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Some fluff in this PR piece, but I love the message that molecular chemistry can now also benefit from deep learning advances, thanks to graph neural networks.
by isoprophlex 4y ago
Some fluff in this PR piece, but I love the message that molecular chemistry can now also benefit from deep learning advances, thanks to graph neural networks. Because molecules are by their nature graphs of atoms, it makes sense to learn a latent representation of a molecular entity with a GNN, and use that for various classification or prediction tasks.
Makes me wonder if the computationally expensive geometry optimization calculations using DFT etc. can be partially done by a GNN. So, pre-optimize a structure with a computationally cheaper GNN to speed up convergence.
- cing 4y agoThis has been a previous area of research for Google (https://ai.googleblog.com/2017/04/predicting-properties-of-molecules-with.html https://ai.googleblog.com/2017/04/predicting-properties-of-m...). It remains routine to benchmark GNNs and other molecular machine learning models on predicting quantum mechanical properties including energies (which speed up geometry optimization)