The AI-based phase-seeding (AI-PhaSeed) method: early applications and statistical analysis.

ABSTRACT: The crystallographic challenge of structure determination is nowadays effectively supported by advanced computational methods, such as direct methods and Patterson techniques, implemented in sophisticated software. With the rapid expansion of artificial intelligence (AI) across diverse scientific domains, exploring its potential contribution to structure solution and its ability to overcome the limitations of traditional approaches has become increasingly compelling. This work builds upon and extends the findings of two recent studies on AI-driven phasing. The first, by Larsen et al. [Science (2024), 385, 522-528], focused on designing and applying a neural network architecture to solve small structures (with unit-cell volumes up to 1000 ų), primarily withi

SUBMITTER: Carrozzini B 

PROVIDER: S-EPMC12810460 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature