{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T10:20:20Z","timestamp":1772187620988,"version":"3.50.1"},"publisher-location":"Cham","reference-count":32,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032045515","type":"print"},{"value":"9783032045522","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,9,23]],"date-time":"2025-09-23T00:00:00Z","timestamp":1758585600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,9,23]],"date-time":"2025-09-23T00:00:00Z","timestamp":1758585600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026]]},"DOI":"10.1007\/978-3-032-04552-2_7","type":"book-chapter","created":{"date-parts":[[2025,9,22]],"date-time":"2025-09-22T14:14:55Z","timestamp":1758550495000},"page":"45-52","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Consensus Prediction of Chemical Reactions with OCHEM-R Platform"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6855-0012","authenticated-orcid":false,"given":"Igor V.","family":"Tetko","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9828-386X","authenticated-orcid":false,"given":"Guillaume","family":"Godin","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4894-4660","authenticated-orcid":false,"given":"Kevin M.","family":"Jablonka","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4033-4235","authenticated-orcid":false,"given":"Adrian","family":"Mirza","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4943-2643","authenticated-orcid":false,"given":"Luc","family":"Patiny","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,9,23]]},"reference":[{"key":"7_CR1","doi-asserted-by":"publisher","unstructured":"Karpov, P., Godin, G., Tetko, I.V.: A transformer model for retrosynthesis. In: Proceedings of the Artificial Neural Networks and Machine Learning \u2013 ICANN 2019: Workshop and Special Sessions; Tetko, I.V., K\u016frkov\u00e1, V., Karpov, P., Theis, F., (eds.); Springer International Publishing: Cham, pp. 817\u2013830 (2019). https:\/\/doi.org\/10.1007\/978-3-030-30493-5_78","DOI":"10.1007\/978-3-030-30493-5_78"},{"key":"7_CR2","doi-asserted-by":"publisher","first-page":"5575","DOI":"10.1038\/s41467-020-19266-y","volume":"11","author":"IV Tetko","year":"2020","unstructured":"Tetko, I.V., Karpov, P., Van Deursen, R., Godin, G.: State-of-the-art augmented NLP transformer models for direct and single-step retrosynthesis. Nat. Commun. 11, 5575 (2020). https:\/\/doi.org\/10.1038\/s41467-020-19266-y","journal-title":"Nat. Commun."},{"key":"7_CR3","doi-asserted-by":"publisher","DOI":"10.1088\/2632-2153\/ac3ffb","volume":"3","author":"R Irwin","year":"2022","unstructured":"Irwin, R., Dimitriadis, S., He, J., Bjerrum, E.J.: Chemformer: a pre-trained transformer for computational chemistry. Mach. Learn. Sci. Technol. 3, 015022 (2022). https:\/\/doi.org\/10.1088\/2632-2153\/ac3ffb","journal-title":"Mach. Learn. Sci. Technol."},{"key":"7_CR4","doi-asserted-by":"publisher","first-page":"6404","DOI":"10.1038\/s41467-024-50617-1","volume":"15","author":"Y Han","year":"2024","unstructured":"Han, Y., et al.: Retrosynthesis prediction with an iterative string editing model. Nat. Commun. 15, 6404 (2024). https:\/\/doi.org\/10.1038\/s41467-024-50617-1","journal-title":"Nat. Commun."},{"key":"7_CR5","doi-asserted-by":"publisher","first-page":"1612","DOI":"10.1021\/jacsau.1c00246","volume":"1","author":"S Chen","year":"2021","unstructured":"Chen, S., Jung, Y.: Deep retrosynthetic reaction prediction using local reactivity and global attention. JACS Au 1, 1612\u20131620 (2021). https:\/\/doi.org\/10.1021\/jacsau.1c00246","journal-title":"JACS Au"},{"key":"7_CR6","doi-asserted-by":"publisher","first-page":"80","DOI":"10.1186\/s13321-024-00877-2","volume":"16","author":"K Zeng","year":"2024","unstructured":"Zeng, K., et al.: Ualign: pushing the limit of template-free retrosynthesis prediction with unsupervised SMILES alignment. J. Cheminformatics 16, 80 (2024). https:\/\/doi.org\/10.1186\/s13321-024-00877-2","journal-title":"J. Cheminformatics"},{"key":"7_CR7","doi-asserted-by":"publisher","first-page":"18031","DOI":"10.1039\/d4sc02408g","volume":"15","author":"D Kreutter","year":"2024","unstructured":"Kreutter, D., Reymond, J.