[{"date_published":"2026-03-17T00:00:00Z","main_file_link":[{"url":"https://openreview.net/forum?id=nZaPtHbd6N#discussion","open_access":"1"}],"day":"17","oa_version":"Published Version","abstract":[{"lang":"eng","text":"Cardiac T1 mapping provides critical quantitative insights into myocardial tissue composition, enabling the assessment of pathologies such as fibrosis, inflammation, and edema.\r\nHowever, the inherently dynamic nature of the heart imposes strict limits on acquisition\r\ntimes, making high-resolution T1 mapping a persistent challenge. Compressed sensing (CS)\r\napproaches have reduced scan durations by undersampling k-space and reconstructing images from partial data, and recent studies show that jointly optimizing the undersampling\r\npatterns with the reconstruction network can substantially improve performance. Still,\r\nmost current T1 mapping pipelines rely on static, hand-crafted masks that do not exploit\r\nthe full acceleration and accuracy potential. Furthermore, most existing methods do not\r\nlevarage the physical T1 decay model in optimization. In this work, we introduce T1-\r\nPILOT: an end-to-end method that explicitly incorporates the T1 signal relaxation model\r\ninto the sampling–reconstruction framework to guide the learning of non-Cartesian trajectories, cross-frame alignment, and T1 decay estimation. Through extensive experiments\r\non the CMRxRecon dataset, T1-PILOT significantly outperforms several baseline strategies (including learned single-mask and fixed radial or golden-angle sampling schemes),\r\nachieving higher T1 map fidelity at greater acceleration factors. In particular, we observe consistent gains in PSNR and VIF relative to existing methods, along with marked\r\nimprovements in delineating finer myocardial structures. Our results highlight that optimizing sampling trajectories in tandem with the physical relaxation model leads to both\r\nenhanced quantitative accuracy and reduced acquisition times. Code for reproducing all\r\nexperiments and results is available at https://github.com/tamirshor7/T1-PILOT"}],"type":"conference","publication_identifier":{"eissn":["2640-3498"]},"citation":{"ieee":"T. Shor, M. Freiman, C. Baskin, and A. M. Bronstein, “T1-PILOT: Physics-informed learned optimized trajectories for T1 mapping acceleration,” in <i>Medical Imaging with Deep Learning</i>, Taipei, Taiwan, vol. 315, pp. 1969–1982.","mla":"Shor, Tamir, et al. “T1-PILOT: Physics-Informed Learned Optimized Trajectories for T1 Mapping Acceleration.” <i>Medical Imaging with Deep Learning</i>, vol. 315, ML Research Press, pp. 1969–82.","ista":"Shor T, Freiman M, Baskin C, Bronstein AM. T1-PILOT: Physics-informed learned optimized trajectories for T1 mapping acceleration. Medical Imaging with Deep Learning. MIDL: Medical Imaging with Deep Learning, PMLR, vol. 315, 1969–1982.","ama":"Shor T, Freiman M, Baskin C, Bronstein AM. T1-PILOT: Physics-informed learned optimized trajectories for T1 mapping acceleration. In: <i>Medical Imaging with Deep Learning</i>. Vol 315. ML Research Press; :1969-1982.","short":"T. Shor, M. Freiman, C. Baskin, A.M. Bronstein, in:, Medical Imaging with Deep Learning, ML Research Press, n.d., pp. 1969–1982.","apa":"Shor, T., Freiman, M., Baskin, C., &#38; Bronstein, A. M. (n.d.). T1-PILOT: Physics-informed learned optimized trajectories for T1 mapping acceleration. In <i>Medical Imaging with Deep Learning</i> (Vol. 315, pp. 1969–1982). Taipei, Taiwan: ML Research Press.","chicago":"Shor, Tamir, Moti Freiman, Chaim Baskin, and Alex M. Bronstein. “T1-PILOT: Physics-Informed Learned Optimized Trajectories for T1 Mapping Acceleration.” In <i>Medical Imaging with Deep Learning</i>, 315:1969–82. ML Research Press, n.d."},"month":"03","status":"public","quality_controlled":"1","OA_place":"publisher","title":"T1-PILOT: Physics-informed learned optimized trajectories for T1 mapping acceleration","page":"1969-1982","tmp":{"image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","short":"CC BY (4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode"},"ddc":["000"],"date_updated":"2026-06-08T08:05:24Z","alternative_title":["PMLR"],"date_created":"2026-06-07T22:01:36Z","related_material":{"link":[{"relation":"software","url":"https://github.com/tamirshor7/T1-PILOT"}]},"scopus_import":"1","article_processing_charge":"No","_id":"21949","language":[{"iso":"eng"}],"publisher":"ML Research Press","department":[{"_id":"AlBr"}],"author":[{"first_name":"Tamir","full_name":"Shor, Tamir","last_name":"Shor"},{"full_name":"Freiman, Moti","last_name":"Freiman","first_name":"Moti"},{"first_name":"Chaim","full_name":"Baskin, Chaim","last_name":"Baskin"},{"last_name":"Bronstein","full_name":"Bronstein, Alexander","first_name":"Alexander","id":"58f3726e-7cba-11ef-ad8b-e6e8cb3904e6","orcid":"0000-0001-9699-8730"}],"publication":"Medical Imaging with Deep Learning","keyword":["Cardiac T1 Mapping","Trajectory Optimization and Reconstruction","PhysicsInformed Deep-Learning"],"corr_author":"1","intvolume":"       315","conference":{"name":"MIDL: Medical Imaging with Deep Learning","location":"Taipei, Taiwan","end_date":"2026-07-10","start_date":"2026-07-08"},"has_accepted_license":"1","OA_type":"gold","oa":1,"publication_status":"accepted","year":"2026","volume":315,"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87"},{"status":"public","quality_controlled":"1","OA_place":"publisher","title":"Experiment-guided AlphaFold3 resolves measurement-consistent protein