Biomolecular nuclear magnetic resonance (NMR) spectroscopy and artificial intelligence (AI) have a burgeoning synergy. Deep learning-based structural predictors have forever changed structural biology, yet these tools currently face limitations in accurately characterizing protein dynamics, allostery, and conformational heterogeneity. We begin by highlighting the unique abilities of biomolecular NMR spectroscopy to complement AI-based structural predictions toward addressing these knowledge...
[NMR paper] High-fidelity control of spin ensemble dynamics via artificial intelligence: from quantum computing to NMR spectroscopy and imaging
High-fidelity control of spin ensemble dynamics via artificial intelligence: from quantum computing to NMR spectroscopy and imaging
High-fidelity control of spin ensemble dynamics is essential for many research areas, spanning from quantum computing and radio-frequency (RF) engineering to NMR spectroscopy and imaging. However, attaining robust and high-fidelity spin operations remains an unmet challenge. Using an evolutionary algorithm and artificial intelligence (AI), we designed new RF pulses with customizable spatial or temporal field inhomogeneity compensation. Compared with the...
Signal Intelligence - MIT Technology Review
http://www.bionmr.com//t3.gstatic.com/images?q=tbn:ANd9GcQuuDsGoZ14X1sGWyonV-KRBcOzvMPqrignY_7ZQN5n-ZAAz7h-oqzPoiIwo0ogp1ph35CoTT1e
MIT Technology Review
<img alt="" height="1" width="1">
Signal Intelligence
MIT Technology Review
... to making it possible to compress terabytes of data quickly, Katabi says, the algorithm is likely to be useful for advanced MRI technology, graphics, astronomy, spectroscopy, and methods of studying protein structure with nuclear magnetic resonance.
Signal Intelligence - MIT Technology Review
Biomolecular NMR spectroscopy in the era of artificial intelligence
Linear Mode
