Machine learning applications in macromolecular X-ray crystallography

被引：10

作者：

Vollmar, Melanie ^{[1
]}

Evans, Gwyndaf ^{[1
,2
]}

机构：

[1] Diamond Light Source Ltd, Harwell Sci & Innovat Campus, Harwell, Berks, England

[2] Rosalind Franklin Inst, Harwell Sci & Innovat Campus, Harwell, Berks, England

来源：

CRYSTALLOGRAPHY REVIEWS | 2021年 / 27卷 / 02期

基金：

英国生物技术与生命科学研究理事会;

关键词：

Machine learning; big data; automation; macromolecular X-ray crystallography; synchrotron; structural biology; PROTEIN-STRUCTURE DETERMINATION; STRUCTURE PREDICTION; STRUCTURAL GENOMICS; PATTERN-RECOGNITION; NEURAL-NETWORKS; AUTOMATED CLASSIFICATION; SECONDARY STRUCTURE; RECEPTIVE FIELDS; DATA-COLLECTION; WEB SERVER;

D O I：

10.1080/0889311X.2021.1982914

中图分类号：

O7 [晶体学];

学科分类号：

0702 ; 070205 ; 0703 ; 080501 ;

摘要：

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.

引用

页码：54 / 101

页数：48

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