ensembles","external_id":{"pmid":["42374114"]},"article_type":"original","tmp":{"image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","short":"CC BY (4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode"},"ddc":["570"],"date_updated":"2026-07-13T09:34:36Z","acknowledgement":"A. Marx acknowledges the financial support of the Helmsley Fellowships Program for Sustainability and Health. A.M.B. and P.S. are supported by the Institute of Science and Technology Austria Internal Project Call grant Generative Protein NMR. S.V. was supported in part by funding from the Eric and Wendy Schmidt Center at the Broad Institute of MIT and Harvard. Open access funding provided by Institute of Science and Technology (IST Austria).","dataavailabilitystatement":"All structures and metrics reported in this paper are openly available on Harvard Dataverse - https://doi.org/10.7910/DVN/PLYUHN. All code is openly available on GitHub (https://github.com/sai-advaith/guided_alphafold); the version used for this paper (version 0.9.1) is permanently archived on Zenodo https://doi.org/10.5281/zenodo.17307005","date_published":"2026-06-29T00:00:00Z","pmid":1,"researchdata_availability":"yes","main_file_link":[{"url":"https://doi.org/10.1038/s41587-026-03166-5","open_access":"1"}],"day":"29","oa_version":"Published Version","abstract":[{"text":"AlphaFold3 predicts highly accurate protein structures from sequence but tends to collapse to a single dominant conformation, even when the underlying structure is inherently heterogeneous. Moreover, its predictions are oblivious to experimental conditions that can alter local sequence conformation. In this work, we show that AlphaFold3 can be guided to match data obtained by nuclear magnetic resonance (NMR) spectroscopy, X-ray crystallography and cryogenic electron microscopy (cryo-EM) experiments and combinations thereof. Our approach can also incorporate data that explicitly report on dynamics, such as site-resolved order parameters. We demonstrate that this methodology generates compact structural ensembles whose ensemble-averaged observables agree with experiment, with fewer distance restraint violations than traditionally resolved NMR structures and with unmodeled alternate conformations uncovered in electron density. This methodology paves the way for experimentally aware predictive models that generate structural ensembles consistent with the measurements, potentially over multiple modalities, and that can be further refined toward thermodynamically grounded ensembles by incorporating energetics.","lang":"eng"}],"type":"journal_article","doi":"10.1038/s41587-026-03166-5","citation":{"ieee":"S. A. Maddipatla <i>et al.</i>, “Experiment-guided AlphaFold3 resolves measurement-consistent protein ensembles,” <i>Nature Biotechnology</i>. Springer Nature, 2026.","mla":"Maddipatla, Sai A., et al. “Experiment-Guided AlphaFold3 Resolves Measurement-Consistent Protein Ensembles.” <i>Nature Biotechnology</i>, Springer Nature, 2026, doi:<a href=\"https://doi.org/10.1038/s41587-026-03166-5\">10.1038/s41587-026-03166-5</a>.","ista":"Maddipatla SA, Sellam NE, Bojan MI, Masalitin V, Vedula S, Schanda P, Marx A, Bronstein AM. 2026. Experiment-guided AlphaFold3 resolves measurement-consistent protein ensembles. Nature Biotechnology.","short":"S.A. Maddipatla, N.E. Sellam, M.I. Bojan, V. Masalitin, S. Vedula, P. Schanda, A. Marx, A.M. Bronstein, Nature Biotechnology (2026).","ama":"Maddipatla SA, Sellam NE, Bojan MI, et al. Experiment-guided AlphaFold3 resolves measurement-consistent protein ensembles. <i>Nature Biotechnology</i>. 2026. doi:<a href=\"https://doi.org/10.1038/s41587-026-03166-5\">10.1038/s41587-026-03166-5</a>","chicago":"Maddipatla, Sai A, Nadav E Sellam, Meital I Bojan, Vova Masalitin, Sanketh Vedula, Paul Schanda, Ailie Marx, and Alex M. Bronstein. “Experiment-Guided AlphaFold3 Resolves Measurement-Consistent Protein Ensembles.” <i>Nature Biotechnology</i>. Springer Nature, 2026. <a href=\"https://doi.org/10.1038/s41587-026-03166-5\">https://doi.org/10.1038/s41587-026-03166-5</a>.","apa":"Maddipatla, S. A., Sellam, N. E., Bojan, M. I., Masalitin, V., Vedula, S., Schanda, P., … Bronstein, A. M. (2026). Experiment-guided AlphaFold3 resolves measurement-consistent protein ensembles. <i>Nature Biotechnology</i>. Springer Nature. <a href=\"https://doi.org/10.1038/s41587-026-03166-5\">https://doi.org/10.1038/s41587-026-03166-5</a>"},"publication_identifier":{"issn":["1087-0156"],"eissn":["1546-1696"]},"month":"06","supplementarymaterial":"yes","publication":"Nature Biotechnology","corr_author":"1","OA_type":"hybrid","has_accepted_license":"1","oa":1,"publication_status":"epub_ahead","PlanS_conform":"1","year":"2026","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","date_created":"2026-07-12T22:02:19Z","scopus_import":"1","article_processing_charge":"Yes (via OA deal)","language":[{"iso":"eng"}],"_id":"22268","das_tickbox":"1","publisher":"Springer Nature","department":[{"_id":"PaSc"},{"_id":"AlBr"},{"_id":"GradSch"}],"author":[{"id":"e957f5e5-91c9-11f0-a95f-e090f66ecb4d","first_name":"Sai A","full_name":"Maddipatla, Sai A","last_name":"Maddipatla"},{"first_name":"Nadav E","id":"ef280fe0-91c9-11f0-a95f-8dea3f5bc513","full_name":"Sellam, Nadav E","last_name":"Sellam"},{"last_name":"Bojan","full_name":"Bojan, Meital I","first_name":"Meital