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A deep language model to predict metabolic network equilibria
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.
Metabolic graphs, LIFE method and the modeling of drug action on Mycobacterium tuberculosis
This paper serves as a framework for designing advanced models for drug action on metabolism.
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