{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,28]],"date-time":"2026-02-28T13:09:37Z","timestamp":1772284177984,"version":"3.50.1"},"publisher-location":"Singapore","reference-count":27,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819543809","type":"print"},{"value":"9789819543816","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,11,16]],"date-time":"2025-11-16T00:00:00Z","timestamp":1763251200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,11,16]],"date-time":"2025-11-16T00:00:00Z","timestamp":1763251200000},"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\/978-981-95-4381-6_11","type":"book-chapter","created":{"date-parts":[[2025,11,15]],"date-time":"2025-11-15T06:59:19Z","timestamp":1763189959000},"page":"153-167","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A GFlowNet-Based World Model for Structural Protein\u2013Protein Interaction Prediction on DB5"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1252-0719","authenticated-orcid":false,"given":"Olaide Nathaniel","family":"Oyelade","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2633-6015","authenticated-orcid":false,"given":"Hui","family":"Wang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,11,16]]},"reference":[{"key":"11_CR1","doi-asserted-by":"crossref","unstructured":"Strom, J.M., Luck, K.: Bias in, bias out \u2013 AlphaFold-Multimer and the structural complexity of protein interfaces. Curr. Opin. Struct. Biol. 91 (2025)","DOI":"10.1016\/j.sbi.2025.103002"},{"key":"11_CR2","doi-asserted-by":"crossref","unstructured":"Bryant, P., Pozzati, G., Elofsson, A.: Improved prediction of protein-protein interactions using AlphaFold2. Nat. Commun. 13 (2022)","DOI":"10.1038\/s41467-022-28865-w"},{"key":"11_CR3","unstructured":"Shen, T., et al.: TacoGFN: Target-conditioned GFlowNet for structure-based drug design. Trans. Mach. Learn. Res. (2024)"},{"key":"11_CR4","unstructured":"Shen, T., et al.: Compositional flows for 3D molecule and synthesis pathway co-design. In: AI for Accelerated Materials Design - ICLR 2025 (2025)"},{"key":"11_CR5","unstructured":"Seo, S., et al.: Generative flows on synthetic pathway for drug design. In: The Thirteenth International Conference on Learning Representations (2024)"},{"key":"11_CR6","unstructured":"Joshi, C.K., et al.: All-atom Diffusion Transformers: unified generative modelling of molecules and materials. In: AI for Accelerated Materials Design - ICLR 2025 (2025)"},{"key":"11_CR7","unstructured":"Cremer, J., Irwin, R., Tibot, A., Janet, J.P., Olsson, S., Clevert, D.-A.: FLOWR: flow matching for structure-aware de novo, interaction- and fragment-based ligand generation. In: CLR 2025 Workshop on Generative and Experimental Perspectives for Biomolecular Design, (2025)"},{"key":"11_CR8","doi-asserted-by":"crossref","unstructured":"Zhu, H., Zhou, R., Tang, J., Li, M.: PGMG: a pharmacophore-guided deep learning approach for bioactive molecular generation. Nat. Commun. (2023)","DOI":"10.1038\/s41467-023-41454-9"},{"key":"11_CR9","doi-asserted-by":"crossref","unstructured":"Gong, H., Liu, Q., Wu, S., Wang, L.: Text-guided molecule generation with diffusion language model. arXiv:2402.13040 [cs.LG] (2024)","DOI":"10.1609\/aaai.v38i1.27761"},{"key":"11_CR10","doi-asserted-by":"crossref","unstructured":"Xiang, Y., Zhao, H., Ma, C., Deng, Z.-H.: Instruction-based molecular graph generation with unified text-graph diffusion model. arXiv:2408.09896 [cs.LG] (2024)","DOI":"10.3233\/FAIA251096"},{"key":"11_CR11","doi-asserted-by":"crossref","unstructured":"Xu, M., Chen, H.: Tree-Invent: a novel multipurpose molecular generative model constrained with a topological tree. J. Chem. Inf. Modeling 63(22) (2023)","DOI":"10.1021\/acs.jcim.3c01626"},{"key":"11_CR12","unstructured":"Lee, S., Lee, S., Kawaguchi, K., Hwang, S.J.: Drug discovery with dynamic goal-aware fragments. In: The Twelfth International Conference on Learning Representations, Vienna, Austria (2024)"},{"key":"11_CR13","doi-asserted-by":"crossref","unstructured":"Pyrkov, A., et al.: Quantum computing for near-term applications in generative chemistry and drug discovery. Drug Discov. Today 28(8) (2023)","DOI":"10.1016\/j.drudis.2023.103675"},{"key":"11_CR14","doi-asserted-by":"crossref","unstructured":"Kao, P.