-L.: Chemoenzymatic multistep retrosynthesis with transformer loops. Chem. Sci. 15, 18031\u201318047 (2024). https:\/\/doi.org\/10.1039\/d4sc02408g","journal-title":"Chem. Sci."},{"key":"7_CR8","doi-asserted-by":"publisher","unstructured":"Sagawa, T., Kojima, R.: ReactionT5: A Large-Scale Pre-Trained Model towards Application of Limited Reaction Data. ArXiv E-Prints (2023). arXiv:2311.06708, https:\/\/doi.org\/10.48550\/arXiv.2311.06708","DOI":"10.48550\/arXiv.2311.06708"},{"key":"7_CR9","doi-asserted-by":"publisher","first-page":"8791","DOI":"10.1021\/acs.jmedchem.9b01919","volume":"63","author":"A Thakkar","year":"2020","unstructured":"Thakkar, A., Selmi, N., Reymond, J.-L., Engkvist, O., Bjerrum, E.J.: \u201cRing breaker\u201d: neural network driven synthesis prediction of the ring system chemical space. J. Med. Chem. 63, 8791\u20138808 (2020). https:\/\/doi.org\/10.1021\/acs.jmedchem.9b01919","journal-title":"J. Med. Chem."},{"key":"7_CR10","doi-asserted-by":"publisher","first-page":"2111","DOI":"10.1021\/acs.jcim.1c01065","volume":"62","author":"P Seidl","year":"2022","unstructured":"Seidl, P., et al.: Improving few- and zero-shot reaction template prediction using modern hopfield networks. J. Chem. Inf. Model. 62, 2111\u20132120 (2022). https:\/\/doi.org\/10.1021\/acs.jcim.1c01065","journal-title":"J. Chem. Inf. Model."},{"key":"7_CR11","doi-asserted-by":"publisher","first-page":"3316","DOI":"10.1039\/C9SC05704H","volume":"11","author":"P Schwaller","year":"2020","unstructured":"Schwaller, P., et al.: Predicting retrosynthetic pathways using transformer-based models and a hyper-graph exploration strategy. Chem. Sci. 11, 3316\u20133325 (2020). https:\/\/doi.org\/10.1039\/C9SC05704H","journal-title":"Chem. Sci."},{"key":"7_CR12","doi-asserted-by":"publisher","unstructured":"Hassen, A.K., Torren-Peraire, P., Genheden, S., Verhoeven, J., Preuss, M., Tetko, I.V.: Mind the Retrosynthesis Gap: Bridging the Divide between Single-Step and Multi-Step Retrosynthesis Prediction ArXiv E-Prints(2022). arXiv:2212.11809, https:\/\/doi.org\/10.48550\/arXiv.2212.11809","DOI":"10.48550\/arXiv.2212.11809"},{"key":"7_CR13","doi-asserted-by":"publisher","first-page":"558","DOI":"10.1039\/D3DD00252G","volume":"3","author":"P Torren-Peraire","year":"2024","unstructured":"Torren-Peraire, P., et al.: Models Matter: the impact of single-step retrosynthesis on synthesis planning. Digit. Discov. 3, 558\u2013572 (2024). https:\/\/doi.org\/10.1039\/D3DD00252G","journal-title":"Digit. Discov."},{"key":"7_CR14","doi-asserted-by":"publisher","DOI":"10.1016\/j.slasd.2024.01.005","volume":"29","author":"A Hunklinger","year":"2024","unstructured":"Hunklinger, A., Hartog, P., \u0160\u00edcho, M., Godin, G., Tetko, I.V.: The openOCHEM consensus model is the best-performing open-source predictive model in the first EUOS\/SLAS joint compound solubility challenge. SLAS Discov. 29, 100144 (2024). https:\/\/doi.org\/10.1016\/j.slasd.2024.01.005","journal-title":"SLAS Discov."},{"key":"7_CR15","doi-asserted-by":"publisher","first-page":"768","DOI":"10.1021\/acs.chemrestox.5b00481","volume":"29","author":"S Novotarskyi","year":"2016","unstructured":"Novotarskyi, S., Abdelaziz, A., Sushko, Y., K\u00f6rner, R., Vogt, J., Tetko, I.V.: ToxCast EPA in vitro to in vivo challenge: insight into the rank-i model. Chem. Res. Toxicol. 