Ultralarge Virtual Screening Identifies SARS-CoV-2 Main Protease Inhibitors with Broad-Spectrum Activity against Coronaviruses

Structure-based virtual screening of vast chemical space as a starting point for drug discovery

Structure-based virtual screening aims to find molecules forming favorable interactions with a biological macromolecule using computational models of complexes. The recent surge of commercially available chemical space provides the opportunity to search for ligands of therapeutic targets among billions of compounds. This review offers a compact overview of structure-based virtual screens of vast chemical spaces, highlighting successful applications in early drug discovery for therapeutically important targets such as G protein-coupled receptors and viral enzymes. Emphasis is placed on strategies to explore ultra-large chemical libraries and synergies with emerging machine learning techniques. The current opportunities and future challenges of virtual screening are discussed, indicating that this approach will play an important role in the next-generation drug discovery pipeline.

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.

SARS-CoV-2-induced phosphorylation and its pharmacotherapy backed by artificial intelligence and machine learning

Aims: To investigate the role of phosphorylation in SARS-CoV-2 infection, potential therapeutic targets and its harmful genetic sequences. Materials & Methods: Data mining techniques were employed to identify upregulated kinases responsible for proteomic changes induced by SARS-CoV-2. Spike and nucleocapsid proteins' sequences were analyzed using predictive tools, including SNAP2, MutPred2, PhD-SNP, SNPs&Go, MetaSNP, Predict-SNP and PolyPhen-2. Missense variants were identified using ensemble-based algorithms and homology/structure-based models like SIFT, PROVEAN, Predict-SNP and MutPred-2. Results: Eight missense variants were identified in viral sequences. Four damaging variants were found, with SNPs&Go and PolyPhen-2. Promising therapeutic candidates, including gilteritinib, pictilisib, sorafenib, RO5126766 and omipalisib, were identified. Conclusion: This research offers insights into SARS-CoV-2 pathogenicity, highlighting potential treatments and harmful variants in viral proteins.

Vector competence of Swedish Culex pipiens mosquitoes for Japanese encephalitis virus

BACKGROUND

Japanese encephalitis virus (JEV) is an emerging mosquito-borne Orthoflavivirus that poses a significant public health risk in many temperate and tropical regions in Asia. Since the climate in some endemic countries is similar to temperate climates observed in Europe, understanding the role of specific mosquito species in the transmission of JEV is essential for predicting and effectively controlling the potential for the introduction and establishment of JEV in Europe.

METHODS

This study aimed to investigate the vector competence of colonized Culex pipiens biotype molestus mosquitoes for JEV. The mosquitoes were initially collected from the field in southern Sweden. The mosquitoes were offered a blood meal containing the Nakayama strain of JEV (genotype III), and infection rates, dissemination rates, and transmission rates were evaluated at 14, 21, and 28 days post-feeding.

RESULTS

The study revealed that colonized Swedish Cx. pipiens are susceptible to JEV infection, with a stable infection rate of around 10% at all timepoints. However, the virus was only detected in the legs of one mosquito at 21 days post-feeding, and no mosquito saliva contained JEV.

CONCLUSIONS

Overall, this research shows that Swedish Cx. pipiens can become infected with JEV, and emphasizes the importance of further understanding of the thresholds and barriers for JEV dissemination in mosquitoes.

COVID-19 drug discovery and treatment options

The coronavirus disease 2019 (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused substantial morbidity and mortality, and serious social and economic disruptions worldwide. Unvaccinated or incompletely vaccinated older individuals with underlying diseases are especially prone to severe disease. In patients with non-fatal disease, long COVID affecting multiple body systems may persist for months. Unlike SARS-CoV and Middle East respiratory syndrome coronavirus, which have either been mitigated or remained geographically restricted, SARS-CoV-2 has disseminated globally and is likely to continue circulating in humans with possible emergence of new variants that may render vaccines less effective. Thus, safe, effective and readily available COVID-19 therapeutics are urgently needed. In this Review, we summarize the major drug discovery approaches, preclinical antiviral evaluation models, representative virus-targeting and host-targeting therapeutic options, and key therapeutics currently in clinical use for COVID-19. Preparedness against future coronavirus pandemics relies not only on effective vaccines but also on broad-spectrum antivirals targeting conserved viral components or universal host targets, and new therapeutics that can precisely modulate the immune response during infection.

Quantitative Assessment of Energetic Contributions of Residues in a SARS-CoV-2 Viral Enzyme/Nanobody Interface

The highly conserved protease enzyme from SARS-CoV-2 (MPro) is crucial for viral replication and is an attractive target for the design of novel inhibitory compounds. MPro is known to be conformationally flexible and has been stabilized in an extended conformation in a complex with a novel nanobody (NB2B4), which inhibits the dimerization of the enzyme via binding to an allosteric site. However, the energetic contributions of the nanobody residues stabilizing the MPro/nanobody interface remain unresolved. We probed these residues using all-atom MD simulations in combination with alchemical free energy calculations by studying the physical residue-residue interactions and discovered the role of hydrophobic and electrostatic interactions in stabilizing the complex. Specifically, we found via mutational analysis that three interfacial nanobody residues (Y59, R106, and L109) contributed significantly, two residues (L107 and P110) contributed moderately, and two residues (H112 and T113) contributed minimally to the overall binding affinity of the nanobody. We also discovered that the nanobody affinity could be enhanced via a charge-reversal mutation (D62R) that alters the local interfacial electrostatic environment of this residue in the complex. These findings are potentially useful in designing novel synthetic nanobodies as allosteric inhibitors of MPro.

Development of Fluorescence-Based Assays for Key Viral Proteins in the SARS-CoV-2 Infection Process and Lifecycle

Since the appearance of SARS-CoV-2 in 2019, the ensuing COVID-19 (Corona Virus Disease 2019) pandemic has posed a significant threat to the global public health system, human health, life, and economic well-being. Researchers worldwide have devoted considerable efforts to curb its spread and development. The latest studies have identified five viral proteins, spike protein (Spike), viral main protease (3CLpro), papain-like protease (PLpro), RNA-dependent RNA polymerase (RdRp), and viral helicase (Helicase), which play crucial roles in the invasion of SARS-CoV-2 into the human body and its lifecycle. The development of novel anti-SARS-CoV-2 drugs targeting these five viral proteins holds immense promise. Therefore, the development of efficient, high-throughput screening methodologies specifically designed for these viral proteins is of utmost importance. Currently, a plethora of screening techniques exists, with fluorescence-based assays emerging as predominant contenders. In this review, we elucidate the foundational principles and methodologies underpinning fluorescence-based screening approaches directed at these pivotal viral targets, hoping to guide researchers in the judicious selection and refinement of screening strategies, thereby facilitating the discovery and development of lead compounds for anti-SARS-CoV-2 pharmaceuticals.

Toward physics-based precision medicine: Exploiting protein dynamics to design new therapeutics and interpret variants

The goal of precision medicine is to utilize our knowledge of the molecular causes of disease to better diagnose and treat patients. However, there is a substantial mismatch between the small number of food and drug administration (FDA)-approved drugs and annotated coding variants compared to the needs of precision medicine. This review introduces the concept of physics-based precision medicine, a scalable framework that promises to improve our understanding of sequence-function relationships and accelerate drug discovery. We show that accounting for the ensemble of structures a protein adopts in solution with computer simulations overcomes many of the limitations imposed by assuming a single protein structure. We highlight studies of protein dynamics and recent methods for the analysis of structural ensembles. These studies demonstrate that differences in conformational distributions predict functional differences within protein families and between variants. Thanks to new computational tools that are providing unprecedented access to protein structural ensembles, this insight may enable accurate predictions of variant pathogenicity for entire libraries of variants. We further show that explicitly accounting for protein ensembles, with methods like alchemical free energy calculations or docking to Markov state models, can uncover novel lead compounds. To conclude, we demonstrate that cryptic pockets, or cavities absent in experimental structures, provide an avenue to target proteins that are currently considered undruggable. Taken together, our review provides a roadmap for the field of protein science to accelerate precision medicine.

PL-PatchSurfer3: Improved Structure-Based Virtual Screening for Structure Variation Using 3D Zernike Descriptors

Structure-based virtual screening (SBVS) is a widely used method in silico drug discovery. It necessitates a receptor structure or binding site to predict the binding pose and fitness of a ligand. Therefore, the performance of the SBVS is affected by the protein conformation. The most frequently used method in SBVS is the protein-ligand docking program, which utilizes atomic distance-based scoring functions. Hence, they are highly prone to sensitivity towards variation in receptor structure, and it is reported that the conformational change significantly drops the performance of the docking program. To address the problem, we have introduced a novel program of SBVS, named PL-PatchSurfer. This program makes use of molecular surface patches and the Zernike descriptor. The surfaces of the pocket and ligand are segmented into several patches by the program. These patches are then mapped with physico-chemical properties such as shape and electrostatic potential before being converted into the Zernike descriptor, which is rotationally invariant. A complementarity between the protein and the ligand is assessed by comparing the descriptors and geometric distribution of the patches in the molecules. A benchmarking study showed that PL-PatchSurfer2 was able to screen active molecules regardless of the receptor structure change with fast speed. However, the program could not achieve high performance for the targets that the hydrogen bonding feature is important such as nuclear hormone receptors. In this paper, we present the newer version of PL-PatchSurfer, PL-PatchSurfer3, which incorporates two new features: a change in the definition of hydrogen bond complementarity and consideration of visibility that contains curvature information of a patch. Our evaluation demonstrates that the new program outperforms its predecessor and other SBVS methods while retaining its characteristic tolerance to receptor structure changes. Interested individuals can access the program at kiharalab.org/plps3.

