Best Practices of Using AI-Based Models in Crystallography and Their Impact in Structural Biology

The recent breakthrough made in the field of three-dimensional (3D) structure prediction by artificial intelligence softwares, such as initially AlphaFold2 (AF2) and RosettaFold (RF) and more recently large Language Models (LLM), has revolutionized the field of structural biology in particular and also biology as a whole. These models have clearly generated great enthusiasm within the scientific community, and different applications of these 3D predictions are regularly described in scientific articles, demonstrating the impact of these high-quality models. Despite the acknowledged high accuracy of these models in general, it seems important to make users of these models aware of the wealth of information they offer and to encourage them to make the best use of them. Here, we focus on the impact of these models in a specific application by structural biologists using X-ray crystallography. We propose guidelines to prepare models to be used for molecular replacement trials to solve the phase problem. We also encourage colleagues to share as much detail as possible about how they use these models in their research, where the models did not yield correct molecular replacement solutions, and how these predictions fit with their experimental 3D structure. We feel this is important to improve the pipelines using these models and also to get feedback on their overall quality.