Potent Noncovalent Inhibitors of the Main Protease of SARS-CoV-2 from Molecular Sculpting of the Drug Perampanel Guided by Free Energy Perturbation Calculations

Leak Proof PDBBind: A Reorganized Dataset of Protein-Ligand Complexes for More Generalizable Binding Affinity Prediction

Many physics-based and machine-learned scoring functions (SFs) used to predict protein-ligand binding free energies have been trained on the PDBBind dataset. However, it is controversial as to whether new SFs are actually improving since the general, refined, and core datasets of PDBBind are cross-contaminated with proteins and ligands with high similarity, and hence they may not perform comparably well in binding prediction of new protein-ligand complexes. In this work we have carefully prepared a cleaned PDBBind data set of non-covalent binders that are split into training, validation, and test datasets to control for data leakage, defined as proteins and ligands with high sequence and structural similarity. The resulting leak-proof (LP)-PDBBind data is used to retrain four popular SFs: AutoDock Vina, Random Forest (RF)-Score, InteractionGraphNet (IGN), and DeepDTA, to better test their capabilities when applied to new protein-ligand complexes. In particular we have formulated a new independent data set, BDB2020+, by matching high quality binding free energies from BindingDB with co-crystalized ligand-protein complexes from the PDB that have been deposited since 2020. Based on all the benchmark results, the retrained models using LP-PDBBind consistently perform better, with IGN especially being recommended for scoring and ranking applications for new protein-ligand systems.

Proof-of-concept studies with a computationally designed Mpro inhibitor as a synergistic combination regimen alternative to Paxlovid

As the SARS-CoV-2 virus continues to spread and mutate, it remains important to focus not only on preventing spread through vaccination but also on treating infection with direct-acting antivirals (DAA). The approval of Paxlovid, a SARS-CoV-2 main protease (Mpro) DAA, has been significant for treatment of patients. A limitation of this DAA, however, is that the antiviral component, nirmatrelvir, is rapidly metabolized and requires inclusion of a CYP450 3A4 metabolic inhibitor, ritonavir, to boost levels of the active drug. Serious drug-drug interactions can occur with Paxlovid for patients who are also taking other medications metabolized by CYP4503A4, particularly transplant or otherwise immunocompromised patients who are most at risk for SARS-CoV-2 infection and the development of severe symptoms. Developing an alternative antiviral with improved pharmacological properties is critical for treatment of these patients. By using a computational and structure-guided approach, we were able to optimize a 100 to 250 μM screening hit to a potent nanomolar inhibitor and lead compound, Mpro61. In this study, we further evaluate Mpro61 as a lead compound, starting with examination of its mode of binding to SARS-CoV-2 Mpro. In vitro pharmacological profiling established a lack of off-target effects, particularly CYP450 3A4 inhibition, as well as potential for synergy with the currently approved alternate antiviral, molnupiravir. Development and subsequent testing of a capsule formulation for oral dosing of Mpro61 in B6-K18-hACE2 mice demonstrated favorable pharmacological properties, efficacy, and synergy with molnupiravir, and complete recovery from subsequent challenge by SARS-CoV-2, establishing Mpro61 as a promising potential preclinical candidate.

Non-peptidic inhibitors targeting SARS-CoV-2 main protease: A review

The COVID-19 pandemic continues to pose a threat to global health, and sounds the alarm for research & development of effective anti-coronavirus drugs, which are crucial for the patients and urgently needed for the current epidemic and future crisis. The main protease (Mpro) stands as an essential enzyme in the maturation process of SARS-CoV-2, playing an irreplaceable role in regulating viral RNA replication and transcription. It has emerged as an ideal target for developing antiviral agents against SARS-CoV-2 due to its high conservation and the absence of homologous proteases in the human body. Among the SARS-CoV-2 Mpro inhibitors, non-peptidic compounds hold promising prospects owing to their excellent antiviral activity and improved metabolic stability. In this review, we offer an overview of research progress concerning non-peptidic SARS-CoV-2 Mpro inhibitors since 2020. The efforts delved into molecular structures, structure-activity relationships (SARs), biological activity, and binding modes of these inhibitors with Mpro. This review aims to provide valuable clues and insights for the development of anti-SARS-CoV-2 agents as well as broad-spectrum coronavirus Mpro inhibitors.

Design, synthesis and biological evaluation of novel 3C-like protease inhibitors as lead compounds against SARS-CoV-2

Background: The epidemic caused by SARS-CoV-2 swept the world in 2019. The 3C-like protease (3CLpro) of SARS-CoV-2 plays a key role in viral replication, and its inhibition could inhibit viral replication. Materials & methods: The virtual screen based on receptor-ligand pharmacophore models and molecular docking were conducted to obtain the novel scaffolds of the 3CLpro. The molecular dynamics simulation was also carried out. All compounds were synthesized and evaluated in biochemical assays. Results: The compound C2 could inhibit 3CLpro with a 72% inhibitory rate at 10 μM. The covalent docking showed that C2 could form a covalent bond with the Cys145 in 3CLpro. Conclusion: C2 could be a potent lead compound of 3CLpro inhibitors against SARS-CoV-2.

Biophysics-Guided Lead Discovery of HBV Capsid Assembly Modifiers

Hepatitis B virus (HBV) is the leading cause of chronic liver pathologies worldwide. HBV nucleocapsid, a key structural component, is formed through the self-assembly of the capsid protein units. Therefore, interfering with the self-assembly process is a promising approach for the development of novel antiviral agents. Applied to HBV, this approach has led to several classes of capsid assembly modulators (CAMs). Here, we report structurally novel CAMs with moderate activity and low toxicity, discovered through a biophysics-guided approach combining docking, molecular dynamics simulations, and a series of assays with a particular emphasis on biophysical experiments. Several of the identified compounds induce the formation of aberrant capsids and inhibit HBV DNA replication in vitro, suggesting that they possess modest capsid assembly modulation effects. The synergistic computational and experimental approaches provided key insights that facilitated the identification of compounds with promising activities. The discovery of preclinical CAMs presents opportunities for subsequent optimization efforts, thereby opening new avenues for HBV inhibition.

Machine learning combines atomistic simulations to predict SARS-CoV-2 Mpro inhibitors from natural compounds

To date, the COVID-19 pandemic has still been infectious around the world, continuously causing social and economic damage on a global scale. One of the most important therapeutic targets for the treatment of COVID-19 is the main protease (Mpro) of SARS-CoV-2. In this study, we combined machine-learning (ML) model with atomistic simulations to computationally search for highly promising SARS-CoV-2 Mpro inhibitors from the representative natural compounds of the National Cancer Institute (NCI) Database. First, the trained ML model was used to scan the library quickly and reliably for possible Mpro inhibitors. The ML output was then confirmed using atomistic simulations integrating molecular docking and molecular dynamic simulations with the linear interaction energy scheme. The results turned out to show that there was evidently good agreement between ML and atomistic simulations. Ten substances were proposed to be able to inhibit SARS-CoV-2 Mpro. Seven of them have high-nanomolar affinity and are very potential inhibitors. The strategy has been proven to be reliable and appropriate for fast prediction of SARS-CoV-2 Mpro inhibitors, benefiting for new emerging SARS-CoV-2 variants in the future accordingly.

A dual diffusion model enables 3D molecule generation and lead optimization based on target pockets

Structure-based generative chemistry is essential in computer-aided drug discovery by exploring a vast chemical space to design ligands with high binding affinity for targets. However, traditional in silico methods are limited by computational inefficiency, while machine learning approaches face bottlenecks due to auto-regressive sampling. To address these concerns, we have developed a conditional deep generative model, PMDM, for 3D molecule generation fitting specified targets. PMDM consists of a conditional equivariant diffusion model with both local and global molecular dynamics, enabling PMDM to consider the conditioned protein information to generate molecules efficiently. The comprehensive experiments indicate that PMDM outperforms baseline models across multiple evaluation metrics. To evaluate the applications of PMDM under real drug design scenarios, we conduct lead compound optimization for SARS-CoV-2 main protease (Mpro) and Cyclin-dependent Kinase 2 (CDK2), respectively. The selected lead optimization molecules are synthesized and evaluated for their in-vitro activities against CDK2, displaying improved CDK2 activity.

Genetic Algorithm-Based Receptor Ligand: A Genetic Algorithm-Guided Generative Model to Boost the Novelty and Drug-Likeness of Molecules in a Sampling Chemical Space

Deep learning-based de novo molecular design has recently gained significant attention. While numerous DL-based generative models have been successfully developed for designing novel compounds, the majority of the generated molecules lack sufficiently novel scaffolds or high drug-like profiles. The aforementioned issues may not be fully captured by commonly used metrics for the assessment of molecular generative models, such as novelty, diversity, and quantitative estimation of the drug-likeness score. To address these limitations, we proposed a genetic algorithm-guided generative model called GARel (genetic algorithm-based receptor-ligand interaction generator), a novel framework for training a DL-based generative model to produce drug-like molecules with novel scaffolds. To efficiently train the GARel model, we utilized dense net to update the parameters based on molecules with novel scaffolds and drug-like features. To demonstrate the capability of the GARel model, we used it to design inhibitors for three targets: AA2AR, EGFR, and SARS-Cov2. The results indicate that GARel-generated molecules feature more diverse and novel scaffolds and possess more desirable physicochemical properties and favorable docking scores. Compared with other generative models, GARel makes significant progress in balancing novelty and drug-likeness, providing a promising direction for the further development of DL-based de novo design methodology with potential impacts on drug discovery.

What to Make of Zero: Resolving the Statistical Noise from Conformational Reorganization in Alchemical Binding Free Energy Estimates with Metadynamics Sampling

We introduce the self-relative binding free energy (self-RBFE) approach to evaluate the intrinsic statistical variance of dual-topology alchemical binding free energy estimators. The self-RBFE is the relative binding free energy between a ligand and a copy of the same ligand, and its true value is zero. Nevertheless, because the two copies of the ligand move independently, the self-RBFE value produced by a finite-length simulation fluctuates and can be used to measure the variance of the model. The results of this validation provide evidence that a significant fraction of the errors observed in benchmark studies reflect the statistical fluctuations of unconverged estimates rather than the models' accuracy. Furthermore, we find that ligand reorganization is a significant contributing factor to the statistical variance of binding free energy estimates and that metadynamics-accelerated conformational sampling of the torsional degrees of freedom of the ligand can drastically reduce the time to convergence.

Structure-Based Identification of Organoruthenium Compounds as Nanomolar Antagonists of Cannabinoid Receptors

Metal complexes exhibit a diverse range of coordination geometries, representing novel privileged scaffolds with convenient click types of preparation inaccessible for typical carbon-centered organic compounds. Herein, we explored the opportunity to identify biologically active organometallic complexes by reverse docking of a rigid, minimum-size octahedral organoruthenium scaffold against thousands of protein-binding pockets. Interestingly, cannabinoid receptor type 1 (CB1) was identified based on the docking scores and the degree of overlap between the docked organoruthenium scaffold and the hydrophobic scaffold of the cocrystallized ligand. Further structure-based optimization led to the discovery of organoruthenium complexes with nanomolar binding affinities and high selectivity toward CB2. Our work indicates that octahedral organoruthenium scaffolds may be advantageous for targeting the large and hydrophobic binding pockets and that the reverse docking approach may facilitate the discovery of novel privileged scaffolds, such as organometallic complexes, for exploring chemical space in lead discovery.

Cathepsin-Targeting SARS-CoV-2 Inhibitors: Design, Synthesis, and Biological Activity

Cathepsins (Cats) are proteases that mediate the successful entry of SARS-CoV-2 into host cells. We designed and synthesized a tailored series of 21 peptidomimetics and evaluated their inhibitory activity against human cathepsins L, B, and S. Structural diversity was realized by combinations of different C-terminal warhead functions and N-terminal capping groups, while a central Leu-Phe fragment was maintained. Several compounds were identified as promising cathepsin L and S inhibitors with Ki values in the low nanomolar to subnanomolar range, for example, the peptide aldehydes 9a and 9b (9a, 2.67 nM, CatL; 0.455 nM, CatS; 9b, 1.76 nM, CatL; 0.512 nM, CatS). The compounds' inhibitory activity against the main protease of SARS-CoV-2 (Mpro) was additionally investigated. Based on the results at CatL, CatS, and Mpro, selected inhibitors were subjected to investigations of their antiviral activity in cell-based assays. In particular, the peptide nitrile 11e exhibited promising antiviral activity with an EC50 value of 38.4 nM in Calu-3 cells without showing cytotoxicity. High metabolic stability and favorable pharmacokinetic properties make 11e suitable for further preclinical development.

Docking and other computing tools in drug design against SARS-CoV-2

The use of computer simulation methods has become an indispensable component in identifying drugs against the SARS-CoV-2 coronavirus. There is a huge body of literature on application of molecular modelling to predict inhibitors against target proteins of SARS-CoV-2. To keep our review clear and readable, we limited ourselves primarily to works that use computational methods to find inhibitors and test the predicted compounds experimentally either in target protein assays or in cell culture with live SARS-CoV-2. Some works containing results of experimental discovery of corresponding inhibitors without using computer modelling are included as examples of a success. Also, some computational works without experimental confirmations are also included if they attract our attention either by simulation methods or by databases used. This review collects studies that use various molecular modelling methods: docking, molecular dynamics, quantum mechanics, machine learning, and others. Most of these studies are based on docking, and other methods are used mainly for post-processing to select the best compounds among those found through docking. Simulation methods are presented concisely, information is also provided on databases of organic compounds that can be useful for virtual screening, and the review itself is structured in accordance with coronavirus target proteins.