Fragment-to-Lead Medicinal Chemistry Publications in 2022

This Perspective is the eighth in an annual series that summarizes successful fragment-to-lead (F2L) case studies published each year. A tabulated summary of relevant articles published in 2022 is provided, and features such as target class, screening methods, and ligand efficiency are discussed both for the 2022 examples and for the combined examples over the years 2015-2022. In addition, trends and new developments in the field are summarized. In 2022, 18 publications described successful fragment-to-lead studies, including the development of three clinical compounds (MTRX1719, MK-8189, and BI-823911).

Perspectives on Computational Enzyme Modeling: From Mechanisms to Design and Drug Development

Understanding enzyme mechanisms is essential for unraveling the complex molecular machinery of life. In this review, we survey the field of computational enzymology, highlighting key principles governing enzyme mechanisms and discussing ongoing challenges and promising advances. Over the years, computer simulations have become indispensable in the study of enzyme mechanisms, with the integration of experimental and computational exploration now established as a holistic approach to gain deep insights into enzymatic catalysis. Numerous studies have demonstrated the power of computer simulations in characterizing reaction pathways, transition states, substrate selectivity, product distribution, and dynamic conformational changes for various enzymes. Nevertheless, significant challenges remain in investigating the mechanisms of complex multistep reactions, large-scale conformational changes, and allosteric regulation. Beyond mechanistic studies, computational enzyme modeling has emerged as an essential tool for computer-aided enzyme design and the rational discovery of covalent drugs for targeted therapies. Overall, enzyme design/engineering and covalent drug development can greatly benefit from our understanding of the detailed mechanisms of enzymes, such as protein dynamics, entropy contributions, and allostery, as revealed by computational studies. Such a convergence of different research approaches is expected to continue, creating synergies in enzyme research. This review, by outlining the ever-expanding field of enzyme research, aims to provide guidance for future research directions and facilitate new developments in this important and evolving field.

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.

Multiple redox switches of the SARS-CoV-2 main protease in vitro provide opportunities for drug design

Besides vaccines, the development of antiviral drugs targeting SARS-CoV-2 is critical for preventing future COVID outbreaks. The SARS-CoV-2 main protease (Mpro), a cysteine protease with essential functions in viral replication, has been validated as an effective drug target. Here, we show that Mpro is subject to redox regulation in vitro and reversibly switches between the enzymatically active dimer and the functionally dormant monomer through redox modifications of cysteine residues. These include a disulfide-dithiol switch between the catalytic cysteine C145 and cysteine C117, and generation of an allosteric cysteine-lysine-cysteine SONOS bridge that is required for structural stability under oxidative stress conditions, such as those exerted by the innate immune system. We identify homo- and heterobifunctional reagents that mimic the redox switching and inhibit Mpro activity. The discovered redox switches are conserved in main proteases from other coronaviruses, e.g. MERS-CoV and SARS-CoV, indicating their potential as common druggable sites.

Targeting ion channels with ultra-large library screening for hit discovery

Ion channels play a crucial role in a variety of physiological and pathological processes, making them attractive targets for drug development in diseases such as diabetes, epilepsy, hypertension, cancer, and chronic pain. Despite the importance of ion channels in drug discovery, the vastness of chemical space and the complexity of ion channels pose significant challenges for identifying drug candidates. The use of in silico methods in drug discovery has dramatically reduced the time and cost of drug development and has the potential to revolutionize the field of medicine. Recent advances in computer hardware and software have enabled the screening of ultra-large compound libraries. Integration of different methods at various scales and dimensions is becoming an inevitable trend in drug development. In this review, we provide an overview of current state-of-the-art computational chemistry methodologies for ultra-large compound library screening and their application to ion channel drug discovery research. We discuss the advantages and limitations of various in silico techniques, including virtual screening, molecular mechanics/dynamics simulations, and machine learning-based approaches. We also highlight several successful applications of computational chemistry methodologies in ion channel drug discovery and provide insights into future directions and challenges in this field.

