{"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.","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.","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.","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.","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.","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."},"file_date_updated":"2026-02-19T08:56:10Z","acknowledged_ssus":[{"_id":"ScienComp"}],"ddc":["000","540"],"abstract":[{"lang":"eng","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."}],"external_id":{"arxiv":["2502.09372"]},"title":"Inverse problems with experiment-guided AlphaFold","date_published":"2025-07-30T00:00:00Z","corr_author":"1","article_processing_charge":"No","quality_controlled":"1","month":"07","author":[{"id":"e957f5e5-91c9-11f0-a95f-e090f66ecb4d","last_name":"Maddipatla","first_name":"Sai A","full_name":"Maddipatla, Sai A"},{"last_name":"Sellam","id":"ef280fe0-91c9-11f0-a95f-8dea3f5bc513","first_name":"Nadav E","full_name":"Sellam, Nadav E"},{"id":"11d88cf5-91ca-11f0-a95f-edf9f08f47b7","last_name":"Bojan","full_name":"Bojan, Meital I","first_name":"Meital I"},{"first_name":"Sanketh","full_name":"Vedula, Sanketh","last_name":"Vedula","id":"94f2fe44-70fa-11f0-b76b-92922c09452b"},{"orcid":"0000-0002-9350-7606","full_name":"Schanda, Paul","first_name":"Paul","last_name":"Schanda","id":"7B541462-FAF6-11E9-A490-E8DFE5697425"},{"last_name":"Marx","first_name":"Ailie","full_name":"Marx, Ailie"},{"full_name":"Bronstein, Alexander","orcid":"0000-0001-9699-8730","first_name":"Alexander","id":"58f3726e-7cba-11ef-ad8b-e6e8cb3904e6","last_name":"Bronstein"}],"oa_version":"Published Version","date_created":"2026-02-18T12:11:17Z","file":[{"checksum":"f33230a6d59b7978d4cd72795e4e9059","date_created":"2026-02-19T08:56:10Z","success":1,"relation":"main_file","file_id":"21338","date_updated":"2026-02-19T08:56:10Z","content_type":"application/pdf","file_name":"2025_ICML_Maddipatla.pdf","file_size":1924177,"access_level":"open_access","creator":"dernst"}],"_id":"21327","publication":"Proceedings of the 42nd International Conference on Machine Learning","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","tmp":{"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","image":"/images/cc_by.png"},"year":"2025","publication_identifier":{"eissn":["2640-3498"]},"page":"42366 - 42393","language":[{"iso":"eng"}],"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. ","publication_status":"published","OA_type":"gold","status":"public","volume":267,"project":[{"name":"AlloSpace. The emergence and mechanisms of allostery","grant_number":"I05812","_id":"eb9c82eb-77a9-11ec-83b8-aadd536561cf"},{"name":"Structure and mechanism of the mitochondrial MIM insertase","_id":"bdb9578d-d553-11ed-ba76-ed5d39fce6f0","grant_number":"I06223"}],"date_updated":"2026-02-19T08:56:43Z","has_accepted_license":"1","publisher":"ML Research Press","type":"conference","intvolume":"       267","department":[{"_id":"PaSc"},{"_id":"AlBr"},{"_id":"GradSch"}],"oa":1,"alternative_title":["PMLR"],"OA_place":"publisher","arxiv":1,"conference":{"name":"ICML: International Conference on Machine Learning","location":"Vancouver, Canada","start_date":"2025-07-13","end_date":"2025-07-19"},"day":"30"}