On the origins of SARS-CoV-2 main protease inhibitors

In order to address the world-wide health challenge caused by the COVID-19 pandemic, the 3CL protease/SARS-CoV-2 main protease (SARS-CoV-2-Mpro) coded by its nsp5 gene became one of the biochemical targets for the design of antiviral drugs. In less than 3 years of research, 4 inhibitors of SARS-CoV-2-Mpro have actually been authorized for COVID-19 treatment (nirmatrelvir, ensitrelvir, leritrelvir and simnotrelvir) and more such as EDP-235, FB-2001 and STI-1558/Olgotrelvir or five undisclosed compounds (CDI-988, ASC11, ALG-097558, QLS1128 and H-10517) are undergoing clinical trials. This review is an attempt to picture this quite unprecedented medicinal chemistry feat and provide insights on how these cysteine protease inhibitors were discovered. Since many series of covalent SARS-CoV-2-Mpro inhibitors owe some of their origins to previous work on other proteases, we first provided a description of various inhibitors of cysteine-bearing human caspase-1 or cathepsin K, as well as inhibitors of serine proteases such as human dipeptidyl peptidase-4 or the hepatitis C protein complex NS3/4A. This is then followed by a description of the results of the approaches adopted (repurposing, structure-based and high throughput screening) to discover coronavirus main protease inhibitors.

Development of de-novo coronavirus 3-chymotrypsin-like protease (3CLpro) inhibitors since COVID-19 outbreak: A strategy to tackle challenges of persistent virus infection

Although no longer a public health emergency of international concern, COVID-19 remains a persistent and critical health concern. The development of effective antiviral drugs could serve as the ultimate piece of the puzzle to curbing this global crisis. 3-chymotrypsin-like protease (3CLpro), with its substrate specificity mirroring that of the main picornavirus 3C protease and conserved across various coronaviruses, emerges as an ideal candidate for broad-spectrum antiviral drug development. Moreover, it holds the potential as a reliable contingency option to combat emerging SARS-CoV-2 variants. In this light, the approved drugs, promising candidates, and de-novo small molecule therapeutics targeting 3CLpro since the COVID-19 outbreak in 2020 are discussed. Emphasizing the significance of diverse structural characteristics in inhibitors, be they peptidomimetic or nonpeptidic, with a shared mission to minimize the risk of cross-resistance. Moreover, the authors propose an innovative optimization strategy for 3CLpro reversible covalent PROTACs, optimizing pharmacodynamics and pharmacokinetics to better prepare for potential future viral outbreaks.

Performance and Analysis of the Alchemical Transfer Method for Binding-Free-Energy Predictions of Diverse Ligands

The Alchemical Transfer Method (ATM) is herein validated against the relative binding-free energies (RBFEs) of a diverse set of protein-ligand complexes. We employed a streamlined setup workflow, a bespoke force field, and AToM-OpenMM software to compute the RBFEs of the benchmark set prepared by Schindler and collaborators at Merck KGaA. This benchmark set includes examples of standard small R-group ligand modifications as well as more challenging scenarios, such as large R-group changes, scaffold hopping, formal charge changes, and charge-shifting transformations. The novel coordinate perturbation scheme and a dual-topology approach of ATM address some of the challenges of single-topology alchemical RBFE methods. Specifically, ATM eliminates the need for splitting electrostatic and Lennard-Jones interactions, atom mapping, defining ligand regions, and postcorrections for charge-changing perturbations. Thus, ATM is simpler and more broadly applicable than conventional alchemical methods, especially for scaffold-hopping and charge-changing transformations. Here, we performed well over 500 RBFE calculations for eight protein targets and found that ATM achieves accuracy comparable to that of existing state-of-the-art methods, albeit with larger statistical fluctuations. We discuss insights into the specific strengths and weaknesses of the ATM method that will inform future deployments. This study confirms that ATM can be applied as a production tool for RBFE predictions across a wide range of perturbation types within a unified, open-source framework.

Medicinal chemistry strategies towards the development of non-covalent SARS-CoV-2 Mpro inhibitors

The main protease (Mpro) of SARS-CoV-2 is an attractive target in anti-COVID-19 therapy for its high conservation and major role in the virus life cycle. The covalent Mpro inhibitor nirmatrelvir (in combination with ritonavir, a pharmacokinetic enhancer) and the non-covalent inhibitor ensitrelvir have shown efficacy in clinical trials and have been approved for therapeutic use. Effective antiviral drugs are needed to fight the pandemic, while non-covalent Mpro inhibitors could be promising alternatives due to their high selectivity and favorable druggability. Numerous non-covalent Mpro inhibitors with desirable properties have been developed based on available crystal structures of Mpro. In this article, we describe medicinal chemistry strategies applied for the discovery and optimization of non-covalent Mpro inhibitors, followed by a general overview and critical analysis of the available information. Prospective viewpoints and insights into current strategies for the development of non-covalent Mpro inhibitors are also discussed.

Lessons learnt from the preclinical discovery and development of ensitrelvir as a COVID-19 therapeutic option

INTRODUCTION

The COVID-19 pandemic stimulated the development of several therapeutic tools with several degrees of success. Ensitrelvir, a protease inhibitor that blocks the replication of SARS-CoV-2, can reduce the viral load and the severity of symptoms in infected patients and become available for emergency use in Japan. Clinical trials showed a good tolerability profile although the potential for interactions with substrates, inhibitors, and inducers of CYP3A must be considered. The occurrence of resistance is also a matter of investigation.

AREAS COVERED

In this article, the authors describe the development of ensitrelvir starting from the identification of the molecule to the pre-clinical and clinical trials up to the post-authorization phase.

EXPERT OPINION

Ensitrelvir was developed in a late phase of the pandemic when the availability of patients that can be candidate to enter the clinical trial was limited with consequences for the possibility of assessing certain outcomes and for the robustness of results. Although the evidence about the benefits of ensitrelvir in COVID-19 is not questionable, the problems of interactions with other drugs, emerging resistant variants, the availability of alternative therapeutic options, costs, and accessibility will concur to its probable limited clinical use in the future.

Design, Synthesis, and Biological Evaluation of Trisubstituted Piperazine Derivatives as Noncovalent Severe Acute Respiratory Syndrome Coronavirus 2 Main Protease Inhibitors with Improved Antiviral Activity and Favorable Druggability

The ongoing transmission of SARS-CoV-2 necessitates the development of additional potent antiviral agents capable of combating the current highly infectious variants and future coronaviruses. Here, we present the discovery of potent nonpeptide main protease (Mpro) inhibitors with prominent antiviral activity and improved pharmacokinetic properties. Three series of 1,2,4-trisubstituted piperazine derivatives were designed and synthesized, and the optimal GC-78-HCl demonstrated high enzyme-inhibitory potency (IC50 = 0.19 μM) and exhibited excellent antiviral activity (EC50 = 0.40 μM), reaching the same level as Nirmatrelvir (EC50 = 0.38 μM). Additionally, GC-78-HCl displayed potent antiviral activities against various SARS-CoV-2 variants as well as HCoV-OC43 and HCoV-229E, indicating its potential broad-spectrum anticoronaviral activity. Notably, the pharmacokinetic properties of GC-78-HCl were somewhat enhanced compared to those of the lead compound. Furthermore, the cocrystal and molecular docking elucidated the mechanism of action. In conclusion, we discovered a novel nonpeptidic Mpro inhibitor with promising antiviral activity and a favorable pharmacokinetic profile.

