This meeting will focus on the intersection of machine learning with structural biology, bringing together researchers working on development of core computational methods for proteins and molecules, their integration with experimental data, and their application at scale to biological problems.

The meeting aims to:

1. present the latest methodological developments in this fast moving field, including design and prediction of stable structures and conformational ensembles
2. showcase how machine learning is changing the practice of structural biology
3. illustrate how the availability of large structural databases opens the door to new types of biological questions
4. build bridges between method developers focused on de novo prediction and design and those incorporating experimental structural data
5. delineate the frontiers of machine learning for macromolecular structures  and function

Attendees will gain a better understanding of what is currently possible using state-of-the-art tools, where the most promising applications are, and what new developments lie over the horizon. By engaging related but normally disparate fields, this meeting will spark new collaborations spanning method developers, data generators, and biological hypothesis testers to ultimately drive applications that will advance biology and medicine. Typically these communities are siloed, with machine learning conferences covering key methodological developments but lacking attendees from the life sciences. Conversely, the most exciting applications of protein structure prediction are typically presented at biology-focused symposia such as Keystone, which attract few machine learning researchers. This meeting will connect these critical communities together to collectively address gaps between these fields and accelerate the development of machine learning approaches that will shape the future of structural biology.  This first-of-its-kind conference will act as a nexus to spur innovative methodology and applications to serve unmet needs and yield transformative insights in structural biology.

This conference will be held jointly with the Keystone Symposium on Imaging Biomolecules Across Scales:  From Atoms to Tissues to enable cross-disciplinary insights and collaborations towards integrating the state-of-the-art in cryoEM/ET technologies with machine learning capabilities.