Machine learning applications in macromolecular X-ray crystallography

DOI: 10.1080/0889311X.2021.1982914

Authors: Melanie Vollmar (Diamond Light Source) , Gwyndaf Evans (Diamond Light Source)
Co-authored by industrial partner: No

Type: Journal Paper
Journal: Crystallography Reviews , VOL 73 , PAGES 1 - 48

State: Published (Approved)
Published: October 2021

Abstract: After more than half a century of evolution, machine learning and artificial intelligence, in general, are entering a truly exciting era of broad application in commercial and research sectors. In X-ray crystallography, and its application to structural biology, machine learning is finding a home within expert and automated systems, is forecasting experiment and data analysis outcomes, is predicting whether crystals can be grown and even generating macromolecular structures. This review provides a historical perspective on AI and machine learning, offers an introduction and guide to its application in crystallography and concludes with topical examples of how it is currently influencing macromolecular crystallography.

Journal Keywords: Machine learning; big data; automation; macromolecular X-ray crystallography; synchrotron; structural biology

Subject Areas: Information and Communication Technology, Biology and Bio-materials


Technical Areas:

Added On: 11/10/2021 09:16

Documents:
0889311X.2021.1982914.pdf

Discipline Tags:

Artificial Intelligence Information & Communication Technologies Structural biology Data processing Life Sciences & Biotech

Technical Tags: