Machine learning in biological physics: From biomolecular prediction to design
Jonathan Martin,
Marcos Lequerica Mateos,
José N. Onuchic
et al.
Abstract:Machine learning has been proposed as an alternative to theoretical modeling when dealing with complex problems in biological physics. However, in this perspective, we argue that a more successful approach is a proper combination of these two methodologies. We discuss how ideas coming from physical modeling neuronal processing led to early formulations of computational neural networks, e.g., Hopfield networks. We then show how modern learning approaches like Potts models, Boltzmann machines, and the transforme… Show more
“…For our example of AlphaFold, the transformer idea appears to be absolutely essential. A brief guide to how transformers fit in with more general machine learning concepts dating all the way back to the Hopfield associative memory model ( 14 ) is discussed in the paper by Martin et al ( 15 ).…”
Section: And the Protein Folding Problemmentioning
confidence: 99%
“…But this is not the only important source of data. As described in the article by Martin et al ( 15 ), much of the progress in protein folding arose from the recognition that comparing sequences of the same protein in different organisms could enable one to obtain important information regarding the contact map. The contact map is a matrix representation of the chance that residues located some distance apart along the backbone were likely in the folded structure to be nearby in three-dimensional space.…”
Section: And the Protein Folding Problemmentioning
“…For our example of AlphaFold, the transformer idea appears to be absolutely essential. A brief guide to how transformers fit in with more general machine learning concepts dating all the way back to the Hopfield associative memory model ( 14 ) is discussed in the paper by Martin et al ( 15 ).…”
Section: And the Protein Folding Problemmentioning
confidence: 99%
“…But this is not the only important source of data. As described in the article by Martin et al ( 15 ), much of the progress in protein folding arose from the recognition that comparing sequences of the same protein in different organisms could enable one to obtain important information regarding the contact map. The contact map is a matrix representation of the chance that residues located some distance apart along the backbone were likely in the folded structure to be nearby in three-dimensional space.…”
Section: And the Protein Folding Problemmentioning
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