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

ABSTRACT

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.