no code implementations • 7 Dec 2021 • François Charton, Amaury Hayat, Sean T. McQuade, Nathaniel J. Merrill, Benedetto Piccoli
We show that deep learning models, and especially architectures like the Transformer, originally intended for natural language, can be trained on randomly generated datasets to predict to very high accuracy both the qualitative and quantitative features of metabolic networks.
no code implementations • 26 Mar 2020 • Sean T. McQuade, Nathaniel J. Merrill, Benedetto Piccoli
This paper serves as a framework for designing advanced models for drug action on metabolism.