Structural biology, the study of the 3D structure or shape of proteins and other biomolecules, has been transformed by breakthroughs from machine learning algorithms. While methods such as AlphaFold2 have made exponential progress in certain areas, many active and open challenges for the field remain, including modeling protein dynamics, predicting the structure of other classes of biomolecules such as RNA, and ultimately relating the structure of isolated proteins to the in vivo and contextual nature of their underlying function. These challenges are diverse and require interdisciplinary collaboration between ML and structural biology researchers. The 4th edition of the Machine Learning in Structural Biology (MLSB) workshop focuses on these challenges and opportunities. In a unique commitment of support, PRX Life journal has committed to waiving publication fees for accepted papers in a special collection for interested authors. We anticipate this workshop will be of significant interest to both ML researchers as well as computational / experimental biologists and will stimulate continued problem-solving and new directions in the field.
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