR Protocols Molecular/Chemical Probes Systems biology Chemistry Computer sciences |
| title | Protocol to generate dual-target compounds using a transformer chemical language model |
| title_full | Protocol to generate dual-target compounds using a transformer chemical language model |
| title_fullStr | Protocol to generate dual-target compounds using a transformer chemical language model |
| title_full_unstemmed | Protocol to generate dual-target compounds using a transformer chemical language model |
| title_short | Protocol to generate dual-target compounds using a transformer chemical language model |
| title_sort | protocol to generate dual target compounds using a transformer chemical language model |
| topic | Molecular/Chemical Probes Systems biology Chemistry Computer sciences |
| url | http://www.sciencedirect.com/science/article/pii/S2666166724007494 |
| work_keys_str_mv | AT sanjanasrinivasan protocoltogeneratedualtargetcompoundsusingatransformerchemicallanguagemodel AT jurgenbajorath protocoltogeneratedualtargetcompoundsusingatransformerchemicallanguagemodel |