Discovery of 3-oxo-1,2,3,4-tetrahydropyrido[1,2-a]pyrazin derivatives as SARS-CoV-2 main protease inhibitors through virtual screening and biological evaluation

The COVID-19 caused by SARS-CoV-2 has led to a global pandemic that continues to impact societies and economies worldwide. The main protease (Mpro) plays a crucial role in SARS-CoV-2 replication and is an attractive target for anti-SARS-CoV-2 drug discovery. Herein, we report a series of 3-oxo-1,2,3,4-tetrahydropyrido[1,2-a]pyrazin derivatives as non-peptidomimetic inhibitors targeting SARS-CoV-2 Mpro through structure-based virtual screening and biological evaluation. Further similarity search and structure-activity relationship study led to the identification of compound M56-S2 with the enzymatic IC50 value of 4.0 μM. Moreover, the molecular simulation and predicted ADMET properties, indicated that non-peptidomimetic inhibitor M56-S2 might serve as a useful starting point for the further discovery of highly potent inhibitors targeting SARS-CoV-2 Mpro.

Multiplexed experimental strategies for fragment library screening against challenging drug targets using SPR biosensors

Surface plasmon resonance (SPR) biosensor methods are ideally suited for fragment-based lead discovery.  However, generally applicable experimental procedures and detailed protocols are lacking, especially for structurally or physico-chemically challenging targets or when tool compounds are not available. Success depends on accounting for the features of both the target and the chemical library, purposely designing screening experiments for identification and validation of hits with desired specificity and mode-of-action, and availability of orthogonal methods capable of confirming fragment hits. The range of targets and libraries amenable to an SPR biosensor-based approach for identifying hits is considerably expanded by adopting multiplexed strategies, using multiple complementary surfaces or experimental conditions. Here we illustrate principles and multiplexed approaches for using flow-based SPR biosensor systems for screening fragment libraries of different sizes (90 and 1056 compounds) against a selection of challenging targets. It shows strategies for the identification of fragments interacting with 1) large and structurally dynamic targets, represented by acetyl choline binding protein (AChBP), a Cys-loop receptor ligand gated ion channel homologue, 2) targets in multi protein complexes, represented by lysine demethylase 1 and a corepressor (LSD1/CoREST), 3) structurally variable or unstable targets, represented by farnesyl pyrophosphate synthase (FPPS), 4) targets containing intrinsically disordered regions, represented by protein tyrosine phosphatase 1B  (PTP1B), and 5) aggregation-prone proteins, represented by an engineered form of human tau  (tau K18M). Practical considerations and procedures accounting for the characteristics of the proteins and libraries, and that increase robustness, sensitivity, throughput and versatility are highlighted. The study shows that the challenges for addressing these types of targets is not identification of potentially useful fragments per se, but establishing methods for their validation and evolution into leads.

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.

De Novo Potent Peptide Nucleic Acid Antisense Oligomer Inhibitors Targeting SARS-CoV-2 RNA-Dependent RNA Polymerase via Structure-Guided Drug Design

Global reports of novel SARS-CoV-2 variants and recurrence cases continue despite substantial vaccination campaigns, raising severe concerns about COVID-19. While repurposed drugs offer some treatment options for COVID-19, notably, nucleoside inhibitors like Remdesivir stand out as curative therapies for COVID-19 that are approved by the US Food and Drug Administration (FDA). The emergence of highly contagious SARS-CoV-2 variants underscores the imperative for antiviral drugs adaptable to evolving viral mutations. RNA-dependent RNA polymerase (RdRp) plays a key role in viral genome replication. Currently, inhibiting viral RdRp function remains a pivotal strategy to tackle the notorious virus. Peptide nucleic acid (PNA) therapy shows promise by effectively targeting specific genome regions, reducing viral replication, and inhibiting infection. In our study, we designed PNA antisense oligomers conjugated with cell-penetrating peptides (CPP) aiming to evaluate their antiviral effects against RdRp target using structure-guided drug design, which involves molecular docking simulations, drug likeliness and pharmacokinetic evaluations, molecular dynamics simulations, and computing binding free energy. The in silico analysis predicts that chemically modified PNAs might act as antisense molecules in order to disrupt ribosome assembly at RdRp's translation start site, and their chemically stable and neutral backbone might enhance sequence-specific RNA binding interaction. Notably, our findings demonstrate that PNA-peptide conjugates might be the most promising inhibitors of SARS-CoV-2 RdRp, with superior binding free energy compared to Remdesivir in the current COVID-19 medication. Specifically, PNA-CPP-1 could bind simultaneously to the active site residues of RdRp protein and sequence-specific RdRp-RNA target in order to control viral replication.