Assessing Metadynamics and Docking for Absolute Binding Free Energy Calculations Using Severe Acute Respiratory Syndrome Coronavirus 2 Main Protease Inhibitors

Absolute binding free energy (ABFE) calculations can be an important part of the drug discovery process by identifying molecules that have the potential to be strong binders for a biomolecular target. Recent work has used free energy perturbation (FEP) theory for these calculations, focusing on a set of 16 inhibitors of the severe acute respiratory syndrome coronavirus 2 main protease (Mpro). Herein, the same data set is evaluated by metadynamics (MetaD), four different docking programs, and molecular mechanics with generalized Born and surface area solvation. MetaD yields a Kendall τ distance of 0.28 and Pearson r2 of 0.49, which reflect somewhat less accuracy than that from the ABFE FEP results. Notably, it is demonstrated that an ensemble docking protocol by which each ligand is docked into the 13 crystal structures in this data set provides improved performance, particularly when docking is carried out with Glide XP (Kendall τ distance = 0.20, Pearson r2 = 0.71), Glide SP (Kendall τ distance = 0.19, Pearson r2 = 0.66), or AutoDock 4 (Kendall τ distance = 0.21, Pearson r2 = 0.55). The best results are obtained with "superconsensus" docking by averaging the 52 results for each compound using the 4 docking protocols and all 13 crystal structures (Kendall τ distance = 0.18, Pearson r2 = 0.73).

Discovery of Nirmatrelvir Resistance Mutations in SARS-CoV-2 3CLpro: A Computational-Experimental Approach

The COVID-19 pandemic has emphasized the urgency for effective antiviral therapies against SARS-CoV-2. Targeting the main protease (3CLpro) of the virus has emerged as a promising approach, and nirmatrelvir (PF-07321332), the active component of Pfizer's oral drug Paxlovid, has demonstrated remarkable clinical efficacy. However, the emergence of resistance mutations poses a challenge to its continued success. In this study, we employed alchemical free energy perturbation (FEP) alanine scanning to identify nirmatrelvir-resistance mutations within SARS-CoV-2 3CLpro. FEP identified several mutations, which were validated through in vitro IC50 experiments and found to result in 8- and 72-fold increases in nirmatrelvir IC50 values. Additionally, we constructed SARS-CoV-2 omicron replicons containing these mutations, and one of the mutants (S144A/E166A) displayed a 20-fold increase in EC50, confirming the role of FEP in identifying drug-resistance mutations. Our findings suggest that FEP can be a valuable tool in proactively monitoring the emergence of resistant strains and guiding the design of future inhibitors with reduced susceptibility to drug resistance. As nirmatrelvir is currently widely used for treating COVID-19, this research has important implications for surveillance efforts and antiviral development.

A High-Throughput, High-Containment Human Primary Epithelial Airway Organ-on-Chip Platform for SARS-CoV-2 Therapeutic Screening

COVID-19 emerged as a worldwide pandemic in early 2020, and while the rapid development of safe and efficacious vaccines stands as an extraordinary achievement, the identification of effective therapeutics has been less successful. This process has been limited in part by a lack of human-relevant preclinical models compatible with therapeutic screening on the native virus, which requires a high-containment environment. Here, we report SARS-CoV-2 infection and robust viral replication in PREDICT96-ALI, a high-throughput, human primary cell-based organ-on-chip platform. We evaluate unique infection kinetic profiles across lung tissue from three human donors by immunofluorescence, RT-qPCR, and plaque assays over a 6-day infection period. Enabled by the 96 devices/plate throughput of PREDICT96-ALI, we also investigate the efficacy of Remdesivir and MPro61 in a proof-of-concept antiviral study. Both compounds exhibit an antiviral effect against SARS-CoV-2 in the platform. This demonstration of SARS-CoV-2 infection and antiviral dosing in a high-throughput organ-on-chip platform presents a critical capability for disease modeling and therapeutic screening applications in a human physiology-relevant in vitro system.

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.

Alchemical Free Energy Calculations on Membrane-Associated Proteins

Membrane proteins have diverse functions within cells and are well-established drug targets. The advances in membrane protein structural biology have revealed drug and lipid binding sites on membrane proteins, while computational methods such as molecular simulations can resolve the thermodynamic basis of these interactions. Particularly, alchemical free energy calculations have shown promise in the calculation of reliable and reproducible binding free energies of protein-ligand and protein-lipid complexes in membrane-associated systems. In this review, we present an overview of representative alchemical free energy studies on G-protein-coupled receptors, ion channels, transporters as well as protein-lipid interactions, with emphasis on best practices and critical aspects of running these simulations. Additionally, we analyze challenges and successes when running alchemical free energy calculations on membrane-associated proteins. Finally, we highlight the value of alchemical free energy calculations calculations in drug discovery and their applicability in the pharmaceutical industry.

Upgrading nirmatrelvir to inhibit SARS-CoV-2 Mpro via DeepFrag and free energy calculations

The first oral drug for the treatment of COVID-19, Paxlovid, has been authorized; however, nirmatrelvir, a major component of the drug, is reported to be associated with some side effects. Moreover, the appearance of many novel variants raises concerns about drug resistance, and designing new potent inhibitors to prevent viral replication is thus urgent. In this context, using a hybrid approach combining machine learning (ML) and free energy simulations, 6 compounds obtained by modifying nirmatrelvir were proposed to bind strongly to SARS-CoV-2 Mpro. The structural modification of nirmatrelvir significantly enhances the electrostatic interaction free energy between the protein and ligand and slightly decreases the vdW term. However, the vdW term is the most important factor in controlling the ligand-binding affinity. In addition, the modified nirmatrelvir might be less toxic to the human body than the original inhibitor.

Design and Optimization of Novel Competitive, Non-peptidic, SARS-CoV-2 Mpro Inhibitors

The SARS-CoV-2 main protease (Mpro) has been proven to be a highly effective target for therapeutic intervention, yet only one drug currently holds FDA approval status for this target. We were inspired by a series of publications emanating from the Jorgensen and Anderson groups describing the design of potent, non-peptidic, competitive SARS-CoV-2 Mpro inhibitors, and we saw an opportunity to make several design modifications to improve the overall pharmacokinetic profile of these compounds without losing potency. To this end, we created a focused virtual library using reaction-based enumeration tools in the Schrödinger suite. These compounds were docked into the Mpro active site and subsequently prioritized for synthesis based upon relative binding affinity values calculated by FEP+. Fourteen compounds were selected, synthesized, and evaluated both biochemically and in cell culture. Several of the synthesized compounds proved to be potent, competitive Mpro inhibitors with improved metabolic stability profiles.

Discovery and Mechanism Study of SARS-CoV-2 3C-like Protease Inhibitors with a New Reactive Group

3CLpro is an attractive target for the treatment of COVID-19. Using the scaffold hopping strategy, we identified a potent inhibitor of 3CLpro (3a) that contains a thiocyanate moiety as a novel warhead that can form a covalent bond with Cys145 of the protein. Tandem mass spectrometry (MS/MS) and X-ray crystallography confirmed the mechanism of covalent formation between 3a and the protein in its catalytic pocket. Moreover, several analogues of compound 3a were designed and synthesized. Among them, compound 3h shows the best inhibition of 3CLpro with an IC50 of 0.322 μM and a kinact/Ki value of 1669.34 M-1 s-1, and it exhibits good target selectivity for 3CLpro against host proteases. Compound 3c inhibits SARS-CoV-2 in Vero E6 cells (EC50 = 2.499 μM) with low cytotoxicity (CC50 > 200 μM). These studies provide ideas and insights to explore and develop new 3CLpro inhibitors in the future.

