Machine Learning inStructural Biology
Join leading researchers at the intersection of machine learning and structural biology and explore cutting-edge advances in predictive modelling and design of biological macromolecules.
Key Topics
- Prediction of biomolecular structures, complexes, and interactions
- Design of or generative models for structure and/or sequence
- Methods for structure determination / biophysics (Cryo-EM/ET, NMR, crystallography, single-molecule methods, etc.)
- Conformational change, ensembles, and dynamics
- Integration of biomolecular physics
- Function and property prediction
- Structural bioinformatics and systems biology
- Therapeutic screening and design
- Language models and other implicit representations of protein structure
- Forward-looking position papers
Important Dates
Submissions with open-sourced code, models, or datasets will be prioritized for spotlight talks, but all are eligible for poster presentation.
This workshop is non-archival; accepted papers may be shared on our website. Concurrent submissions and work-in-progress or position papers are welcome.
Invited Speakers
Hannah K. Wayment-Steele
UW Madison
Biomolecular Dynamics
Sam Rodrigues
Future House
AI Agents
Bruno Correia
EPFL
Protein Design
Mile Sikic
Genome Institute of Singapore
Nucleic Acid Structure Prediction
Ellen Zhong
Princeton University
Cryo-EM
Pratyush Tiwari
University of Maryland, College Park
Molecular Simulation
Anastassia Vorobieva
VIB-VUB Center for Structural Biology
Protein Design
Our Sponsors
If you are interested in sponsoring MLSB please reach out via e-mail.