Identification of synthetically tractable MERS-CoV main protease inhibitors using structure-based virtual screening and molecular dynamics potential of mean force (PMF) calculations

The Middle East Respiratory Syndrome Coronavirus (MERS-CoV) is a potentially lethal infection that presents a substantial threat to health, especially in Middle East nations. Given that no FDA-approved specific therapy for MERS infection exists, designing and discovering a potent antiviral therapy for MERS-CoV is crucial. One pivotal strategy for inhibiting MERS replication is to focus on the viral main protease (Mpro). In this study, we identify potential novel Mpro inhibitors employing structure-based virtual screening of our recently reported Ugi reaction-derived library (URDL) consisting of cherry-picked molecules from the literature. The key features of the URDL library include synthetic tractability (1-2 pot synthesis) of the molecules scaffold and unexplored chemical space. The hits were ranked based on the docking score, MM-GBSA free energy of binding, and the interaction pattern with the active site residues. A molecular dynamics (MD) simulation study was performed for the first two top-ranked compounds to analyze the stability and free binding energy based on the molecular mechanics Poisson-Boltzmann surface area. The potential mean force calculated from the steered molecular dynamics (SMD) simulations of the hits indicates improved H-bond potential, enhanced conformational stability, and binding affinity toward the target, compared to the cocrystallized ligand. The discovered hits represent novel synthetically tractable scaffolds as potential MERS-CoV Mpro inhibitors.Communicated by Ramaswamy H. Sarma.

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.

Preparing for the next pandemic

New lead drugs to treat COVID-19 are beginning to emerge.

EasyDock: customizable and scalable docking tool

Docking of large compound collections becomes an important procedure to discover new chemical entities. Screening of large sets of compounds may also occur in de novo design projects guided by molecular docking. To facilitate these processes, there is a need for automated tools capable of efficiently docking a large number of molecules using multiple computational nodes within a reasonable timeframe. These tools should also allow for easy integration of new docking programs and provide a user-friendly program interface to support the development of further approaches utilizing docking as a foundation. Currently available tools have certain limitations, such as lacking a convenient program interface or lacking support for distributed computations. In response to these limitations, we have developed a module called EasyDock. It can be deployed over a network of computational nodes using the Dask library, without requiring a specific cluster scheduler. Furthermore, we have proposed and implemented a simple model that predicts the runtime of docking experiments and applied it to minimize overall docking time. The current version of EasyDock supports popular docking programs, namely Autodock Vina, gnina, and smina. Additionally, we implemented a supplementary feature to enable docking of boron-containing compounds, which are not inherently supported by Vina and smina, and demonstrated its applicability on a set of 55 PDB protein-ligand complexes.

A practical guide to machine-learning scoring for structure-based virtual screening

Structure-based virtual screening (SBVS) via docking has been used to discover active molecules for a range of therapeutic targets. Chemical and protein data sets that contain integrated bioactivity information have increased both in number and in size. Artificial intelligence and, more concretely, its machine-learning (ML) branch, including deep learning, have effectively exploited these data sets to build scoring functions (SFs) for SBVS against targets with an atomic-resolution 3D model (e.g., generated by X-ray crystallography or predicted by AlphaFold2). Often outperforming their generic and non-ML counterparts, target-specific ML-based SFs represent the state of the art for SBVS. Here, we present a comprehensive and user-friendly protocol to build and rigorously evaluate these new SFs for SBVS. This protocol is organized into four sections: (i) using a public benchmark of a given target to evaluate an existing generic SF; (ii) preparing experimental data for a target from public repositories; (iii) partitioning data into a training set and a test set for subsequent target-specific ML modeling; and (iv) generating and evaluating target-specific ML SFs by using the prepared training-test partitions. All necessary code and input/output data related to three example targets (acetylcholinesterase, HMG-CoA reductase, and peroxisome proliferator-activated receptor-α) are available at https://github.com/vktrannguyen/MLSF-protocol , can be run by using a single computer within 1 week and make use of easily accessible software/programs (e.g., Smina, CNN-Score, RF-Score-VS and DeepCoy) and web resources. Our aim is to provide practical guidance on how to augment training data to enhance SBVS performance, how to identify the most suitable supervised learning algorithm for a data set, and how to build an SF with the highest likelihood of discovering target-active molecules within a given compound library.

Developing a SARS-CoV-2 main protease binding prediction random forest model for drug repurposing for COVID-19 treatment