Bench-to-bedside: Innovation of small molecule anti-SARS-CoV-2 drugs in China

The ongoing COVID-19 pandemic has resulted in millions of deaths globally, highlighting the need to develop potent prophylactic and therapeutic strategies against SARS-CoV-2. Small molecule inhibitors (remdesivir, Paxlovid, and molnupiravir) are essential complements to vaccines and play important roles in clinical treatment of SARS-CoV-2. Many advances have been made in development of anti-SARS-CoV-2 inhibitors in China, but progress in discovery and characterization of pharmacological activity, antiviral mechanisms, and clinical efficacy are limited. We review development of small molecule anti-SARS-CoV-2 drugs (azvudine [approved by the NMPA of China on July 25, 2022], VV116 [approved by the NMPA of China on January 29, 2023], FB2001, WPV01, pentarlandir, and cepharanthine) in China and summarize their pharmacological activity, potential mechanisms of action, clinical trials and use, and important milestones in their discovery. The role of structural biology in drug development is also reviewed. Future studies should focus on development of diverse second-generation inhibitors with excellent oral bioavailability, superior plasma half-life, increased antiviral activity against SARS-CoV-2 and its variants, high target specificity, minimal side effects, reduced drug-drug interactions, and improved lung histopathology.

Computation of Absolute Binding Free Energies for Noncovalent Inhibitors with SARS-CoV-2 Main Protease

Accurate, routine calculation of absolute binding free energies (ABFEs) for protein-ligand complexes remains a key goal of computer-aided drug design since it can enable screening and optimization of drug candidates. For development and testing of related methods, it is important to have high-quality datasets. To this end, from our own experimental studies, we have selected a set of 16 inhibitors of the SARS-CoV-2 main protease (Mpro) with structural diversity and well-distributed BFEs covering a 5 kcal/mol range. There is also minimal structural uncertainty since X-ray crystal structures have been deposited for 12 of the compounds. For methods testing, we report ABFE results from 2 μs molecular dynamics (MD) simulations using free energy perturbation (FEP) theory. The correlation of experimental and computed results is encouraging, with a Pearson's r2 of 0.58 and a Kendall τ of 0.24. The results indicate that current FEP-based ABFE calculations can be used for identification of active compounds (hits). While their accuracy for lead optimization is not yet sufficient, this activity remains addressable in separate lead series by relative BFE calculations.

A Systematic Survey of Reversibly Covalent Dipeptidyl Inhibitors of the SARS-CoV-2 Main Protease

SARS-CoV-2, the COVID-19 pathogen, relies on its main protease (MPro) for replication and pathogenesis. MPro is a demonstrated target for the development of antivirals for SARS-CoV-2. Past studies have systematically explored tripeptidyl inhibitors such as nirmatrelvir as MPro inhibitors. However, dipeptidyl inhibitors especially those with a spiro residue at their P2 position have not been systematically investigated. In this work, we synthesized about 30 dipeptidyl MPro inhibitors and characterized them on enzymatic inhibition potency, structures of their complexes with MPro, cellular MPro inhibition potency, antiviral potency, cytotoxicity, and in vitro metabolic stability. Our results indicated that MPro has a flexible S2 pocket to accommodate inhibitors with a large P2 residue and revealed that dipeptidyl inhibitors with a large P2 spiro residue such as (S)-2-azaspiro [4,4]nonane-3-carboxylate and (S)-2-azaspiro[4,5]decane-3-carboxylate have favorable characteristics. One compound, MPI60, containing a P2 (S)-2-azaspiro[4,4]nonane-3-carboxylate displayed high antiviral potency, low cellular cytotoxicity, and high in vitro metabolic stability.

DNA-encoded chemical libraries yield non-covalent and non-peptidic SARS-CoV-2 main protease inhibitors

The development of SARS-CoV-2 main protease (Mpro) inhibitors for the treatment of COVID-19 has mostly benefitted from X-ray structures and preexisting knowledge of inhibitors; however, an efficient method to generate Mpro inhibitors, which circumvents such information would be advantageous. As an alternative approach, we show here that DNA-encoded chemistry technology (DEC-Tec) can be used to discover inhibitors of Mpro. An affinity selection of a 4-billion-membered DNA-encoded chemical library (DECL) using Mpro as bait produces novel non-covalent and non-peptide-based small molecule inhibitors of Mpro with low nanomolar Ki values. Furthermore, these compounds demonstrate efficacy against mutant forms of Mpro that have shown resistance to the standard-of-care drug nirmatrelvir. Overall, this work demonstrates that DEC-Tec can efficiently generate novel and potent inhibitors without preliminary chemical or structural information.

Large library docking for novel SARS-CoV-2 main protease non-covalent and covalent inhibitors

Antiviral therapeutics to treat SARS-CoV-2 are needed to diminish the morbidity of the ongoing COVID-19 pandemic. A well-precedented drug target is the main viral protease (MPro ), which is targeted by an approved drug and by several investigational drugs. Emerging viral resistance has made new inhibitor chemotypes more pressing. Adopting a structure-based approach, we docked 1.2 billion non-covalent lead-like molecules and a new library of 6.5 million electrophiles against the enzyme structure. From these, 29 non-covalent and 11 covalent inhibitors were identified in 37 series, the most potent having an IC50 of 29 and 20 μM, respectively. Several series were optimized, resulting in low micromolar inhibitors. Subsequent crystallography confirmed the docking predicted binding modes and may template further optimization. While the new chemotypes may aid further optimization of MPro inhibitors for SARS-CoV-2, the modest success rate also reveals weaknesses in our approach for challenging targets like MPro versus other targets where it has been more successful, and versus other structure-based techniques against MPro itself.

In Silico Discovery of Small-Molecule Inhibitors Targeting SARS-CoV-2 Main Protease

The COVID-19 pandemic has caused severe health threat globally, and novel SARS-Cov-2 inhibitors are urgently needed for antiviral treatment. The main protease (M^pro) of the virus is one of the most effective and conserved targets for anti-SARS-CoV-2 drug development. In this study, we utilized a molecular docking-based virtual screening approach against the conserved catalytic site to identify small-molecule inhibitors of SARS-CoV-2 M^pro. Further biological evaluation helped us identify two compounds, AF-399/40713777 and AI-942/42301830, with moderate inhibitory activity. Besides that, the in silico data, including molecular dynamics (MD) simulation, binding free energy calculations, and AMDET profiles, suggested that these two hits could serve as the starting point for the future development of COVID-19 intervention treatments.