29, 768\u2013775 (2016). https:\/\/doi.org\/10.1021\/acs.chemrestox.5b00481","journal-title":"Chem. Res. Toxicol."},{"key":"7_CR16","doi-asserted-by":"publisher","unstructured":"Eytcheson, S.A., Tetko, I.V.: Which Modern AI Methods Provide Accurate Predictions of Toxicological Endpoints? Analysis of Tox24 Challenge Results. Chem. Res. Toxicol.  (2025). https:\/\/doi.org\/10.1021\/acs.chemrestox.5c00273","DOI":"10.1021\/acs.chemrestox.5c00273"},{"key":"7_CR17","doi-asserted-by":"publisher","first-page":"392","DOI":"10.1021\/acs.chemrestox.4c00421","volume":"38","author":"DM Makarov","year":"2025","unstructured":"Makarov, D.M., Ksenofontov, A.A., Budkov, Y.A.: Consensus modeling for predicting chemical binding to transthyretin as the winning solution of the tox24 challenge. Chem. Res. Toxicol. 38, 392\u2013399 (2025). https:\/\/doi.org\/10.1021\/acs.chemrestox.4c00421","journal-title":"Chem. Res. Toxicol."},{"key":"7_CR18","doi-asserted-by":"publisher","first-page":"1061","DOI":"10.1021\/acs.chemrestox.5c00018","volume":"38","author":"T Cirino","year":"2025","unstructured":"Cirino, T., et al.: Consensus modeling strategies for predicting transthyretin binding affinity from tox24 challenge data. Chem. Res. Toxicol. 38, 1061\u20131071 (2025). https:\/\/doi.org\/10.1021\/acs.chemrestox.5c00018","journal-title":"Chem. Res. Toxicol."},{"key":"7_CR19","doi-asserted-by":"publisher","first-page":"533","DOI":"10.1007\/s10822-011-9440-2","volume":"25","author":"I Sushko","year":"2011","unstructured":"Sushko, I., et al.: Online chemical modeling environment (OCHEM): web platform for data storage, model development and publishing of chemical information. J. Comput. Aided Mol. Des. 25, 533\u2013554 (2011). https:\/\/doi.org\/10.1007\/s10822-011-9440-2","journal-title":"J. Comput. Aided Mol. Des."},{"key":"7_CR20","unstructured":"Lowe, D.M.: Extraction of Chemical Structures and Reactions from the Literature. PhD Thesis, Apollo - University of Cambridge Repository (2012)"},{"key":"7_CR21","doi-asserted-by":"publisher","first-page":"1103","DOI":"10.1021\/acscentsci.7b00303","volume":"3","author":"B Liu","year":"2017","unstructured":"Liu, B., et al.: Retrosynthetic reaction prediction using neural sequence-to-sequence models. ACS Cent. Sci. 3, 1103\u20131113 (2017). https:\/\/doi.org\/10.1021\/acscentsci.7b00303","journal-title":"ACS Cent. Sci."},{"key":"7_CR22","doi-asserted-by":"publisher","first-page":"18820","DOI":"10.1021\/jacs.1c09820","volume":"143","author":"SM Kearnes","year":"2021","unstructured":"Kearnes, S.M., et al.: The open reaction database. J. Am. Chem. Soc. 143, 18820\u201318826 (2021). https:\/\/doi.org\/10.1021\/jacs.1c09820","journal-title":"J. Am. Chem. Soc."},{"key":"7_CR23","doi-asserted-by":"publisher","first-page":"144","DOI":"10.1038\/s42256-020-00284-w","volume":"3","author":"P Schwaller","year":"2021","unstructured":"Schwaller, P., et al.: Mapping the space of chemical reactions using attention-based neural networks. Nat. Mach. Intell. 3, 144\u2013152 (2021). https:\/\/doi.org\/10.1038\/s42256-020-00284-w","journal-title":"Nat. Mach. Intell."},{"key":"7_CR24","doi-asserted-by":"publisher","unstructured":"Douze, M., et al.: The Faiss Library. ArXiv E-Prints (2024). arXiv:2401.08281, https:\/\/doi.org\/10.48550\/arXiv.2401.08281","DOI":"10.48550\/arXiv.2401.08281"},{"key":"7_CR25","unstructured":"openochem Open OCHEM -- AI Models for Drug Discovery and Environmental Chemistry (2022)"},{"key":"7_CR26","first-page":"69410","volume":"10","author":"MV Kachaeva","year":"2018","unstructured":"Kachaeva, M.V., Pilyo, S.G., Demydchuk, B.A., Prokopenko, V.M., Zhirnov, V.V., Brovarets, V.S.: 4-Cyano-1,3-oxazole-5-sulfonamides as novel promising anticancer lead compounds. Int. J. Curr. Res. 10, 69410\u201369425 (2018)","journal-title":"Int. J. Curr. Res."