Screening Anti-inflammatory, Anticoagulant, and Respiratory Agents for SARS-CoV-2 3CL Inhibition from Chemical Fingerprints Through a Deep Learning Approach.

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.