The coronavirus disease 2019 (COVID-19) global pandemic resulted in millions of people becoming infected with the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus and close to seven million deaths worldwide. It is essential to further explore and design effective COVID-19 treatment drugs that target the main protease of SARS-CoV-2, a major target for COVID-19 drugs. In this study, machine learning was applied for predicting the SARS-CoV-2 main protease binding of Food and Drug Administration (FDA)-approved drugs to assist in the identification of potential repurposing candidates for COVID-19 treatment. Ligands bound to the SARS-CoV-2 main protease in the Protein Data Bank and compounds experimentally tested in SARS-CoV-2 main protease binding assays in the literature were curated. These chemicals were divided into training (516 chemicals) and testing (360 chemicals) data sets. To identify SARS-CoV-2 main protease binders as potential candidates for repurposing to treat COVID-19, 1188 FDA-approved drugs from the Liver Toxicity Knowledge Base were obtained. A random forest algorithm was used for constructing predictive models based on molecular descriptors calculated using Mold2 software. Model performance was evaluated using 100 iterations of fivefold cross-validations which resulted in 78.8% balanced accuracy. The random forest model that was constructed from the whole training dataset was used to predict SARS-CoV-2 main protease binding on the testing set and the FDA-approved drugs. Model applicability domain and prediction confidence on drugs predicted as the main protease binders discovered 10 FDA-approved drugs as potential candidates for repurposing to treat COVID-19. Our results demonstrate that machine learning is an efficient method for drug repurposing and, thus, may accelerate drug development targeting SARS-CoV-2.

Identification of novel 1,2,3-triazole isatin derivatives as potent SARS-CoV-2 3CLpro inhibitors via click-chemistry-based rapid screening

SARS-CoV-2 3-chymotrypsin-like protease (3CLpro) is considered an attractive target for the development of anti-COVID-19 agents due to its vital function. The N-substituted isatin derivative L-26 is a potential SARS-CoV-2 3CLpro inhibitor, but it has poor cell-based antiviral activity and high cytotoxicity. With L-26 as the lead compound, 58 isatin derivatives were prepared using click-chemistry-based miniaturized synthesis and their 3CLpro inhibitory activities were determined by a fluorescence resonance energy transfer-based enzymatic assay. Compounds D1N8 (IC50 = 0.44 ± 0.12 μM) and D1N52 (IC50 = 0.53 ± 0.21 μM) displayed excellent inhibitory potency against SARS-CoV-2 3CLpro, being equivalent to that of L-26 (IC50 = 0.30 ± 0.14 μM). In addition, the cytotoxicity of D1N8 (CC50 >20 μM) and D1N52 (CC50 >20 μM) decreased significantly compared with L-26 (CC50 <2.6 μM). Further molecular dynamics simulations revealed the potential binding interactions between D1N52 and SARS-CoV-2 3CLpro. These efforts lay a solid foundation for the research of novel anti-SARS-CoV-2 agents targeting 3CLpro.

Promising Experimental Anti-SARS-CoV-2 Agent "SLL-0197800": The Prospective Universal Inhibitory Properties against the Coming Versions of the Coronavirus

Isoquinoline derivatives having some nucleosidic structural features are considered as candidate choices for effective remediation of the different severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections and their following disease, the coronavirus disease 2019 (COVID-19). SLL-0197800 is a recently discovered isoquinoline compound with potential strong universal anticoronaviral activities against SARS-CoV-2 and its previous strains. SLL-0197800 nonspecifically hits the main protease (Mpro) enzyme of the different coronaviruses. Herein in the present study, we tested the probability of the previous findings of this experimental agent to be extended to comprise any coronavirus through concurrently disrupting the mutable-less replication enzymes like the RNA-dependent RNA polymerase (RdRp) protein as well as the 3'-to-5' exoribonuclease (ExoN) protein. The in vitro anti-RdRp/ExoN assay revealed the potent inhibitory activities of SLL-0197800 on the coronaviral replication with minute values of anti-RdRp and anti-RdRp/ExoN EC50 (about 0.16 and 0.27 μM, respectively). The preliminary in silico outcomes significantly supported these biochemical findings. To put it simply, the present important results of these extension efforts greatly reinforce and extend the SLL-0197800's preceding findings, showing that the restraining/blocking actions (i.e., inhibitory activities) of this novel investigational anti-SARS-CoV-2 agent against the Mpro protein could be significantly extended against other copying and multiplication enzymes such as RdRp and ExoN, highlighting the potential use of SLL-0197800 against the coming versions of the homicidal coronavirus (if any), i.e., revealing the probable nonspecific anticoronaviral features and qualities of this golden experimental drug against nearly any coronaviral strain, for instance, SARS-CoV-3.

Advancements in small molecule drug design: A structural perspective

In this review, we outline recent advancements in small molecule drug design from a structural perspective. We compare protein structure prediction methods and explore the role of the ligand binding pocket in structure-based drug design. We examine various structural features used to optimize drug candidates, including functional groups, stereochemistry, and molecular weight. Computational tools such as molecular docking and virtual screening are discussed for predicting and optimizing drug candidate structures. We present examples of drug candidates designed based on their molecular structure and discuss future directions in the field. By effectively integrating structural information with other valuable data sources, we can improve the drug discovery process, leading to the identification of novel therapeutics with improved efficacy, specificity, and safety profiles.