},{"key":"7_CR27","doi-asserted-by":"publisher","first-page":"26","DOI":"10.1186\/s13321-025-00953-1","volume":"17","author":"P Torren-Peraire","year":"2025","unstructured":"Torren-Peraire, P., Verhoeven, J., Herman, D., Ceulemans, H., Tetko, I.V., Wegner, J.K.: Improving route development using convergent retrosynthesis planning. J. Cheminformatics 17, 26 (2025). https:\/\/doi.org\/10.1186\/s13321-025-00953-1","journal-title":"J. Cheminformatics"},{"key":"7_CR28","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1186\/s13321-025-00974-w","volume":"17","author":"M Andronov","year":"2025","unstructured":"Andronov, M., Andronova, N., Wand, M., Schmidhuber, J., Clevert, D.-A.: Accelerating the inference of string generation-based chemical reaction models for industrial applications. J. Cheminformatics 17, 31 (2025). https:\/\/doi.org\/10.1186\/s13321-025-00974-w","journal-title":"J. Cheminformatics"},{"key":"7_CR29","doi-asserted-by":"publisher","first-page":"1771","DOI":"10.1021\/acs.jcim.4c01821","volume":"65","author":"PBR Hartog","year":"2025","unstructured":"Hartog, P.B.R., Westerlund, A.M., Tetko, I.V., Genheden, S.: Investigations into the efficiency of computer-aided synthesis planning. J. Chem. Inf. Model. 65, 1771\u20131781 (2025). https:\/\/doi.org\/10.1021\/acs.jcim.4c01821","journal-title":"J. Chem. Inf. Model."},{"key":"7_CR30","unstructured":"Hu, E.J., et al.: LoRA: low-rank adaptation of large language models. In Proceedings of the International Conference on Learning Representations (2022)"},{"key":"7_CR31","doi-asserted-by":"publisher","unstructured":"Pratap, S., Aranha, A.R., Kumar, D., Malhotra, G., Iyer, A.P.N.: The fine art of fine-tuning: a structured review of advanced llm fine-tuning techniques. Nat. Lang. Process. J. 11, 100144 (2025). https:\/\/doi.org\/10.1016\/j.nlp.2025.100144","DOI":"10.1016\/j.nlp.2025.100144"},{"key":"7_CR32","doi-asserted-by":"publisher","first-page":"42","DOI":"10.1021\/acs.jcim.3c01524","volume":"64","author":"V Voinarovska","year":"2024","unstructured":"Voinarovska, V., Kabeshov, M., Dudenko, D., Genheden, S., Tetko, I.V.: When yield prediction does not yield prediction: an overview of the current challenges. J. Chem. Inf. Model. 64, 42\u201356 (2024). https:\/\/doi.org\/10.1021\/acs.jcim.3c01524","journal-title":"J. Chem. Inf. Model."}],"container-title":["Lecture Notes in Computer Science","Artificial Neural Networks and Machine Learning. ICANN 2025 International Workshops and Special Sessions"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-04552-2_7","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T09:22:31Z","timestamp":1772184151000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-04552-2_7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,23]]},"ISBN":["9783032045515","9783032045522"],"references-count":32,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-04552-2_7","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,23]]},"assertion":[{"value":"23 September 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICANN","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Artificial Neural Networks","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Kaunas","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Lithuania","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9 September 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 September 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"34","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icann2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/e-nns.org\/icann2025\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}