I","id":"11d88cf5-91ca-11f0-a95f-edf9f08f47b7"},{"last_name":"Masalitin","full_name":"Masalitin, Vova","id":"ff7958eb-91c9-11f0-a95f-f3bf65828cf6","first_name":"Vova"},{"last_name":"Vedula","full_name":"Vedula, Sanketh","first_name":"Sanketh"},{"id":"7B541462-FAF6-11E9-A490-E8DFE5697425","orcid":"0000-0002-9350-7606","first_name":"Paul","full_name":"Schanda, Paul","last_name":"Schanda"},{"full_name":"Marx, Ailie","last_name":"Marx","first_name":"Ailie"},{"first_name":"Alexander","id":"58f3726e-7cba-11ef-ad8b-e6e8cb3904e6","orcid":"0000-0001-9699-8730","full_name":"Bronstein, Alexander","last_name":"Bronstein"}]},{"tmp":{"image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","short":"CC BY (4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode"},"ddc":["520","000"],"article_type":"original","acknowledgement":"This research was partially supported by the Israeli Science Foundation grant 1834/24.","date_updated":"2026-02-17T12:33:19Z","quality_controlled":"1","OA_place":"publisher","status":"public","external_id":{"arxiv":["2507.10666"]},"title":"Machine Learning inference of stellar properties using integrated photometric and spectroscopic data","doi":"10.3847/1538-4357/ae0cbc","type":"journal_article","publication_identifier":{"issn":["0004-637X"],"eissn":["1538-4357"]},"citation":{"ieee":"I. Kamai, A. M. Bronstein, and H. B. Perets, “Machine Learning inference of stellar properties using integrated photometric and spectroscopic data,” <i>The Astrophysical Journal</i>, vol. 994. IOP Publishing, 2025.","mla":"Kamai, Ilay, et al. “Machine Learning Inference of Stellar Properties Using Integrated Photometric and Spectroscopic Data.” <i>The Astrophysical Journal</i>, vol. 994, 110, IOP Publishing, 2025, doi:<a href=\"https://doi.org/10.3847/1538-4357/ae0cbc\">10.3847/1538-4357/ae0cbc</a>.","ama":"Kamai I, Bronstein AM, Perets HB. Machine Learning inference of stellar properties using integrated photometric and spectroscopic data. <i>The Astrophysical Journal</i>. 2025;994. doi:<a href=\"https://doi.org/10.3847/1538-4357/ae0cbc\">10.3847/1538-4357/ae0cbc</a>","ista":"Kamai I, Bronstein AM, Perets HB. 2025. Machine Learning inference of stellar properties using integrated photometric and spectroscopic data. The Astrophysical Journal. 994, 110.","short":"I. Kamai, A.M. Bronstein, H.B. Perets, The Astrophysical Journal 994 (2025).","chicago":"Kamai, Ilay, Alex M. Bronstein, and Hagai B. Perets. “Machine Learning Inference of Stellar Properties Using Integrated Photometric and Spectroscopic Data.” <i>The Astrophysical Journal</i>. IOP Publishing, 2025. <a href=\"https://doi.org/10.3847/1538-4357/ae0cbc\">https://doi.org/10.3847/1538-4357/ae0cbc</a>.","apa":"Kamai, I., Bronstein, A. M., &#38; Perets, H. B. (2025). Machine Learning inference of stellar properties using integrated photometric and spectroscopic data. <i>The Astrophysical Journal</i>. IOP Publishing. <a href=\"https://doi.org/10.3847/1538-4357/ae0cbc\">https://doi.org/10.3847/1538-4357/ae0cbc</a>"},"month":"11","date_published":"2025-11-19T00:00:00Z","file":[{"file_name":"2025_AstrophysicalJournal_Kamai.pdf","success":1,"date_updated":"2026-02-17T12:32:18Z","access_level":"open_access","relation":"main_file","checksum":"255ffd6d664e6c2d1cffbaced650bd10","creator":"dernst","content_type":"application/pdf","file_id":"21302","file_size":16415089,"date_created":"2026-02-17T12:32:18Z"}],"DOAJ_listed":"1","abstract":[{"text":"Stellar astrophysics relies on diverse observational modalities—primarily photometric light curves and spectroscopic data—from which fundamental stellar properties are inferred. While machine learning (ML) has advanced analysis within individual modalities, the complementary information encoded across modalities remains largely underexploited. We present the dual embedding for stellar astronomy (DESA) model, a novel multimodal foundation model that integrates light curves and spectra to learn a unified, physically meaningful latent space for stars. DESA first trains separate modality-specific encoders using a hybrid supervised/self-supervised scheme, and then aligns them through DualFormer, a transformer-based cross-modal integration module tailored for astrophysical data. DualFormer combines cross- and self-attention, a novel dual-projection alignment loss, and a projection-space eigendecomposition that yields physically structured embeddings. We demonstrate that DESA significantly outperforms leading unimodal and self-supervised baselines across a range of tasks. In zero- and few-shot settings, DESA’s learned representations recover stellar color–magnitude and Hertzsprung–Russell diagrams with high fidelity (R2 = 0.92 for photometric regressions). In full fine-tuning, DESA achieves state-of-the-art accuracy for binary star detection (AUC = 0.99, AP = 1.00) and stellar age prediction (RMSE = 0.94 Gyr). As a compelling case, DESA naturally separates synchronized binaries from young stars—two populations with nearly identical light curves—purely from their embedded positions in UMAP space, without requiring external kinematic or luminosity information. DESA thus offers a powerful new framework for multimodal, data-driven stellar population analysis, enabling both accurate prediction and novel discovery.","lang":"eng"}],"oa_version":"Published Version","arxiv":1,"day":"19","publication_status":"published","PlanS_conform":"1","OA_type":"gold","has_accepted_license":"1","oa":1,"volume":994,"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","year":"2025","file_date_updated":"2026-02-17T12:32:18Z","publication":"The Astrophysical Journal","intvolume":"       994","department":[{"_id":"AlBr"}],"article_number":"110","author":[{"first_name":"Ilay","full_name":"Kamai, Ilay","last_name":"Kamai"},{"last_name":"Bronstein","full_name":"Bronstein, Alexander","first_name":"Alexander","id":"58f3726e-7cba-11ef-ad8b-e6e8cb3904e6","orcid":"0000-0001-9699-8730"},{"last_name":"Perets","full_name":"Perets, Hagai B.","first_name":"Hagai B."