Two years later, there is a follow up question to the one asked here: How do we know if the folding@home project results are right? Since we are quite sure F@H is working right and following this article's statement:

Similar techniques could be applied to protein folding, reducing energy consumption, or searching for revolutionary new drugs and materials.

...I would like to ask if AI stuff, like deep learning, neural networks and the rest of today's buzz words could be applied to molecular dynamics, especially in the protein folding field?


Yes, and no :-)

In the meantime many protein structures can be predicted quite accurately - even those for which no reference fold had been known before.

In this case the important buzz word is "big data": co-mutations (of charged amino acids) that can be found when sequencing many independent genomes. (... which indirectly bypasses the emphasis on dynamics for protein folding)

Editorial: 2017, Science: http://www.sciencemag.org/news/2017/01/hundreds-elusive-protein-structures-pinned-down-genome-data

Perspective: Soding et al. , 2017, Science ( http://science.sciencemag.org/content/355/6322/248 )

Research article: Ovchinnikov et al. 2017, Science ( https://www.bakerlab.org/wp-content/uploads/2017/01/ovchinnikov_science_2017.pdf )


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