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Predicting quantum mechanics energies of molecules using neural networks actually works, and can be used to speed up geometry optimization during drug discovery
by comicjk 6y ago
Predicting quantum mechanics energies of molecules using neural networks actually works, and can be used to speed up geometry optimization during drug discovery.
See:
https://arxiv.org/abs/1912.05079 https://arxiv.org/abs/1912.05079
https://chemrxiv.org/articles/Extending_the_Applicability_of_the_ANI_Deep_Learning_Molecular_Potential_to_Sulfur_and_Halogens/11819268/1 https://chemrxiv.org/articles/Extending_the_Applicability_of...
- fock 6y agowell, this is not predicting "quantum mechanics energies", it's just parametrizing the molecular bond interaction potential with a neural network instead of an analytic function (such as e.g. a Lennard-Jones potential). It's nice, but not really quantum-mechanics level (which is maybe HF, DFT or coupled cluster), which takes a lot more cycles (but also allows to optimize geometries without knowing wether a bond exists)
- comicjk 6y agoThese neural network models do not need to know whether a bond exists - in fact, they have no concept of bond topology. They are designed to be a drop-in replacement for DFT in terms of energies and forces. The only inputs are XYZ coordinates and chemical element labels for the nuclei (and, in the near future, net charge of the system).
- fock 6y agowith the non-bonded interactions parametrized with dimers it might work... sometimes (might be good enough for a lot of things though)