Drug Repurposing for the Identification of Compounds with Anti-SARS-CoV-2 Capability via Multiple Targets

Identification of SARS-CoV-2 Main Protease Inhibitors Using Chemical Similarity Analysis Combined with Machine Learning

SARS-CoV-2 Main Protease (Mpro) is an enzyme that cleaves viral polyproteins translated from the viral genome, which is critical for viral replication. Mpro is a target for anti-SARS-CoV-2 drug development. Herein, we performed a large-scale virtual screening by comparing multiple structural descriptors of reference molecules with reported anti-coronavirus activity against a library with >17 million compounds. Further filtering, performed by applying two machine learning algorithms, identified eighteen computational hits as anti-SARS-CoV-2 compounds with high structural diversity and drug-like properties. The activities of twelve compounds on Mpro's enzymatic activity were evaluated by fluorescence resonance energy transfer (FRET) assays. Compound 13 (ZINC13878776) significantly inhibited SARS-CoV-2 Mpro activity and was employed as a reference for an experimentally hit expansion. The structural analogues 13a (ZINC4248385), 13b (ZNC13523222), and 13c (ZINC4248365) were tested as Mpro inhibitors, reducing the enzymatic activity of recombinant Mpro with potency as follows: 13c > 13 > 13b > 13a. Then, their anti-SARS-CoV-2 activities were evaluated in plaque reduction assays using Vero CCL81 cells. Subtoxic concentrations of compounds 13a, 13c, and 13b displayed in vitro antiviral activity with IC50 in the mid micromolar range. Compounds 13a-c could become lead compounds for the development of new Mpro inhibitors with improved activity against anti-SARS-CoV-2.

Targeting SARS-CoV-2 nonstructural protein 3: Function, structure, inhibition, and perspective in drug discovery

As a highly contagious human pathogen, severe acute respiratory syndrome-associated coronavirus-2 (SARS-CoV-2) has infected billions of people worldwide with more than 6 million deaths. With several effective vaccines and antiviral drugs now available, the SARS-CoV-2 pandemic been brought under control. However, a new pathogenic coronavirus could emerge in the future, given the zoonotic nature of this virus. Natural evolution and drug-induced mutations of SARS-CoV-2 also require continued efforts for new anti-coronavirus drugs. Nonstructural protein (nsp) 3 of CoVs is a large, multifunctional protein, containing a papain-like protease (PLpro) and a macrodomain (Mac1), which are essential for viral replication. Here, we provide a comprehensive review of the function, structure, and inhibition of SARS-CoV/-CoV-2 PLpro and Mac1. We also discuss advances in, and challenges to, the discovery of drugs against these targets.

Structure and function of SARS-CoV and SARS-CoV-2 main proteases and their inhibition: A comprehensive review

Severe acute respiratory syndrome-associated coronavirus (SARS-CoV) identified in 2003 infected ∼8000 people in 26 countries with 800 deaths, which was soon contained and eradicated by syndromic surveillance and enhanced quarantine. A closely related coronavirus SARS-CoV-2, the causative agent of COVID-19 identified in 2019, has been dramatically more contagious and catastrophic. It has infected and caused various flu-like symptoms of billions of people in >200 countries, including >6 million people died of or with the virus. Despite the availability of several vaccines and antiviral drugs against SARS-CoV-2, finding new therapeutics is needed because of viral evolution and a possible emerging coronavirus in the future. The main protease (Mpro) of these coronaviruses plays important roles in their life cycle and is essential for the viral replication. This article represents a comprehensive review of the function, structure and inhibition of SARS-CoV and -CoV-2 Mpro, including structure-activity relationships, protein-inhibitor interactions and clinical trial status.

Hybrid Approach to Identifying Druglikeness Leading Compounds against COVID-19 3CL Protease

SARS-CoV-2 is a positive single-strand RNA-based macromolecule that has caused the death of more than 6.3 million people since June 2022. Moreover, by disturbing global supply chains through lockdowns, the virus has indirectly caused devastating damage to the global economy. It is vital to design and develop drugs for this virus and its various variants. In this paper, we developed an in silico study-based hybrid framework to repurpose existing therapeutic agents in finding drug-like bioactive molecules that would cure COVID-19. In the first step, a total of 133 drug-likeness bioactive molecules are retrieved from the ChEMBL database against SARS coronavirus 3CL Protease. Based on the standard IC50, the dataset is divided into three classes: active, inactive, and intermediate. Our comparative analysis demonstrated that the proposed Extra Tree Regressor (ETR)-based QSAR model has improved prediction results related to the bioactivity of chemical compounds as compared to Gradient Boosting-, XGBoost-, Support Vector-, Decision Tree-, and Random Forest-based regressor models. ADMET analysis is carried out to identify thirteen bioactive molecules with the ChEMBL IDs 187460, 190743, 222234, 222628, 222735, 222769, 222840, 222893, 225515, 358279, 363535, 365134, and 426898. These molecules are highly suitable drug candidates for SARS-CoV-2 3CL Protease. In the next step, the efficacy of the bioactive molecules is computed in terms of binding affinity using molecular docking, and then six bioactive molecules are shortlisted, with the ChEMBL IDs 187460, 222769, 225515, 358279, 363535, and 365134. These molecules can be suitable drug candidates for SARS-CoV-2. It is anticipated that the pharmacologist and/or drug manufacturer would further investigate these six molecules to find suitable drug candidates for SARS-CoV-2. They can adopt these promising compounds for their downstream drug development stages.