}],"article_processing_charge":"Yes","date_created":"2026-02-16T15:35:29Z","publisher":"IOP Publishing","language":[{"iso":"eng"}],"_id":"21246"},{"acknowledged_ssus":[{"_id":"ScienComp"}],"abstract":[{"text":"Proteins exist as a dynamic ensemble of multiple conformations, and these motions are often crucial for their functions. However, current structure prediction methods predominantly yield a single conformation, overlooking the conformational heterogeneity revealed by diverse experimental modalities. Here, we present a framework for building experiment-grounded protein structure generative models that infer conformational ensembles consistent with measured experimental data. The key idea is to treat stateof-the-art protein structure predictors (e.g., AlphaFold3) as sequence-conditioned structural priors, and cast ensemble modeling as posterior inference of protein structures given experimental measurements. Through extensive real-data experiments, we demonstrate the generality of our method to incorporate a variety of experimental measurements. In particular, our framework uncovers previously unmodeled conformational heterogeneity from crystallographic densities, and generates high-accuracy NMR ensembles orders of magnitude faster than the status quo. Notably, we demonstrate that our ensembles outperform AlphaFold3 (Abramson et al., 2024) and sometimes better fit experimental data than publicly deposited structures to the Protein Data Bank (PDB, Burley et al. (2017)). We believe that this approach will unlock building predictive models that fully embrace experimentally observed conformational diversity.","lang":"eng"}],"arxiv":1,"oa_version":"Published Version","day":"30","date_published":"2025-07-30T00:00:00Z","file":[{"date_updated":"2026-02-19T08:56:10Z","success":1,"file_name":"2025_ICML_Maddipatla.pdf","checksum":"f33230a6d59b7978d4cd72795e4e9059","creator":"dernst","access_level":"open_access","relation":"main_file","file_id":"21338","content_type":"application/pdf","file_size":1924177,"date_created":"2026-02-19T08:56:10Z"}],"month":"07","publication_identifier":{"eissn":["2640-3498"]},"citation":{"mla":"Maddipatla, Sai A., et al. “Inverse Problems with Experiment-Guided AlphaFold.” <i>Proceedings of the 42nd International Conference on Machine Learning</i>, vol. 267, ML Research Press, 2025, pp. 42366–93.","ieee":"S. A. Maddipatla <i>et al.</i>, “Inverse problems with experiment-guided AlphaFold,” in <i>Proceedings of the 42nd International Conference on Machine Learning</i>, Vancouver, Canada, 2025, vol. 267, pp. 42366–42393.","apa":"Maddipatla, S. A., Sellam, N. E., Bojan, M. I., Vedula, S., Schanda, P., Marx, A., &#38; Bronstein, A. M. (2025). Inverse problems with experiment-guided AlphaFold. In <i>Proceedings of the 42nd International Conference on Machine Learning</i> (Vol. 267, pp. 42366–42393). Vancouver, Canada: ML Research Press.","chicago":"Maddipatla, Sai A, Nadav E Sellam, Meital I Bojan, Sanketh Vedula, Paul Schanda, Ailie Marx, and Alex M. Bronstein. “Inverse Problems with Experiment-Guided AlphaFold.” In <i>Proceedings of the 42nd International Conference on Machine Learning</i>, 267:42366–93. ML Research Press, 2025.","short":"S.A. Maddipatla, N.E. Sellam, M.I. Bojan, S. Vedula, P. Schanda, A. Marx, A.M. Bronstein, in:, Proceedings of the 42nd International Conference on Machine Learning, ML Research Press, 2025, pp. 42366–42393.","ista":"Maddipatla SA, Sellam NE, Bojan MI, Vedula S, Schanda P, Marx A, Bronstein AM. 2025. Inverse problems with experiment-guided AlphaFold. Proceedings of the 42nd International Conference on Machine Learning. ICML: International Conference on Machine Learning, PMLR, vol. 267, 42366–42393.","ama":"Maddipatla SA, Sellam NE, Bojan MI, et al. Inverse problems with experiment-guided AlphaFold. In: <i>Proceedings of the 42nd International Conference on Machine Learning</i>. Vol 267. ML Research Press; 2025:42366-42393."},"type":"conference","page":"42366 - 42393","external_id":{"arxiv":["2502.09372"]},"title":"Inverse problems with experiment-guided AlphaFold","OA_place":"publisher","quality_controlled":"1","status":"public","alternative_title":["PMLR"],"acknowledgement":"This work was supported by the Israeli Science Foundation (ISF) grant number 1834/24. We acknowledge support from the Austrian Science Fund (FWF, grant numbers I5812-B and I6223) and the financial support of the Helmsley Fellowships Program for Sustainability and Health. This research uses resources of the Institute of Science and Technology Austria’s scientific computing cluster. ","date_updated":"2026-02-19T08:56:43Z","ddc":["000","540"],"tmp":{"image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","short":"CC BY (4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode"},"publisher":"ML Research Press","language":[{"iso":"eng"}],"_id":"21327","article_processing_charge":"No","date_created":"2026-02-18T12:11:17Z","author":[{"first_name":"Sai A","id":"e957f5e5-91c9-11f0-a95f-e090f66ecb4d","last_name":"Maddipatla","full_name":"Maddipatla, Sai A"},{"id":"ef280fe0-91c9-11f0-a95f-8dea3f5bc513","first_name":"Nadav E","full_name":"Sellam, Nadav E","last_name":"Sellam"},{"first_name":"Meital