Microsoft’s New AI Method to Predict How Molecules Move and Function

Well, apparently Microsoft’s teams applying AI to science (together with a renowned professor, see notes at the very end) might have a first “Yes” as an answer to my question. They have just presented their novel “Distributional Graphormer” which can predict not just single molecular structures (of proteins, or other molecules, or materials) but also actually the multiple alternative structures (or “conformations”) that a molecule or piece of material can adopt in 3D. That is, the alternative possible arrangements that their atoms can adopt in space. Moreover, the new AI model also “understands” that different structures will have different energies and thus be populated to different extents; thus, the new model can be trained to predict the underlying thermodynamics that govern how the different conformations exchange as the molecule moves. As implemented in its first version, the Distributional Graphormer can parse any kind of molecule. In fact, the preprint presenting the model describes example applications to the tasks of predicting how proteins move (what I called “structural diversity” in my question above), how proteins bind small molecules (which covers from enzyme substrates to medicaments, metabolites, etc.), also how molecules adsorb on the surface of catalysts, and how carbon structures can be tuned in their electron-conducting properties. <a href="https://towardsdatascience.com/microsofts-new-ai-method-to-predict-how-molecules-move-and-function-93d47e246b5d">Click Here</a>