{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,2]],"date-time":"2026-09-02T09:24:50Z","timestamp":1788341090400,"version":"build-2803163510"},"reference-count":35,"publisher":"Wiley","issue":"3","license":[{"start":{"date-parts":[[2026,7,27]],"date-time":"2026-07-27T00:00:00Z","timestamp":1785110400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,7,27]],"date-time":"2026-07-27T00:00:00Z","timestamp":1785110400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Journal of Microscopy"],"published-print":{"date-parts":[[2026,9]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Structural biology has entered a period of rapid methodological change defined by the convergence of cryo\u2010electron microscopy (cryo\u2010EM) and artificial intelligence (AI)\u2010driven structure prediction. Prior reviews have generally addressed cryo\u2010EM instrumentation or AI structure prediction individually, or their combination primarily from a structural biology or drug discovery perspective; a microscopy\u2010centred synthesis tracing this convergence from detector physics and image\u2010processing workflows through validation standards to in situ cryo\u2010electron tomography (cryo\u2010ET), while situating AI tools specifically as inputs to and complements of the cryo\u2010EM workflow, has been comparatively underexplored. This review provides such a synthesis, organised around: (i) the instrumentation, image\u2010processing, and validation advances underlying cryo\u2010EM's resolution gains, including detector physics, contrast transfer function (CTF) estimation, Bayesian and deep\u2010learning\u2010based particle picking and reconstruction, and map\u2010to\u2010model validation and deposition; (ii) cryo\u2010electron tomography and subtomogram averaging (STA) for in situ structural biology, including deep generative approaches to heterogeneity analysis; and (iii) the AlphaFold2, AlphaFold3, and RoseTTAFold All\u2010Atom systems, considered specifically in relation to how they interface with and depend upon cryo\u2010EM data for model building, refinement, and validation. Building on this microscopy\u2010centred account, we propose a three\u2010tier classification framework for selecting between AI\u2010primed, experiment\u2010led, and in situ integrative pipelines, and we summarise performance, transparency, and accessibility differences between leading AI tools. We illustrate these methods with examples from membrane proteins, ion channels, and viral glycoproteins, and briefly note implications for structure\u2010based drug design and for structural biology capacity in resource\u2010limited settings. Throughout, we distinguish between demonstrated capability, exceptional proof\u2010of\u2010principle results, and routine practice, and identify the experimental and computational limitations that constrain each approach.<\/jats:p>","DOI":"10.1111\/jmi.70154","type":"journal-article","created":{"date-parts":[[2026,7,27]],"date-time":"2026-07-27T09:06:06Z","timestamp":1785143166000},"page":"235-248","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Convergence of cryo\u2010electron microscopy and artificial intelligence in integrative structural biology: A critical review of advances, synergies, and implications for molecular biophysics and drug discovery"],"prefix":"10.1111","volume":"303","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0181-9336","authenticated-orcid":false,"family":"Abinawanto","sequence":"first","affiliation":[{"name":"Department of Biology Faculty of Mathematics and Natural Sciences Universitas Indonesia  Depok Indonesia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5206-2110","authenticated-orcid":false,"given":"Alfi","family":"Sophian","sequence":"additional","affiliation":[{"name":"Food and Drug Investigation Laboratory The Indonesian Food and Drug Authority (BPOM)  Jakarta Indonesia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,7,27]]},"reference":[{"key":"e_1_2_15_2_1","doi-asserted-by":"publisher","DOI":"10.1126\/science.1251652"},{"key":"e_1_2_15_3_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41586\u2010021\u201003819\u20102"},{"key":"e_1_2_15_4_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41586\u2010021\u201003828\u20101"},{"key":"e_1_2_15_5_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41586\u2010024\u201007487\u2010w"},{"key":"e_1_2_15_6_1","doi-asserted-by":"publisher","DOI":"10.1126\/science.adl2528"},{"key":"e_1_2_15_7_1","volume-title":"The Nobel Prize in chemistry 2024: Computational protein design and protein structure prediction","author":"Nobel Prize 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