I","id":"11d88cf5-91ca-11f0-a95f-edf9f08f47b7","full_name":"Bojan, Meital I","last_name":"Bojan"},{"first_name":"Sanketh","id":"94f2fe44-70fa-11f0-b76b-92922c09452b","last_name":"Vedula","full_name":"Vedula, Sanketh"},{"full_name":"Schanda, Paul","last_name":"Schanda","orcid":"0000-0002-9350-7606","id":"7B541462-FAF6-11E9-A490-E8DFE5697425","first_name":"Paul"},{"first_name":"Ailie","last_name":"Marx","full_name":"Marx, Ailie"},{"orcid":"0000-0001-9699-8730","id":"58f3726e-7cba-11ef-ad8b-e6e8cb3904e6","first_name":"Alexander","last_name":"Bronstein","full_name":"Bronstein, Alexander"}],"department":[{"_id":"PaSc"},{"_id":"AlBr"},{"_id":"GradSch"}],"project":[{"_id":"eb9c82eb-77a9-11ec-83b8-aadd536561cf","grant_number":"I05812","name":"AlloSpace. The emergence and mechanisms of allostery"},{"name":"Structure and mechanism of the mitochondrial MIM insertase","grant_number":"I06223","_id":"bdb9578d-d553-11ed-ba76-ed5d39fce6f0"}],"conference":{"name":"ICML: International Conference on Machine Learning","end_date":"2025-07-19","start_date":"2025-07-13","location":"Vancouver, Canada"},"corr_author":"1","intvolume":"       267","publication":"Proceedings of the 42nd International Conference on Machine Learning","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","volume":267,"file_date_updated":"2026-02-19T08:56:10Z","year":"2025","publication_status":"published","oa":1,"has_accepted_license":"1","OA_type":"gold"},{"citation":{"ieee":"Y. Davidson, A. Philipp, S. Chakraborty, A. M. Bronstein, and R. Gershoni-Poranne, “How local is ‘local’? Deep learning reveals locality of the induced magnetic field of polycyclic aromatic hydrocarbons,” <i>Journal of Chemical Physics</i>, vol. 162, no. 14. AIP Publishing, 2025.","mla":"Davidson, Yair, et al. “How Local Is ‘Local’? Deep Learning Reveals Locality of the Induced Magnetic Field of Polycyclic Aromatic Hydrocarbons.” <i>Journal of Chemical Physics</i>, vol. 162, no. 14, 144101, AIP Publishing, 2025, doi:<a href=\"https://doi.org/10.1063/5.0257558\">10.1063/5.0257558</a>.","ama":"Davidson Y, Philipp A, Chakraborty S, Bronstein AM, Gershoni-Poranne R. How local is “local”? Deep learning reveals locality of the induced magnetic field of polycyclic aromatic hydrocarbons. <i>Journal of Chemical Physics</i>. 2025;162(14). doi:<a href=\"https://doi.org/10.1063/5.0257558\">10.1063/5.0257558</a>","short":"Y. Davidson, A. Philipp, S. Chakraborty, A.M. Bronstein, R. Gershoni-Poranne, Journal of Chemical Physics 162 (2025).","ista":"Davidson Y, Philipp A, Chakraborty S, Bronstein AM, Gershoni-Poranne R. 2025. How local is “local”? Deep learning reveals locality of the induced magnetic field of polycyclic aromatic hydrocarbons. Journal of Chemical Physics. 162(14), 144101.","chicago":"Davidson, Yair, Aviad Philipp, Sabyasachi Chakraborty, Alex M. Bronstein, and Renana Gershoni-Poranne. “How Local Is ‘Local’? Deep Learning Reveals Locality of the Induced Magnetic Field of Polycyclic Aromatic Hydrocarbons.” <i>Journal of Chemical Physics</i>. AIP Publishing, 2025. <a href=\"https://doi.org/10.1063/5.0257558\">https://doi.org/10.1063/5.0257558</a>.","apa":"Davidson, Y., Philipp, A., Chakraborty, S., Bronstein, A. M., &#38; Gershoni-Poranne, R. (2025). How local is “local”? Deep learning reveals locality of the induced magnetic field of polycyclic aromatic hydrocarbons. <i>Journal of Chemical Physics</i>. AIP Publishing. <a href=\"https://doi.org/10.1063/5.0257558\">https://doi.org/10.1063/5.0257558</a>"},"publication_identifier":{"eissn":["1089-7690"],"issn":["0021-9606"]},"month":"04","doi":"10.1063/5.0257558","type":"journal_article","day":"14","oa_version":"Published Version","abstract":[{"text":"We investigate the locality of magnetic response in polycyclic aromatic molecules using a novel deep-learning approach. Our method employs graph neural networks (GNNs) with a graph-of-rings representation to predict nucleus independent chemical shifts (NICS) in the space around the molecule. We train a series of models, each time reducing the size of the largest molecules used in training. The accuracy of prediction remains high (MAE < 0.5 ppm), even when training the model only on molecules with up to four rings, thus providing strong evidence for the locality of magnetic response. To overcome the known problem of generalization of GNNs, we implement a k-hop expansion strategy and succeed in achieving accurate predictions for molecules with up to 15 rings (almost 4 times the size of the largest training example). Our findings have implications for understanding the magnetic response in complex molecules and demonstrate a promising approach to overcoming GNN scalability limitations. Furthermore, the trained models enable rapid characterization, without the need for more expensive DFT calculations.","lang":"eng"}],"file":[{"file_size":7812182,"date_created":"2025-04-22T09:27:43Z","content_type":"application/pdf","file_id":"19606","access_level":"open_access","relation":"main_file","checksum":"20a31a4c506b52de863bab7d3ff989ef","creator":"dernst","success":1,"file_name":"2025_JourChemicalPhysics_Davidson.pdf","date_updated":"2025-04-22T09:27:43Z"}],"pmid":1,"date_published":"2025-04-14T00:00:00Z","isi":1,"date_updated":"2025-09-30T12:06:51Z","acknowledgement":"The authors express their gratitude to Professor Dr. Peter Chen for his continued support. The authors acknowledge the Branco Weiss Fellowship for supporting this research as part of a Society in Science grant and the Israel Science Foundation for financial support (Grant No. 1745/23 to R.G.