Macromolecular crystallography provides mechanistic understanding of biological processes and can be applied in drug design. Nowadays, the use of robotic systems for crystal growth and diffraction analysis is widespread and high-throughput protein-to-structure pipelines for ligand and fragment screening are revolutionizing the field. However, the identification of crystals is still largely carried out through manual inspection, sometimes involving tens of thousands of images, which represents a bottleneck in an otherwise highly automated process. Here we describe AXIS, an AI-based Crystal Identification System combining the DINOv2 computer vision model, state-of-the-art transfer learning and MARCO, the largest crystallization dataset available to date, for automated crystal detection. AXIS can operate with both visible and UV light images and integrates a Lab-in-the-Loop approach combining ML and expert inputs for iterative learning and specialization. AXIS enables automated annotation of large crystallization image datasets with performance and accuracy comparable to that of human experts, and the Lab-in-the-Loop approach introduced here enables efficient adaptation to local conditions, facilitating widespread application, which has been a major limitation to date. AXIS can help to correct human errors in image annotation and removes critical bottlenecks, particularly in the context of extensive crystallization screens or high-throughput applications like fragment and ligand screening, unlocking the potential for higher levels of automation that are key in both fundamental and translational research.
Personnaz et al. (Thu,) studied this question.