CryoRL: Reinforcement Learning Enables Efficient Cryo-EM Data Collection
This work addresses the need for more efficient and objective cryo-EM data collection for structural biologists, representing an incremental improvement by applying an existing method to a new domain.
The paper tackled the problem of inefficient and labor-intensive cryo-EM data collection by formulating it as an optimization task and using reinforcement learning to plan data collection, resulting in better performance than average users under similar settings.
Single-particle cryo-electron microscopy (cryo-EM) has become one of the mainstream structural biology techniques because of its ability to determine high-resolution structures of dynamic bio-molecules. However, cryo-EM data acquisition remains expensive and labor-intensive, requiring substantial expertise. Structural biologists need a more efficient and objective method to collect the best data in a limited time frame. We formulate the cryo-EM data collection task as an optimization problem in this work. The goal is to maximize the total number of good images taken within a specified period. We show that reinforcement learning offers an effective way to plan cryo-EM data collection, successfully navigating heterogenous cryo-EM grids. The approach we developed, cryoRL, demonstrates better performance than average users for data collection under similar settings.