From Neural Networks to Protein Design: The Evolution of Machine Learning in Protein Science
- Colloquium
From Neural Networks to Protein Design: The Evolution of Machine Learning in Protein Science
This colloquium explores the groundbreaking work recognized by the 2024 Nobel Prize in Chemistry, awarded to David Baker, Demis Hassabis, and John Jumper. We examine the origins of these discoveries and how they represent a culmination of decades of progress in machine learning in combination with physics-based simulation techniques and its application to protein science.
Our presentation aims to provide physics students with an appreciation for the interdisciplinary nature of Nobel Prize-winning research and the far-reaching implications of advances in machine learning for biotechnology and beyond.
![Band structure of 2D semimetal based on HgTe quantum well. Experimental points are obtained from the analysis of the cyclotron resonance in the quasi-classical approximation. Solid lines are predictions of the kp theory with no free parameters. Splitting of the conduction (e1,2) and valence (h1) band is due to the quantum confinement. [J. Gospodaric, AP, et al., PRB 104, 115307].](/storages/physik/_processed_/b/5/csm_Kolloquium_Pimenov_0fa7761647.png)





