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Fig. 2 | BMC Chemistry

Fig. 2

From: De novo design and bioactivity prediction of SARS-CoV-2 main protease inhibitors using recurrent neural network-based transfer learning

Fig. 2

Overview of the ULMFit approach. Initially, a general chemical model is trained to learn the “chemical language” contained in a collection of input molecules. The learned features can then be transferred to a target-task and adapted to the idiosyncrasies of the data. These “chemical models” can be used to generate molecules on demand. The last step consists of using the fine-tuned features to train a classifier that predicts bioactivity

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BMC Chemistry

ISSN: 2661-801X

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