Mpro-targeted anti-SARS-CoV-2 inhibitor-based drugs

The COVID-19 pandemic caused by severe acute respiratory syndrome coronavirus 2 is a global health emergency. The main protease is an important drug target in coronaviruses. It plays an important role in the processing of viral RNA-translated polyproteins and is highly conserved in the amino acid sequence and three-dimensional structure, making it a good drug target for which several small molecule inhibitors are available. This paper describes the various anti-severe acute respiratory syndrome coronavirus 2 inhibitor drugs targeting Mpro discovered since the severe acute respiratory syndrome coronavirus 2 outbreak at the end of 2019, with all these compounds inhibiting severe acute respiratory syndrome coronavirus 2 Mpro activity in vitro. This provides a reference for the development of severe acute respiratory syndrome coronavirus 2 Mpro-targeted inhibitors and the design of therapeutic approaches to address newly emerged severe acute respiratory syndrome coronavirus 2 mutant strains with immune evasion capabilities.

Accelerating antiviral drug discovery: lessons from COVID-19

During the coronavirus disease 2019 (COVID-19) pandemic, a wave of rapid and collaborative drug discovery efforts took place in academia and industry, culminating in several therapeutics being discovered, approved and deployed in a 2-year time frame. This article summarizes the collective experience of several pharmaceutical companies and academic collaborations that were active in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) antiviral discovery. We outline our opinions and experiences on key stages in the small-molecule drug discovery process: target selection, medicinal chemistry, antiviral assays, animal efficacy and attempts to pre-empt resistance. We propose strategies that could accelerate future efforts and argue that a key bottleneck is the lack of quality chemical probes around understudied viral targets, which would serve as a starting point for drug discovery. Considering the small size of the viral proteome, comprehensively building an arsenal of probes for proteins in viruses of pandemic concern is a worthwhile and tractable challenge for the community.

Accelerating drug target inhibitor discovery with a deep generative foundation model

Inhibitor discovery for emerging drug-target proteins is challenging, especially when target structure or active molecules are unknown. Here, we experimentally validate the broad utility of a deep generative framework trained at-scale on protein sequences, small molecules, and their mutual interactions-unbiased toward any specific target. We performed a protein sequence-conditioned sampling on the generative foundation model to design small-molecule inhibitors for two dissimilar targets: the spike protein receptor-binding domain (RBD) and the main protease from SARS-CoV-2. Despite using only the target sequence information during the model inference, micromolar-level inhibition was observed in vitro for two candidates out of four synthesized for each target. The most potent spike RBD inhibitor exhibited activity against several variants in live virus neutralization assays. These results establish that a single, broadly deployable generative foundation model for accelerated inhibitor discovery is effective and efficient, even in the absence of target structure or binder information.

Garbage in, garbage out: how reliable training data improved a virtual screening approach against SARS-CoV-2 MPro

Introduction: The identification of chemical compounds that interfere with SARS-CoV-2 replication continues to be a priority in several academic and pharmaceutical laboratories. Computational tools and approaches have the power to integrate, process and analyze multiple data in a short time. However, these initiatives may yield unrealistic results if the applied models are not inferred from reliable data and the resulting predictions are not confirmed by experimental evidence. Methods: We undertook a drug discovery campaign against the essential major protease (MPro) from SARS-CoV-2, which relied on an in silico search strategy -performed in a large and diverse chemolibrary- complemented by experimental validation. The computational method comprises a recently reported ligand-based approach developed upon refinement/learning cycles, and structure-based approximations. Search models were applied to both retrospective (in silico) and prospective (experimentally confirmed) screening. Results: The first generation of ligand-based models were fed by data, which to a great extent, had not been published in peer-reviewed articles. The first screening campaign performed with 188 compounds (46 in silico hits and 100 analogues, and 40 unrelated compounds: flavonols and pyrazoles) yielded three hits against MPro (IC₅₀ ≤ 25 μM): two analogues of in silico hits (one glycoside and one benzo-thiazol) and one flavonol. A second generation of ligand-based models was developed based on this negative information and newly published peer-reviewed data for MPro inhibitors. This led to 43 new hit candidates belonging to different chemical families. From 45 compounds (28 in silico hits and 17 related analogues) tested in the second screening campaign, eight inhibited MPro with IC₅₀ = 0.12-20 μM and five of them also impaired the proliferation of SARS-CoV-2 in Vero cells (EC₅₀ 7-45 μM). Discussion: Our study provides an example of a virtuous loop between computational and experimental approaches applied to target-focused drug discovery against a major and global pathogen, reaffirming the well-known "garbage in, garbage out" machine learning principle.

Structure-based discovery of thiosemicarbazones as SARS-CoV-2 main protease inhibitors

Aim: Discovery of novel SARS-CoV-2 main protease (Mpro) inhibitors using a structure-based drug discovery strategy. Materials & methods: Virtual screening employing covalent and noncovalent docking was performed to discover Mpro inhibitors, which were subsequently evaluated in biochemical and cellular assays. Results: 91 virtual hits were selected for biochemical assays, and four were confirmed as reversible inhibitors of SARS CoV-2 Mpro with IC50 values of 0.4-3 μM. They were also shown to inhibit SARS-CoV-1 Mpro and human cathepsin L. Molecular dynamics simulations indicated the stability of the Mpro inhibitor complexes and the interaction of ligands at the subsites. Conclusion: This approach led to the discovery of novel thiosemicarbazones as potent SARS-CoV-2 Mpro inhibitors.

Insights into targeting SARS-CoV-2: design, synthesis, in silico studies and antiviral evaluation of new dimethylxanthine derivatives

Aiming to achieve efficient activity against severe acute respiratory syndrome coronavirus (SARS-CoV-2), the expansion of the structure- and ligand-based drug design approaches was adopted, which has been recently reported by our research group. Purine ring is a corner stone in the development of SARS-CoV-2 main protease (Mpro) inhibitors. The privileged purine scaffold was elaborated to achieve additional affinity based on hybridization and fragment-based approaches. Thus, the characteristic pharmacophoric features that are required for the inhibition of Mpro and RNA-dependent RNA polymerase (RdRp) of SARS-CoV-2 were utilized along with the crystal structure information of both targets. The designed pathways involved rationalized hybridization with large sulfonamide moieties and a carboxamide fragment for the synthesis of ten new dimethylxanthine derivatives. The synthesis was performed under diverse conditions to afford N-alkylated xanthine derivatives, and cyclization afforded tricyclic compounds. Molecular modeling simulations were used to confirm and gain insights into the binding interactions at both targets' active sites. The merit of designed compounds and the in silico studies resulted in the selection of three compounds that were evaluated in vitro to estimate their antiviral activity against SARS-CoV-2 (compounds 5, 9a and 19 with IC50 values of 38.39, 8.86 and 16.01 μM, respectively). Furthermore, oral toxicity of the selected antiviral candidates was predicted, in addition to cytotoxicity investigations. Compound 9a showed IC50 values of 8.06 and 3.22 μM against Mpro and RdRp of SARS-CoV-2, respectively, in addition to promising molecular dynamics stability in both target active sites. The current findings encourage further specificity evaluations of the promising compounds for confirming their specific protein targeting.

