ArticleIUCrJ2023
A deep learning solution for crystallographic structure determination.
Article in IUCrJ, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- GraPhAI: Neural Networks for Solving Centrosymmetric Crystal Structures.Journal of the American Chemical Society · 2026Article
- On artificial crystal structure generation for solving the phase problem with deep learning.Acta crystallographica. Section A, Foundations and advances · 2026Article
- Completion of partial structures using Patterson maps with the CrysFormer machine-learning model.Acta crystallographica. Section D, Structural biology · 2025Article
- Ab initio structure solutions from nanocrystalline powder diffraction data via diffusion models.Nature materials · 2025Article
- The phase-seeding method for solving non-centrosymmetric crystal structures: a challenge for artificial intelligence.Acta crystallographica. Section A, Foundations and advances · 2025Article
- Diffuse scattering from correlated electron systems.Science advances · 2025Review
- Genetic Algorithm-Enhanced Direct Method in Protein Crystallography.Molecules (Basel, Switzerland) · 2025Article
- Ligand identification in CryoEM and X-ray maps using deep learning.Bioinformatics (Oxford, England) · 2024Article
- Ligand Identification in CryoEM and X-ray Maps Using Deep Learning.bioRxiv : the preprint server for biology · 2024Article
- CrysFormer: Protein structure determination via Patterson maps, deep learning, and partial structure attention.Structural dynamics (Melville, N.Y.) · 2024Article
- Article
- Deep-learning map segmentation for protein X-ray crystallographic structure determination.Acta crystallographica. Section D, Structural biology · 2024Article
- Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
The general de novo solution of the crystallographic phase problem is difficult and only possible under certain conditions. This paper develops an initial pathway to a deep learning neural network approach for the phase problem in protein crystallography, based on a synthetic dataset of small fragments derived from a large well curated subset of solved structures in the Protein Data Bank (PDB). In particular, electron-density estimates of simple artificial systems are produced directly from corresponding Patterson maps using a convolutional neural network architecture as a proof of concept.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.