-P.). R.G.-P. is a Branco Weiss Fellow, a Horev Fellow, and an Alon Scholarship recipient. A.M.B. was supported by the ERC StG EARS and the Israeli Science Foundation.","article_type":"original","tmp":{"legal_code_url":"https://creativecommons.org/licenses/by-nc/4.0/legalcode","short":"CC BY-NC (4.0)","name":"Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)","image":"/images/cc_by_nc.png"},"ddc":["000"],"title":"How local is “local”? Deep learning reveals locality of the induced magnetic field of polycyclic aromatic hydrocarbons","external_id":{"isi":["001466311300030"],"pmid":["40197568"]},"status":"public","quality_controlled":"1","OA_place":"publisher","author":[{"first_name":"Yair","full_name":"Davidson, Yair","last_name":"Davidson"},{"last_name":"Philipp","full_name":"Philipp, Aviad","first_name":"Aviad"},{"first_name":"Sabyasachi","last_name":"Chakraborty","full_name":"Chakraborty, Sabyasachi"},{"full_name":"Bronstein, Alexander","last_name":"Bronstein","id":"58f3726e-7cba-11ef-ad8b-e6e8cb3904e6","first_name":"Alexander","orcid":"0000-0001-9699-8730"},{"first_name":"Renana","full_name":"Gershoni-Poranne, Renana","last_name":"Gershoni-Poranne"}],"article_number":"144101","project":[{"name":"Acoustics-based drone navigation and interaction","_id":"92f4a086-16d5-11f0-9cad-c929f5c58b0c","grant_number":"863839"}],"department":[{"_id":"AlBr"}],"language":[{"iso":"eng"}],"_id":"19595","publisher":"AIP Publishing","date_created":"2025-04-20T22:01:28Z","related_material":{"link":[{"relation":"software","url":"https://gitlab.com/porannegroup/magnetic_locality"}]},"scopus_import":"1","article_processing_charge":"Yes (in subscription journal)","year":"2025","file_date_updated":"2025-04-22T09:27:43Z","volume":162,"user_id":"317138e5-6ab7-11ef-aa6d-ffef3953e345","OA_type":"hybrid","has_accepted_license":"1","oa":1,"publication_status":"published","intvolume":"       162","corr_author":"1","publication":"Journal of Chemical Physics","issue":"14"},{"intvolume":"        27","publication":"Computational and Structural Biotechnology Journal","file_date_updated":"2025-08-04T06:25:23Z","year":"2025","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","volume":27,"oa":1,"has_accepted_license":"1","OA_type":"gold","publication_status":"published","PlanS_conform":"1","_id":"20100","language":[{"iso":"eng"}],"publisher":"Elsevier","related_material":{"link":[{"relation":"software","url":"https://github.com/sankethvedula/AFChimera"}],"record":[{"relation":"software","status":"public","id":"20103"}]},"date_created":"2025-08-03T22:01:31Z","article_processing_charge":"Yes","scopus_import":"1","author":[{"last_name":"Vedula","full_name":"Vedula, Sanketh","id":"94f2fe44-70fa-11f0-b76b-92922c09452b","first_name":"Sanketh"},{"last_name":"Bronstein","full_name":"Bronstein, Alexander","first_name":"Alexander","orcid":"0000-0001-9699-8730","id":"58f3726e-7cba-11ef-ad8b-e6e8cb3904e6"},{"first_name":"Ailie","full_name":"Marx, Ailie","last_name":"Marx"}],"department":[{"_id":"AlBr"}],"title":"Improving prediction accuracy in chimeric proteins with windowed multiple sequence alignment","page":"3292-3298","external_id":{"isi":["001583543100001"]},"status":"public","OA_place":"publisher","quality_controlled":"1","date_updated":"2025-11-27T14:09:59Z","acknowledgement":"AM acknowledges the financial support of the Helmsley Fellowships Program for Sustainability and Health. AMB is supported by the Schmidt Chair in Artificial Intelligence.","article_type":"original","ddc":["000","570"],"tmp":{"image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","short":"CC BY (4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode"},"day":"27","abstract":[{"lang":"eng","text":"A key step in protein structure prediction involves the detection of co-evolving pairs of residues, a signal for spatial proximity. This information is gleaned from multiple sequence alignment and underscores Alphafold’s structure prediction for almost every known protein. A simple means to create proteins beyond those found in nature, is by unnaturally fusing together two known proteins or protein parts. Here we demonstrate that structured peptides are predicted with significantly reduced accuracy when added to the terminal ends of scaffold proteins. Appending the multiple sequence alignment for the individual peptide tags to that of the scaffold protein often restores prediction accuracy. This work suggests that this windowed multiple sequence alignment approach can be a useful tool for predicting the structure of fused, chimeric proteins."}],"oa_version":"Published Version","DOAJ_listed":"1","file":[{"date_updated":"2025-08-04T06:25:23Z","success":1,"file_name":"2025_CompStrucBiotechJour_Vedula.pdf","checksum":"78d01f30fc1dc11dd2bd1d7bb7ac8a62","creator":"dernst","access_level":"open_access","relation":"main_file","file_id":"20104","content_type":"application/pdf","date_created":"2025-08-04T06:25:23Z","file_size":6609770}],"isi":1,"date_published":"2025-06-27T00:00:00Z","month":"06","citation":{"mla":"Vedula, Sanketh, et al. “Improving Prediction Accuracy in Chimeric Proteins with Windowed Multiple Sequence Alignment.” <i>Computational and Structural Biotechnology Journal</i>, vol. 27, Elsevier, 2025, pp. 3292–98, doi:<a href=\"https://doi.org/10.1016/j.csbj.2025.07.039\">10.1016/j.csbj.2025.07.039</a>.","ieee":"S. Vedula, A. M. Bronstein, and A. Marx, “Improving prediction accuracy in chimeric proteins with windowed multiple sequence alignment,” <i>Computational and Structural Biotechnology