Discovery of quinazolin-4-one-based non-covalent inhibitors targeting the severe acute respiratory syndrome coronavirus 2 main protease (SARS-CoV-2 Mpro)

The COVID-19 pandemic caused by SARS-CoV-2 continues to pose a great threat to public health while various vaccines are available worldwide. Main protease (M^pro) has been validated as an effective anti-COVID-19 drug target. Using medicinal chemistry and rational drug design strategies, we identified a quinazolin-4-one series of nonpeptidic, noncovalent SARS-CoV-2 M^pro inhibitors based on baicalein, 5,6,7-trihydroxy-2-phenyl-4H-chromen-4-one. In particular, compound C7 exhibits superior inhibitory activity against SARS-CoV-2 M^pro relative to baicalein (IC₅₀ = 0.085 ± 0.006 and 0.966 ± 0.065 μM, respectively), as well as improved physicochemical and drug metabolism and pharmacokinetics (DMPK) properties. In addition, C7 inhibits viral replication in SARS-CoV-2-infected Vero E6 cells more effectively than baicalein (EC₅₀ = 1.10 ± 0.12 and 5.15 ± 1.64 μM, respectively) with low cytotoxicity (CC₅₀ ＞ 50 μM). An X-ray co-crystal structure reveals a non-covalent mechanism of action, and a noncanonical binding mode not observed by baicalein. These results suggest that C7 represents a promising lead for development of more effective SARS-CoV-2 M^pro inhibitors and anti-COVID-19 drugs.

In vitro selection of macrocyclic peptide inhibitors containing cyclic γ2,4-amino acids targeting the SARS-CoV-2 main protease

γ-Amino acids can play important roles in the biological activities of natural products; however, the ribosomal incorporation of γ-amino acids into peptides is challenging. Here we report how a selection campaign employing a non-canonical peptide library containing cyclic γ^2,4-amino acids resulted in the discovery of very potent inhibitors of the SARS-CoV-2 main protease (M^pro). Two kinds of cyclic γ^2,4-amino acids, cis-3-aminocyclobutane carboxylic acid (γ¹) and (1R,3S)-3-aminocyclopentane carboxylic acid (γ²), were ribosomally introduced into a library of thioether-macrocyclic peptides. One resultant potent M^pro inhibitor (half-maximal inhibitory concentration = 50 nM), GM4, comprising 13 residues with γ¹ at the fourth position, manifests a 5.2 nM dissociation constant. An M^pro:GM4 complex crystal structure reveals the intact inhibitor spans the substrate binding cleft. The γ¹ interacts with the S1' catalytic subsite and contributes to a 12-fold increase in proteolytic stability compared to its alanine-substituted variant. Knowledge of interactions between GM4 and M^pro enabled production of a variant with a 5-fold increase in potency.

SARS-CoV-2 3CL-protease inhibitors derived from ML300: investigation of P1 and replacements of the 1,2,3-benzotriazole

Starting from compound 5 (CCF0058981), a structure-based optimization of the P1 subsite was performed against the severe acute respiratory syndrome coronavirus (SARS-CoV-2) main protease (3CLpro). Inhibitor 5 and the compounds disclosed bind to 3CLpro using a non-covalent mode of action that utilize a His163 H-bond interaction in the S1 subpocket. In an effort to examine more structurally diverse P1 groups a number of azoles and heterocycles were designed. Several azole ring systems and replacements, including C-linked azoles, with similar or enhanced potency relative to 5 were discovered (28, 29, and 30) with demonstrated IC50 values less than 100 nM. In addition, pyridyl and isoquinoline P1 groups were successful as P1 replacements leading to 3-methyl pyridyl 36 (IC50 = 85 nM) and isoquinoline 27 (IC50 = 26 nM). High resolution X-ray crystal structures of these inhibitors were utilized to confirm binding orientation and guide optimization. These findings have implications towards antiviral development and preparedness to combat SARS-like zoonotic coronavirus outbreaks.

AI-Driven De Novo Design and Molecular Modeling for Discovery of Small-Molecule Compounds as Potential Drug Candidates Targeting SARS-CoV-2 Main Protease

Over the past three years, significant progress has been made in the development of novel promising drug candidates against COVID-19. However, SARS-CoV-2 mutations resulting in the emergence of new viral strains that can be resistant to the drugs used currently in the clinic necessitate the development of novel potent and broad therapeutic agents targeting different vulnerable spots of the viral proteins. In this study, two deep learning generative models were developed and used in combination with molecular modeling tools for de novo design of small molecule compounds that can inhibit the catalytic activity of SARS-CoV-2 main protease (Mpro), an enzyme critically important for mediating viral replication and transcription. As a result, the seven best scoring compounds that exhibited low values of binding free energy comparable with those calculated for two potent inhibitors of Mpro, via the same computational protocol, were selected as the most probable inhibitors of the enzyme catalytic site. In light of the data obtained, the identified compounds are assumed to present promising scaffolds for the development of new potent and broad-spectrum drugs inhibiting SARS-CoV-2 Mpro, an attractive therapeutic target for anti-COVID-19 agents.

COVID-19 therapeutics: small-molecule drug development targeting SARS-CoV-2 main protease

The severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2) is the causative factor behind the 2019 global coronavirus pandemic (COVID-19). The main protease, known as M^pro, is encoded by the viral genome and is essential for viral replication. It has also been an effective target for drug development. In this review, we discuss the rationale for inhibitors that specifically target SARS-CoV-2 M^pro. Small molecules and peptidomimetic inhibitors are two types of inhibitor with various modes of action and we focus here on novel inhibitors that were only discovered during the COVID-19 pandemic highlighting their binding modes and structures.

Transmissible SARS-CoV-2 variants with resistance to clinical protease inhibitors

Vaccines and drugs have helped reduce disease severity and blunt the spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). However, ongoing virus transmission, continuous evolution, and increasing selective pressures have the potential to yield viral variants capable of resisting these interventions. Here, we investigate the susceptibility of natural variants of the main protease [Mpro; 3C-like protease (3CLpro)] of SARS-CoV-2 to protease inhibitors. Multiple single amino acid changes in Mpro confer resistance to nirmatrelvir (the active component of Paxlovid). An additional clinical-stage inhibitor, ensitrelvir (Xocova), shows a different resistance mutation profile. Importantly, phylogenetic analyses indicate that several of these resistant variants have pre-existed the introduction of these drugs into the human population and are capable of spreading. These results encourage the monitoring of resistance variants and the development of additional protease inhibitors and other antiviral drugs with different mechanisms of action and resistance profiles for combinatorial therapy.

Structural basis of main proteases of HCoV-229E bound to inhibitor PF-07304814 and PF-07321332

PF-07321332 and PF-07304814, inhibitors against SARS-CoV-2 developed by Pfizer, exhibit broad-spectrum inhibitory activity against the main protease (M^pro) from various coronaviruses. Structures of PF-07321332 or PF-07304814 in complex with M^pros of various coronaviruses reveal their inhibitory mechanisms against different M^pros. However, the structural information on the lower pathogenic coronavirus M^pro with PF-07321332 or PF-07304814 is currently scarce, which hinders our comprehensive understanding of the inhibitory mechanisms of these two inhibitors. Meanwhile, given that some immunocompromised individuals are still affected by low pathogenic coronaviruses, we determined the structures of lower pathogenic coronavirus HCoV-229E M^pro with PF-07321332 and PF-07304814, respectively, and analyzed and defined in detail the structural basis for the inhibition of HCoV-229E M^pro by both inhibitors. Further, we compared the crystal structures of multiple coronavirus M^pro complexes with PF-07321332 or PF-07304814 to illustrate the differences in the interaction of M^pros, and found that the inhibition mechanism of lower pathogenic coronavirus M^pro was more similar to that of moderately pathogenic coronaviruses. Our structural studies provide new insights into drug development for low pathogenic coronavirus M^pro, and provide theoretical basis for further optimization of both inhibitors to contain potential future coronaviruses.

