Thursday, March 8, 2018 - 10:30am - 11:30am
Garrett Goh (Pacific Northwest National Laboratory)
With access to large datasets, deep neural networks (DNN) have achieved human-level accuracy in image and speech recognition tasks. However, in chemistry, data is inherently small and fragmented. In this work, we develop various approaches of using rule-based models and physics-based simulations to train ChemNet, a transferable and generalizable pre-trained network for small-molecule property prediction that learns in a weak-supervised manner from large unlabeled chemical databases.
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