Journal</i>, vol. 27. Elsevier, pp. 3292–3298, 2025.","apa":"Vedula, S., Bronstein, A. M., &#38; Marx, A. (2025). Improving prediction accuracy in chimeric proteins with windowed multiple sequence alignment. <i>Computational and Structural Biotechnology Journal</i>. Elsevier. <a href=\"https://doi.org/10.1016/j.csbj.2025.07.039\">https://doi.org/10.1016/j.csbj.2025.07.039</a>","chicago":"Vedula, Sanketh, Alex M. Bronstein, and Ailie Marx. “Improving Prediction Accuracy in Chimeric Proteins with Windowed Multiple Sequence Alignment.” <i>Computational and Structural Biotechnology Journal</i>. Elsevier, 2025. <a href=\"https://doi.org/10.1016/j.csbj.2025.07.039\">https://doi.org/10.1016/j.csbj.2025.07.039</a>.","ama":"Vedula S, Bronstein AM, Marx A. Improving prediction accuracy in chimeric proteins with windowed multiple sequence alignment. <i>Computational and Structural Biotechnology Journal</i>. 2025;27:3292-3298. doi:<a href=\"https://doi.org/10.1016/j.csbj.2025.07.039\">10.1016/j.csbj.2025.07.039</a>","short":"S. Vedula, A.M. Bronstein, A. Marx, Computational and Structural Biotechnology Journal 27 (2025) 3292–3298.","ista":"Vedula S, Bronstein AM, Marx A. 2025. Improving prediction accuracy in chimeric proteins with windowed multiple sequence alignment. Computational and Structural Biotechnology Journal. 27, 3292–3298."},"publication_identifier":{"eissn":["2001-0370"]},"type":"journal_article","doi":"10.1016/j.csbj.2025.07.039"},{"OA_place":"repository","status":"public","title":"Replication Data for: \"Improving Prediction Accuracy in Chimeric Proteins with Windowed Multiple Sequence Alignment\"","tmp":{"short":"CC0 (1.0)","legal_code_url":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","name":"Creative Commons Public Domain Dedication (CC0 1.0)","image":"/images/cc_0.png"},"ddc":["000"],"has_accepted_license":"1","oa":1,"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","year":"2025","date_updated":"2025-11-27T14:09:58Z","date_published":"2025-06-27T00:00:00Z","main_file_link":[{"url":"https://doi.org/10.7910/DVN/DYEBVM","open_access":"1"}],"article_processing_charge":"No","date_created":"2025-08-04T06:18:55Z","related_material":{"record":[{"status":"public","relation":"used_for_analysis_in","id":"20100"}]},"abstract":[{"text":"Official implementation, windowed MSAs, and the predictions as reported in the manuscript titled \"Improving Prediction Accuracy in Chimeric Proteins with Windowed Multiple Sequence Alignment\". (2025-06-27)","lang":"eng"}],"publisher":"Harvard Dataverse","oa_version":"Published Version","_id":"20103","day":"27","doi":"10.7910/DVN/DYEBVM","type":"research_data_reference","department":[{"_id":"AlBr"}],"author":[{"full_name":"Vedula, Sanketh","last_name":"Vedula","first_name":"Sanketh","id":"94f2fe44-70fa-11f0-b76b-92922c09452b"},{"full_name":"Bronstein, Alexander","last_name":"Bronstein","first_name":"Alexander","id":"58f3726e-7cba-11ef-ad8b-e6e8cb3904e6","orcid":"0000-0001-9699-8730"},{"first_name":"Ailie","last_name":"Marx","full_name":"Marx, Ailie"}],"citation":{"ieee":"S. Vedula, A. M. Bronstein, and A. Marx, “Replication Data for: ‘Improving Prediction Accuracy in Chimeric Proteins with Windowed Multiple Sequence Alignment.’” Harvard Dataverse, 2025.","mla":"Vedula, Sanketh, et al. <i>Replication Data for: “Improving Prediction Accuracy in Chimeric Proteins with Windowed Multiple Sequence Alignment.”</i> Harvard Dataverse, 2025, doi:<a href=\"https://doi.org/10.7910/DVN/DYEBVM\">10.7910/DVN/DYEBVM</a>.","short":"S. Vedula, A.M. Bronstein, A. Marx, (2025).","ista":"Vedula S, Bronstein AM, Marx A. 2025. Replication Data for: ‘Improving Prediction Accuracy in Chimeric Proteins with Windowed Multiple Sequence Alignment’, Harvard Dataverse, <a href=\"https://doi.org/10.7910/DVN/DYEBVM\">10.7910/DVN/DYEBVM</a>.","ama":"Vedula S, Bronstein AM, Marx A. Replication Data for: “Improving Prediction Accuracy in Chimeric Proteins with Windowed Multiple Sequence Alignment.” 2025. doi:<a href=\"https://doi.org/10.7910/DVN/DYEBVM\">10.7910/DVN/DYEBVM</a>","chicago":"Vedula, Sanketh, Alex M. Bronstein, and Ailie Marx. “Replication Data for: ‘Improving Prediction Accuracy in Chimeric Proteins with Windowed Multiple Sequence Alignment.’” Harvard Dataverse, 2025. <a href=\"https://doi.org/10.7910/DVN/DYEBVM\">https://doi.org/10.7910/DVN/DYEBVM</a>.","apa":"Vedula, S., Bronstein, A. M., &#38; Marx, A. (2025). Replication Data for: “Improving Prediction Accuracy in Chimeric Proteins with Windowed Multiple Sequence Alignment.” Harvard Dataverse. <a href=\"https://doi.org/10.7910/DVN/DYEBVM\">https://doi.org/10.7910/DVN/DYEBVM</a>"},"month":"06"},{"tmp":{"image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","short":"CC BY (4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode"},"ddc":["000"],"date_updated":"2025-12-01T07:22:09Z","status":"public","quality_controlled":"1","OA_place":"publisher","title":"From lab to wrist: Bridging metabolic monitoring and consumer wearables for heart rate and oxygen consumption modeling","external_id":{"arxiv":["2505.00101"]},"page":"60-77","type":"conference","doi":"10.1145/3716553.3750815","publication_identifier":{"isbn":["9798400714993"]},"citation":{"apa":"Gahtan, B., Vedula, S., Samuelly Leichtag, G., Kodesh, E., &#38; Bronstein, A. M. (2025). From lab to wrist: Bridging metabolic monitoring and consumer wearables for heart rate and oxygen consumption modeling. In <i>Proceedings of the 27th International Conference on Multimodal Interaction</i> (pp. 