%0 Journal Article
%T Artificial Intelligence at Cryo-EM–Medicinal Chemistry Interface: Structural Resolution in Drug Discovery
%A Noah Williams
%A Grace Collins
%A Ethan Brooks
%A Daniel Green
%J International Journal of Pharmaceutical And Phytopharmacological Research
%@ 2250-1029
%D 2026
%V 16
%N 1
%R 10.51847/Aim2kBzeJx
%P 126-135
%X Cryo-electron microscopy (cryo-EM) is increasingly moving from a structural-rescue technique toward a source of medicinal-chemistry information, while artificial intelligence (AI) is changing how density maps are interpreted, atomic models are built, conformational states are reconstructed, and ligand hypotheses are generated. The critical question is therefore no longer whether higher-resolution cryo-EM can support drug discovery, but which chemically consequential inferences become defensible at each evidential level.

A bounded integrative review was conducted across peer-reviewed Q1 literature published from 2017 through 2026. Search and citation-chaining procedures identified 56 records; after duplicate removal and screening, 44 full texts were assessed and 36 studies were included. Evidence was extracted at claim level across reconstruction, validation, ligand placement, conformational heterogeneity, structure prediction, structure-guided medicinal chemistry, and simulation. The literature shows rapid improvement in AI-assisted map-to-model workflows and a growing ability to obtain ligand-relevant structures for difficult targets. However, global resolution, local resolvability, atomic-model correctness, ligand identity, pose confidence, pocket-state assignment, and pharmacological meaning remain distinct inferential layers. The review therefore proposes an evidence-preserving interpretation in which increasing structural resolution expands the set of tractable medicinal-chemistry questions without automatically increasing confidence in every downstream chemical claim. AI and cryo-EM are becoming mutually reinforcing components of modern structure-enabled discovery, but their value depends on local validation, explicit uncertainty, experimentally grounded state interpretation, and orthogonal testing. The principal limitation is that prospective, multi-target validation of fully integrated workflows remains limited.
%U https://eijppr.com/article/artificial-intelligence-at-cryo-emmedicinal-chemistry-interface-structural-resolution-in-drug-disc-6rasnwurdue9jda