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Revista de investigación clínica
versión On-line ISSN 2564-8896versión impresa ISSN 0034-8376
Resumen
SILVEIRA, Elena Caires. Screening Anti-inflammatory, Anticoagulant, and Respiratory Agents for SARS-CoV-2 3CLpro Inhibition from Chemical Fingerprints Through a Deep Learning Approach. Rev. invest. clín. [online]. 2022, vol.74, n.1, pp.31-39. Epub 28-Feb-2022. ISSN 2564-8896. https://doi.org/10.24875/ric.21000282.
Background:
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the etiologic agent of coronavirus disease 2019 (COVID-19), triggers a pathophysiological process linked not only to viral mechanisms of infectivity, but also to the pattern of host response. Drug repurposing is a promising strategy for rapid identification of treatments for SARS-CoV-2 infection, and several attractive molecular viral targets can be exploited. Among those, 3CL protease is a potential target of great interest.
Objective:
The objective of the study was to screen potential 3CLpro inhibitors compounds based on chemical fingerprints among anti-inflammatory, anticoagulant, and respiratory system agents.
Methods:
The screening was developed based on a drug property prediction framework, in which the evaluated property was the ability to inhibit the activity of the 3CLpro protein, and the predictions were performed using a dense neural network trained and validated on bioassay data.
Results:
On the validation and test set, the model obtained area under the curve values of 98.2 and 76.3, respectively, demonstrating high specificity for both sets (98.5% and 94.7%). Regarding the 1278 compounds screened, the model indicated four anti-inflammatory agents, two anticoagulants, and one respiratory agent as potential 3CLpro inhibitors.
Conclusions:
Those findings point to a possible desirable synergistic effect in the management of patients with COVID-19 and provide potential directions for in vitro and in vivo research, which are indispensable for the validation of their results.
Palabras llave : COVID-19; SARS-CoV-2; Deep learning; Drug repositioning; 3C-like viral proteases.