Powerful, transferable representations for molecules through intelligent task selection in deep multitask networks

17 Sep 2018Clyde FareLukas TurcaniEdward O. Pyzer-Knapp

Chemical representations derived from deep learning are emerging as a powerful tool in areas such as drug discovery and materials innovation. Currently, this methodology has three major limitations - the cost of representation generation, risk of inherited bias, and the requirement for large amounts of data... (read more)

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