PF-07304814 for COVID-19

The main protease (Mpro) of SARS-CoV-2 is an essential enzyme that plays a critical part in the virus’s life cycle, making it a significant target for developing antiviral drugs. The inhibition of SARS-CoV-2 Mpro has emerged as a promising approach for developing therapeutic agents to treat COVID-19. This review explores the structure of the Mpro protein and analyzes the progress made in understanding protein–ligand interactions of Mpro inhibitors. It focuses on binding kinetics, origin, and the chemical structure of these inhibitors. The review provides an in-depth analysis of recent clinical trials involving covalent and non-covalent inhibitors and emerging dual inhibitors targeting SARS-CoV-2 Mpro. By integrating findings from the literature and ongoing clinical trials, this review captures the current state of research into Mpro inhibitors, offering a comprehensive understanding of challenges and directions in their future development as anti-coronavirus agents. This information provides new insights and inspiration for medicinal chemists, paving the way for developing more effective Mpro inhibitors as novel COVID-19 therapies.

The main protease (Mpro) plays a pivotal role in the replication of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and is considered a highly conserved viral target. Disruption of the catalytic activity of Mpro produces a detrimental effect on the course of the infection, making this target one of the most attractive for the treatment of COVID-19. The current success of the SARS-CoV-2 Mpro inhibitor Nirmatrelvir, the first oral drug for the treatment of severe forms of COVID-19, has further focused the attention of researchers on this important viral target, making the search for new Mpro inhibitors a thriving and exciting field for the development of antiviral drugs active against SARS-CoV-2 and related coronaviruses.

In this study, we present the highly efficient and rapid synthesis of substituted dihydropyrimidinone derivatives through an ultrasound-accelerated approach. We utilize copper ferrite (CuFe2O4) magnetic nanoparticles as heterogeneous catalysts, employing the well-known Biginelli reaction, under solvent-free conditions. The impact of the solvent, catalyst amount, and catalyst type on the reaction performance is thoroughly investigated. Our method offers several notable advantages, including facile catalyst separation, catalyst reusability for up to three cycles with the minimal loss of activity, a straightforward procedure, mild reaction conditions, and impressive yields, ranging from 79% to 95%, within short reaction times of 20 to 40 min. Furthermore, in the context of fighting COVID-19, we explore the potential of substituted dihydropyrimidinone derivatives as inhibitors of three crucial SARS-CoV-2 proteins. These proteins, glycoproteins, and proteases play pivotal roles in the entry, replication, and spread of the virus. Peptides and antiviral drugs targeting these proteins hold great promise in the development of effective treatments. Through theoretical molecular docking studies, we compare the binding properties of the synthesized dihydropyrimidinone derivatives with the widely used hydroxychloroquine molecule as a reference. Our findings reveal that some of the tested molecules exhibit superior binding characteristics compared to hydroxychloroquine, while others demonstrate comparable results. These results highlight the potential of our synthesized derivatives as effective inhibitors in the fight against SARS-CoV-2.

Abstract Due to the lack of a method to efficiently represent the multimodal information of a protein, including its structure and sequence information, predicting compound-protein binding affinity (CPA) still suffers from low accuracy when applying machine-learning methods. To overcome this limitation, in a novel end-to-end architecture (named FeatNN), we develop a coevolutionary strategy to jointly represent the structure and sequence features of proteins and ultimately optimize the mathematical models for predicting CPA. Furthermore, from the perspective of data-driven approach, we proposed a rational method that can utilize both high- and low-quality databases to optimize the accuracy and generalization ability of FeatNN in CPA prediction tasks. Notably, we visually interpret the feature interaction process between sequence and structure in the rationally designed architecture. As a result, FeatNN considerably outperforms the state-of-the-art (SOTA) baseline in virtual drug evaluation tasks, indicating the feasibility of this approach for practical use. FeatNN provides an outstanding method for higher CPA prediction accuracy and better generalization ability by efficiently representing multimodal information of proteins via a coevolutionary strategy.