Structural biology of SARS-CoV-2 Mpro and drug discovery

Since its outbreak in late 2019, the COVID-19 pandemic has drawn enormous attention worldwide as a consequence of being the most disastrous infectious disease in the past century. As one of the most immediately druggable targets of SARS-CoV-2, the main protease (Mpro) has been studied thoroughly. In this review, we provide a comprehensive summary of recent advances in structural studies of Mpro, which provide new knowledge about Mpro in terms of its biological function, structural characteristics, substrate specificity, and autocleavage process. We examine the remarkable strides made in targeting Mpro for drug discovery during the pandemic. We summarize insights into the current understanding of the structural features of Mpro and the discovery of existing Mpro-targeting drugs, illuminating pathways for the future development of anti-SARS-CoV-2 therapeutics.

Is Target-Based Drug Discovery Efficient? Discovery and "Off-Target" Mechanisms of All Drugs

Target-based drug discovery is the dominant paradigm of drug discovery; however, a comprehensive evaluation of its real-world efficiency is lacking. Here, a manual systematic review of about 32000 articles and patents dating back to 150 years ago demonstrates its apparent inefficiency. Analyzing the origins of all approved drugs reveals that, despite several decades of dominance, only 9.4% of small-molecule drugs have been discovered through "target-based" assays. Moreover, the therapeutic effects of even this minimal share cannot be solely attributed and reduced to their purported targets, as they depend on numerous off-target mechanisms unconsciously incorporated by phenotypic observations. The data suggest that reductionist target-based drug discovery may be a cause of the productivity crisis in drug discovery. An evidence-based approach to enhance efficiency seems to be prioritizing, in selecting and optimizing molecules, higher-level phenotypic observations that are closer to the sought-after therapeutic effects using tools like artificial intelligence and machine learning.

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.

Identification of motif-based interactions between SARS-CoV-2 protein domains and human peptide ligands pinpoint antiviral targets

The virus life cycle depends on host-virus protein-protein interactions, which often involve a disordered protein region binding to a folded protein domain. Here, we used proteomic peptide phage display (ProP-PD) to identify peptides from the intrinsically disordered regions of the human proteome that bind to folded protein domains encoded by the SARS-CoV-2 genome. Eleven folded domains of SARS-CoV-2 proteins were found to bind 281 peptides from human proteins, and affinities of 31 interactions involving eight SARS-CoV-2 protein domains were determined (KD ∼ 7-300 μM). Key specificity residues of the peptides were established for six of the interactions. Two of the peptides, binding Nsp9 and Nsp16, respectively, inhibited viral replication. Our findings demonstrate how high-throughput peptide binding screens simultaneously identify potential host-virus interactions and peptides with antiviral properties. Furthermore, the high number of low-affinity interactions suggest that overexpression of viral proteins during infection may perturb multiple cellular pathways.

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.

Recent Advances in SARS-CoV-2 Main Protease Inhibitors: From Nirmatrelvir to Future Perspectives

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.

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.

FP-MAP: an extensive library of fingerprint-based molecular activity prediction tools

Discovering new drugs for disease treatment is challenging, requiring a multidisciplinary effort as well as time, and resources. With a view to improving hit discovery and lead compound identification, machine learning (ML) approaches are being increasingly used in the decision-making process. Although a number of ML-based studies have been published, most studies only report fragments of the wider range of bioactivities wherein each model typically focuses on a particular disease. This study introduces FP-MAP, an extensive atlas of fingerprint-based prediction models that covers a diverse range of activities including neglected tropical diseases (caused by viral, bacterial and parasitic pathogens) as well as other targets implicated in diseases such as Alzheimer's. To arrive at the best predictive models, performance of ≈4,000 classification/regression models were evaluated on different bioactivity data sets using 12 different molecular fingerprints. The best performing models that achieved test set AUC values of 0.62-0.99 have been integrated into an easy-to-use graphical user interface that can be downloaded from https://gitlab.com/vishsoft/fpmap.

Interactions between carbon nanotubes and external structures of SARS-CoV-2 using molecular docking and molecular dynamics

Molecular modeling techniques are used to describe the process of interaction between nanotubes and the main structures of the Covid-19 virus: the envelope protein, the main protease, and the Spike glycoprotein. Molecular docking studies show that the ligands have interaction characteristics capable of adsorbing the structures. Molecular dynamics simulations provide information on the mean squared deviation of atomic positions ​​between 0.5 and 3.0 Å. The Gibbs free energy model and solvent accessible surface area approaches are used. Through the results obtained through molecular dynamics simulations, it is noted that the zig-zag nanotube prefers to interact with E-pro, M-pro, and S-gly, respectively. Molecular couplings and free energy showed that the S-gly active site residues strongly interact with zigzag, chiral, and armchair nanotubes, in this order. The interactions demonstrated in this manuscript may predict some promising candidates for virus antagonists, which may be confirmed through experimental approaches.