-Y., et al.: Exploring the advantages of quantum generative adversarial networks in generative chemistry. J. Chem. Inf. Modeling 63(11) (2023)","DOI":"10.1021\/acs.jcim.3c00562"},{"key":"11_CR15","doi-asserted-by":"crossref","unstructured":"Sun, K., et al.: SynLlama: generating synthesizable molecules and their analogs with large language models. arXiv:2503.12602 [cs.LG] (2025)","DOI":"10.1021\/acscentsci.5c01285"},{"key":"11_CR16","doi-asserted-by":"crossref","unstructured":"Monteiro, N.R., Pereira, T.O., Machado, A.C.D., Oliveira, J.L., Abbasi, M., Arrais, J.P.: FSM-DDTR: end-to-end feedback strategy for multi-objective De Novo drug design using transformers. Comput. Biol. Med. 164 (2023)","DOI":"10.1016\/j.compbiomed.2023.107285"},{"issue":"210","key":"11_CR17","first-page":"1","volume":"24","author":"Y Bengio","year":"2023","unstructured":"Bengio, Y., Lahlou, S., Deleu, T., Hu, E.J., Tiwari, M., Bengio, E.: GFlowNet foundations. JMLR 24(210), 1\u201355 (2023)","journal-title":"JMLR"},{"key":"11_CR18","doi-asserted-by":"publisher","first-page":"3031","DOI":"10.1016\/j.jmb.2015.07.016","volume":"427","author":"T Vreven","year":"2015","unstructured":"Vreven, T., et al.: Docking benchmark version 5 and affinity benchmark version 2. J. Mol. Biol. 427, 3031\u20133041 (2015)","journal-title":"J. Mol. Biol."},{"key":"11_CR19","unstructured":"Seo, S., Kim, W.Y.: PharmacoNet: accelerating large-scale virtual screening by deep pharmacophore modeling. In: NeurIPS Workshop 2023 (2023)"},{"key":"11_CR20","doi-asserted-by":"crossref","unstructured":"Li, B., et al.: DrugMetric: quantitative drug-likeness scoring based on chemical space distance. Briefings Bioinform. 25(4) (2024)","DOI":"10.1093\/bib\/bbae321"},{"key":"11_CR21","doi-asserted-by":"crossref","unstructured":"Ertl, P., Schuffenhauer, A.: Estimation of synthetic accessibility score of drug-like molecules based on molecular complexity and fragment contributions. J. Cheminformatic 1 (2009)","DOI":"10.1186\/1758-2946-1-8"},{"key":"11_CR22","unstructured":"Ganea, O.-E., et al.: Github - equidock_public. https:\/\/github.com\/octavian-ganea\/equidock_public\/tree\/main\/data\/benchmark5.5\/cv\/cv_0. Accessed 10 May 2025"},{"key":"11_CR23","doi-asserted-by":"crossref","unstructured":"Chu, L.-S., Sarma, S., Gray, J.J.: Unified sampling and ranking for protein docking with DFMDock. bioRxiv (2024)","DOI":"10.1101\/2024.09.27.615401"},{"key":"11_CR24","doi-asserted-by":"crossref","unstructured":"Chen, Y., et al.: Deep generative model for drug design from protein target sequence. J. Cheminformatics 15 (2023)","DOI":"10.1186\/s13321-023-00702-2"},{"key":"11_CR25","unstructured":"Zhang, Z., Wang, M., Liu, Q.: FlexSBDD: structure-based drug design with flexible protein modeling. In: 38th Conference on Neural Information Processing Systems (NeurIPS 2024) (2024)"},{"key":"11_CR26","unstructured":"Zhou, X., et al.: Integrating protein dynamics into structure based drug design via full-atom stochastic flows. In: ICLR 2025 (2025)"},{"key":"11_CR27","doi-asserted-by":"crossref","unstructured":"Eyal Mazuz, G.S., Shapira, B., Rokach, L.: Molecule generation using transformers and policy gradient reinforcement learning. Sci. Rep. (2023)","DOI":"10.1038\/s41598-023-35648-w"}],"container-title":["Lecture Notes in Computer Science","Neural Information Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-95-4381-6_11","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,28]],"date-time":"2026-02-28T12:23:47Z","timestamp":1772281427000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-95-4381-6_11"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,16]]},"ISBN":["9789819543809","9789819543816"],"references-count":27,"URL":"https:\/\/doi.org\/10.1007\/978-981-95-4381-6_11","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11,16]]},"assertion":[{"value":"16 November 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"Authors declare that there is not any competing interest.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"value":"ICONIP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Neural Information Processing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Okinawa","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Japan","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":"20 November 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24 November 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"32","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iconip2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/iconip2025.apnns.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}