{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T20:32:34Z","timestamp":1742934754050,"version":"3.40.3"},"publisher-location":"Cham","reference-count":17,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031723582"},{"type":"electronic","value":"9783031723599"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"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":[[2024]]},"DOI":"10.1007\/978-3-031-72359-9_4","type":"book-chapter","created":{"date-parts":[[2024,9,18]],"date-time":"2024-09-18T12:28:54Z","timestamp":1726662534000},"page":"43-57","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Neural SHAKE: Geometric Constraints in\u00a0Graph Generative Models"],"prefix":"10.1007","author":[{"given":"Justin","family":"Diamond","sequence":"first","affiliation":[]},{"given":"Markus A.","family":"Lill","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,9,18]]},"reference":[{"key":"4_CR1","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1140\/epjst\/e2011-01525-9","volume":"200","author":"R Elber","year":"2011","unstructured":"Elber, R., Ruymgaart, A.P., Hess, B.: SHAKE parallelization. Eur. Phys. J. Spec. Top. 200, 211\u2013223 (2011)","journal-title":"Eur. Phys. J. Spec. Top."},{"key":"4_CR2","unstructured":"Landrum, G.: RDKit: open-source cheminformatics software. In: (2016). http:\/\/www.rdkit.org\/"},{"key":"4_CR3","unstructured":"Chen, R.T., et al.: Neural Ordinary Differential Equations (2018). In: arXiv preprint arXiv:1806.07366. https:\/\/arxiv.org\/abs\/1806.07366"},{"key":"4_CR4","unstructured":"Hoffmann, M., No\u00e9, F.: Generating Valid Euclidean Distance Matrices (2019). In: arXiv preprint arXiv:1910.03131. https:\/\/arxiv.org\/abs\/1910.03131"},{"key":"4_CR5","doi-asserted-by":"publisher","unstructured":"No\u00e9, F., et al.: Boltzmann generators: sampling equilibrium states of many-body systems with deep learning. Science 365 (6457), eaaw1147 (2019). ISSN: 0036\u20138075. https:\/\/doi.org\/10.1126\/science.aaw1147","DOI":"10.1126\/science.aaw1147"},{"key":"4_CR6","unstructured":"Zhang, T., et al.: ANODEV2: a coupled neural ODE framework. Adv. Neural Inf. Proc. Syst. 32 (2019)"},{"key":"4_CR7","unstructured":"Ho, J., Jain, A., Abbeel, P.: Denoising Diffusion Probabilistic Models (2020). In: arXiv preprint arXiv:2006.11239. https:\/\/arxiv.org\/abs\/2006.11239"},{"key":"4_CR8","doi-asserted-by":"publisher","unstructured":"Langevin, M., et al.: Scaffold-Constrained Molecular Generation. J. Chem. Inf. Model. 60(12), pp. 5637\u20135646 (2020). Publication Date: December 10, 2020 https:\/\/doi.org\/10.1021\/acs.jcim.0c01015https:\/\/doi.org\/10.1021\/acs.jcim.0c01015","DOI":"10.1021\/acs.jcim.0c01015"},{"key":"4_CR9","doi-asserted-by":"publisher","unstructured":"Wang, J., et al.: Multi-constraint molecular generation based on conditional transformer, knowledge distillation and reinforcement learning. Nat. Mach. Intell. 3(10), pp. 914\u2013922 (2021). https:\/\/doi.org\/10.1038\/s42256-021-00403-1https:\/\/doi.org\/10.1038\/s42256-021-00403-1","DOI":"10.1038\/s42256-021-00403-1"},{"key":"4_CR10","unstructured":"Xu, M., et al.: Geodiff: A geometric diffusion model for molecular conformation generation (2022). In: arXiv preprint arXiv:2203.02923. https:\/\/arxiv.org\/abs\/2203.02923"},{"key":"4_CR11","unstructured":"Corso, G., et al.: DiffDock: diffusion steps, twists, and turns for molecular docking. In: International Conference on Learning Representations (2023). https:\/\/arxiv.org\/abs\/2210 .01776"},{"key":"4_CR12","unstructured":"Fishman, N., et\u00a0al.: Diffusion models for constrained domains (2023). In: arXiv preprint arXiv:2304.05364"},{"key":"4_CR13","unstructured":"Hoogeboom, E., et al.: Equivariant diffusion for molecule generation in 3d. In: International Conference on Machine Learning, pp. 8867\u20138887 (2023). https:\/\/arxiv.org\/pdf\/2203.17003.pdf"},{"key":"4_CR14","unstructured":"Lou, A., Ermon, S.: Reflected diffusion models. In: International Conference on Machine Learning, pp. 22675\u2013 22701. PMLR (2023). https:\/\/proceedings.mlr.press\/v202\/lou23a\/lou23a.pdf"},{"key":"4_CR15","unstructured":"Murphy, K.P.: Probabilistic Machine Learning: Advanced Topics. MIT Press, 2023. http:\/\/probml.github.io\/book2"},{"key":"4_CR16","unstructured":"Fishman, N., et\u00a0al.: Metropolis sampling for constrained diffusion models. Adv. Neural Inf. Proc. Syst. 36 (2024)"},{"key":"4_CR17","unstructured":"Quan, W., et al.: Deep learning-based image and video inpainting: a survey. Int. J. Comput. Vis. 1\u201334 (2024). https:\/\/arxiv.org\/abs\/2401.03395"}],"container-title":["Lecture Notes in Computer Science","Artificial Neural Networks and Machine Learning \u2013 ICANN 2024"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-72359-9_4","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,18]],"date-time":"2024-09-18T12:29:29Z","timestamp":1726662569000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-72359-9_4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031723582","9783031723599"],"references-count":17,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-72359-9_4","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"18 September 2024","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":"Lugano","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Switzerland","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 September 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20 September 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"33","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icann2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}