60–77). Canberra, Australia: Association for Computing Machinery. <a href=\"https://doi.org/10.1145/3716553.3750815\">https://doi.org/10.1145/3716553.3750815</a>","chicago":"Gahtan, Barak, Sanketh Vedula, Gil Samuelly Leichtag, Einat Kodesh, and Alex M. Bronstein. “From Lab to Wrist: Bridging Metabolic Monitoring and Consumer Wearables for Heart Rate and Oxygen Consumption Modeling.” In <i>Proceedings of the 27th International Conference on Multimodal Interaction</i>, 60–77. Association for Computing Machinery, 2025. <a href=\"https://doi.org/10.1145/3716553.3750815\">https://doi.org/10.1145/3716553.3750815</a>.","ista":"Gahtan B, Vedula S, Samuelly Leichtag G, Kodesh E, Bronstein AM. 2025. From lab to wrist: Bridging metabolic monitoring and consumer wearables for heart rate and oxygen consumption modeling. Proceedings of the 27th International Conference on Multimodal Interaction. ICMI: International Conference on Multimodal Interaction, 60–77.","ama":"Gahtan B, Vedula S, Samuelly Leichtag G, Kodesh E, Bronstein AM. From lab to wrist: Bridging metabolic monitoring and consumer wearables for heart rate and oxygen consumption modeling. In: <i>Proceedings of the 27th International Conference on Multimodal Interaction</i>. Association for Computing Machinery; 2025:60-77. doi:<a href=\"https://doi.org/10.1145/3716553.3750815\">10.1145/3716553.3750815</a>","short":"B. Gahtan, S. Vedula, G. Samuelly Leichtag, E. Kodesh, A.M. Bronstein, in:, Proceedings of the 27th International Conference on Multimodal Interaction, Association for Computing Machinery, 2025, pp. 60–77.","mla":"Gahtan, Barak, et al. “From Lab to Wrist: Bridging Metabolic Monitoring and Consumer Wearables for Heart Rate and Oxygen Consumption Modeling.” <i>Proceedings of the 27th International Conference on Multimodal Interaction</i>, Association for Computing Machinery, 2025, pp. 60–77, doi:<a href=\"https://doi.org/10.1145/3716553.3750815\">10.1145/3716553.3750815</a>.","ieee":"B. Gahtan, S. Vedula, G. Samuelly Leichtag, E. Kodesh, and A. M. Bronstein, “From lab to wrist: Bridging metabolic monitoring and consumer wearables for heart rate and oxygen consumption modeling,” in <i>Proceedings of the 27th International Conference on Multimodal Interaction</i>, Canberra, Australia, 2025, pp. 60–77."},"month":"10","file":[{"file_id":"20713","content_type":"application/pdf","date_created":"2025-12-01T07:19:06Z","file_size":3045062,"date_updated":"2025-12-01T07:19:06Z","success":1,"file_name":"2025_ICMI_Gahtan.pdf","checksum":"f793472a71d27012244567b499a4967f","creator":"dernst","access_level":"open_access","relation":"main_file"}],"date_published":"2025-10-12T00:00:00Z","day":"12","abstract":[{"text":"Understanding physiological responses during running is critical for performance optimization, tailored training prescriptions, and athlete health management. We introduce a comprehensive framework—what we believe to be the first capable of predicting instantaneous oxygen consumption (VO2) trajectories exclusively from consumer-grade wearable data. Our approach employs two complementary physiological models: (1) accurate modeling of heart rate (HR) dynamics via a physiologically constrained ordinary differential equation (ODE) and neural Kalman filter, trained on over 3 million HR observations, achieving 1-second interval predictions with mean absolute errors as low as 2.81 bpm (correlation 0.87); and (2) leveraging the principles of precise HR modeling, a novel VO2 prediction architecture requiring only the initial second of VO2 data for calibration, enabling robust, sequence-to-sequence metabolic demand estimation. Despite relying solely on smartwatch and chest-strap data, our method achieves mean absolute percentage errors of approximately 13%, effectively capturing rapid physiological transitions and steady-state conditions across diverse running intensities. Our synchronized dataset, complemented by blood lactate measurements, further lays the foundation for future noninvasive metabolic zone identification. By embedding physiological constraints within modern machine learning, this framework democratizes advanced metabolic monitoring, bridging laboratory-grade accuracy and everyday accessibility, thus empowering both elite athletes and recreational fitness enthusiasts.","lang":"eng"}],"oa_version":"Published Version","arxiv":1,"OA_type":"hybrid","has_accepted_license":"1","oa":1,"publication_status":"published","year":"2025","file_date_updated":"2025-12-01T07:19:06Z","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","publication":"Proceedings of the 27th International Conference on Multimodal Interaction","corr_author":"1","conference":{"name":"ICMI: International Conference on Multimodal Interaction","location":"Canberra, Australia","end_date":"2025-10-17","start_date":"2025-10-13"},"department":[{"_id":"AlBr"}],"author":[{"full_name":"Gahtan, Barak","last_name":"Gahtan","first_name":"Barak"},{"full_name":"Vedula, Sanketh","last_name":"Vedula","first_name":"Sanketh"},{"first_name":"Gil","last_name":"Samuelly Leichtag","full_name":"Samuelly Leichtag, Gil"},{"first_name":"Einat","full_name":"Kodesh, Einat","last_name":"Kodesh"},{"orcid":"0000-0001-9699-8730","first_name":"Alexander","id":"58f3726e-7cba-11ef-ad8b-e6e8cb3904e6","last_name":"Bronstein","full_name":"Bronstein, Alexander"}],"date_created":"2025-11-30T23:02:08Z","scopus_import":"1","article_processing_charge":"No","language":[{"iso":"eng"}],"_id":"20707","publisher":"Association for Computing Machinery"}]