The impact of SARS-CoV-2 3CL protease mutations on nirmatrelvir inhibitory efficiency. Computational insights into potential resistance mechanisms

The use of antiviral drugs can promote the appearance of mutations in the target protein that increase the resistance of the virus to the treatment. This is also the case of nirmatrelvir, a covalent inhibitor of the 3CL protease, or main protease, of SARS-CoV-2. In this work we show how the by-residue decomposition of noncovalent interactions established between the drug and the enzyme, in combination with an analysis of naturally occurring mutations, can be used to detect potential mutations in the 3CL protease conferring resistance to nirmatrelvir. We also investigate the consequences of these mutations on the reaction mechanism to form the covalent enzyme-inhibitor complex using QM/MM methods. In particular, we show that the E166V variant of the protease displays smaller binding affinity to nirmatrelvir and larger activation free energy for the formation of the covalent complex, both factors contributing to the observed resistance to the treatment with this drug. The conclusions derived from our work can be used to anticipate the consequences of the introduction of nirmatrelvir in the fitness landscape of the virus and to design new inhibitors adapted to some of the possible resistance mechanisms.

Lessons Learnt from COVID-19: Computational Strategies for Facing Present and Future Pandemics

Since its outbreak in December 2019, the COVID-19 pandemic has caused the death of more than 6.5 million people around the world. The high transmissibility of its causative agent, the SARS-CoV-2 virus, coupled with its potentially lethal outcome, provoked a profound global economic and social crisis. The urgency of finding suitable pharmacological tools to tame the pandemic shed light on the ever-increasing importance of computer simulations in rationalizing and speeding up the design of new drugs, further stressing the need for developing quick and reliable methods to identify novel active molecules and characterize their mechanism of action. In the present work, we aim at providing the reader with a general overview of the COVID-19 pandemic, discussing the hallmarks in its management, from the initial attempts at drug repurposing to the commercialization of Paxlovid, the first orally available COVID-19 drug. Furthermore, we analyze and discuss the role of computer-aided drug discovery (CADD) techniques, especially those that fall in the structure-based drug design (SBDD) category, in facing present and future pandemics, by showcasing several successful examples of drug discovery campaigns where commonly used methods such as docking and molecular dynamics have been employed in the rational design of effective therapeutic entities against COVID-19.

Surely you are joking, Mr Docking!

In the wake of recent COVID-19 pandemics scientists around the world rushed to deliver numerous CADD (Computer-Aided Drug Discovery) methods and tools that could be reliably used to discover novel drug candidates against the SARS-CoV-2 virus. With that, there emerged a trend of a significant democratization of CADD that contributed to the rapid development of various COVID-19 drug candidates currently undergoing different stages of validation. On the other hand, this democratization also inadvertently led to the surge rapidly performed molecular docking studies to nominate multiple scores of novel drug candidates supported by computational arguments only. Albeit driven by best intentions, most of such studies also did not follow best practices in the field that require experience and expertise learned through multiple rigorously designed benchmarking studies and rigorous experimental validation. In this Viewpoint we reflect on recent disbalance between small number of rigorous and comprehensive studies and the proliferation of purely computational studies enabled by the ease of docking software availability. We further elaborate on the hyped oversale of CADD methods' ability to rapidly yield viable drug candidates and reiterate the critical importance of rigor and adherence to the best practices of CADD in view of recent emergence of AI and Big Data in the field.

A Systematic Survey of Reversibly Covalent Dipeptidyl Inhibitors of the SARS-CoV-2 Main Protease

SARS-CoV-2 is the coronavirus pathogen of the currently prevailing COVID-19 pandemic. It relies on its main protease (M Pro ) for replication and pathogenesis. M Pro is a demonstrated target for the development of antivirals for SARS-CoV-2. Past studies have systematically explored tripeptidyl inhibitors such as nirmatrelvir as M Pro inhibitors. However, dipeptidyl inhibitors especially those with a spiro residue at their P2 position have not been systematically investigated. In this work, we synthesized about 30 reversibly covalent dipeptidyl M Pro inhibitors and characterized them on in vitro enzymatic inhibition potency, structures of their complexes with M Pro , cellular M Pro inhibition potency, antiviral potency, cytotoxicity, and in vitro metabolic stability. Our results indicated that M Pro has a flexible S2 pocket that accommodates dipeptidyl inhibitors with a large P2 residue and revealed that dipeptidyl inhibitors with a large P2 spiro residue such as ( S )-2-azaspiro[4,4]nonane-3-carboxylate and ( S )-2-azaspiro[4,5]decane-3-carboxylate have optimal characteristics. One compound MPI60 containing a P2 ( S )-2-azaspiro[4,4]nonane-3-carboxylate displayed high antiviral potency, low cellular cytotoxicity, and high in vitro metabolic stability and can be potentially advanced to further preclinical tests.

Chetomin, a SARS-CoV-2 3C-like Protease (3CLpro) Inhibitor: In Silico Screening, Enzyme Docking, Molecular Dynamics and Pharmacokinetics Analysis

The emergence of the Coronavirus Disease 2019 (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has led to over 6 million deaths. The 3C-like protease (3CLpro) enzyme of the SARS-CoV-2 virus is an attractive druggable target for exploring therapeutic drug candidates to combat COVID-19 due to its key function in viral replication. Marine natural products (MNPs) have attracted considerable attention as alternative sources of antiviral drug candidates. In looking for potential 3CLpro inhibitors, the MNP database (>14,000 molecules) was virtually screened against 3CLpro with the assistance of molecular docking computations. The performance of AutoDock and OEDocking software in anticipating the ligand-3CLpro binding mode was first validated according to the available experimental data. Based on the docking scores, the most potent MNPs were further subjected to molecular dynamics (MD) simulations, and the binding affinities of those molecules were computed using the MM-GBSA approach. According to MM-GBSA//200 ns MD simulations, chetomin (UMHMNP1403367) exhibited a higher binding affinity against 3CLpro than XF7, with ΔGbinding values of −55.5 and −43.7 kcal/mol, respectively. The steadiness and tightness of chetomin with 3CLpro were evaluated, revealing the high stabilization of chetomin (UMHMNP1403367) inside the binding pocket of 3CLpro throughout 200 ns MD simulations. The physicochemical and pharmacokinetic features of chetomin were also predicted, and the oral bioavailability of chetomin was demonstrated. Furthermore, the potentiality of chetomin analogues −namely, chetomin A-D− as 3CLpro inhibitors was investigated. These results warrant further in vivo and in vitro assays of chetomin (UMHMNP1403367) as a promising anti-COVID-19 drug candidate.