De novo design and bioactivity prediction of SARS-CoV-2 main protease inhibitors using recurrent neural network-based transfer learning
2021
The global pandemic of coronavirus disease (COVID-19) caused by SARS-CoV-2 (severe acute respiratory syndrome
coronavirus 2) created a rush to discover drug candidates. Despite the efforts, so far no vaccine or drug has been
approved for treatment. Artificial intelligence offers solutions that could accelerate the discovery and optimization of
new antivirals, especially in the current scenario dominated by the scarcity of compounds active against SARS-CoV-2.
The main protease (
Mpro) of SARS-CoV-2 is an attractive target for drug discovery due to the absence in humans
and the essential role in viral replication. In this work, we developed a deep learning platform for de novo design of
putative inhibitors of SARS-CoV-2 main protease (
Mpro). Our methodology consists of 3 main steps: (1) training and
validation of general chemistry-based generative model; (2) fine-tuning of the generative model for the chemical
space of SARS-CoV- Mpro
inhibitors and (3) training of a classifier for bioactivity prediction using transfer learning. The
fine-tuned chemical model generated > 90% valid, diverse and novel (not present on the training set) structures. The
generated molecules showed a good overlap with Mpro
chemical space, displaying similar physicochemical properties
and chemical structures. In addition, novel scaffolds were also generated, showing the potential to explore new
chemical series. The classification model outperformed the baseline area under the precision-recall curve, showing
it can be used for prediction. In addition, the model also outperformed the freely available model Chemprop on an
external test set of fragments screened against SARS-CoV-2 Mpro, showing its potential to identify putative antivirals
to tackle the COVID-19 pandemic. Finally, among the top-20 predicted hits, we identified nine hits via molecular docking
displaying binding poses and interactions similar to experimentally validated inhibitors.
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