Machine learning applications in macromolecular X-ray crystallography

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.

Automated summary

After over 50 years of development, machine learning and artificial intelligence are now experiencing a significant surge in applications in various commercial and research sectors. In the context of X-ray crystallography and structural biology, machine learning is being integrated into expert and automated systems to predict experimental outcomes, analyze data, forecast crystal growth, and even generate macromolecular structures. This review offers a historical overview of AI and machine learning, discusses their application in crystallography, and presents current examples of their impact on macromolecular crystallography.