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.

Novel Inhibitors of androgen receptor's DNA binding domain identified using an ultra-large virtual screening

Androgen receptor (AR) inhibition remains the primary strategy to combat the progression of prostate cancer (PC). However, all clinically used AR inhibitors target the ligand-binding domain (LBD), which is highly susceptible to truncations through splicing or mutations that confer drug resistance. Thus, there exists an urgent need for AR inhibitors with novel modes of action. We thus launched a virtual screening of an ultra-large chemical library to find novel inhibitors of the AR DNA-binding domain (DBD) at two sites: protein-DNA interface (P-box) and dimerization site (D-box). The compounds selected through vigorous computational filtering were then experimentally validated. We identified several novel chemotypes that effectively suppress transcriptional activity of AR and its splice variant V7. The identified compounds represent previously unexplored chemical scaffolds with a mechanism of action that evades the conventional drug resistance manifested through LBD mutations. Additionally, we describe the binding features required to inhibit AR DBD at both P-box and D-box target sites.

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.

Enantioselective synthesis of hydantoins by chiral acid-catalysed condensation of glyoxals and ureas

Hydantoins are important scaffolds in natural products and pharmaceuticals, with only a few synthetic strategies available for their asymmetric preparation. We herein describe a single-step enantioselective synthesis of 5-monosubstituted hydantoins via condensation of glyoxals and ureas in the presence of a chiral phosphoric acid at room temperature. Products were formed in up to 99% yield and 98 : 2 e.r. Using mechanistic and kinetic studies, including time course ¹H NMR monitoring, we revealed that the reaction likely proceeds via face-selective protonation of an enol-type intermediate.

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.

Structure-Based Discovery of Inhibitors of the SARS-CoV-2 Nsp14 N7-Methyltransferase

An under-explored target for SARS-CoV-2 is the S-adenosyl methionine (SAM)-dependent methyltransferase Nsp14, which methylates the N7-guanosine of viral RNA at the 5'-end, allowing the virus to evade host immune response. We sought new Nsp14 inhibitors with three large library docking strategies. First, up to 1.1 billion lead-like molecules were docked against the enzyme's SAM site, leading to three inhibitors with IC50 values from 6 to 50 μM. Second, docking a library of 16 million fragments revealed 9 new inhibitors with IC50 values from 12 to 341 μM. Third, docking a library of 25 million electrophiles to covalently modify Cys387 revealed 7 inhibitors with IC50 values from 3.5 to 39 μM. Overall, 32 inhibitors encompassing 11 chemotypes had IC50 values < 50 μM and 5 inhibitors in 4 chemotypes had IC50 values < 10 μM. These molecules are among the first non-SAM-like inhibitors of Nsp14, providing starting points for future optimization.

MAX IV Laboratory

MAX IV Laboratory is a Swedish national synchrotron radiation facility that comprises three accelerators with varying characteristics. One of the accelerators, the 3 GeV storage ring, is the world's first fourth-generation ring and pioneered the use of the multibend achromat lattice to provide access to ultrahigh brightness X-rays. MAX IV aims to stay at the forefront of the current and future research needs of its multidisciplinary user community, principally located in the Nordic and Baltic regions. Our 16 beamlines currently offer and continue to develop modern X-ray spectroscopy, scattering, diffraction, and imaging techniques to address scientific problems of importance to society.

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.

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 Discovery of SARS-CoV-2 Main Protease Fragment-like Inhibitors with Antiviral Activity In Vitro

SARS-CoV-2 is the causative agent of COVID-19 and is responsible for the current global pandemic. The viral genome contains 5 major open reading frames of which the largest ORF1ab codes for two polyproteins, pp1ab and pp1a, which are subsequently cleaved into 16 nonstructural proteins (nsp) by two viral cysteine proteases encoded within the polyproteins. The main protease (Mpro, nsp5) cleaves the majority of the nsp's, making it essential for viral replication and has been successfully targeted for the development of antivirals. The first oral Mpro inhibitor, nirmatrelvir, was approved for treatment of COVID-19 in late December 2021 in combination with ritonavir as Paxlovid. Increasing the arsenal of antivirals and development of protease inhibitors and other antivirals with a varied mode of action remains a priority to reduce the likelihood for resistance emerging. Here, we report results from an artificial intelligence-driven approach followed by in vitro validation, allowing the identification of five fragment-like Mpro inhibitors with IC50 values ranging from 1.5 to 241 μM. The three most potent molecules (compounds 818, 737, and 183) were tested against SARS-CoV-2 by in vitro replication in Vero E6 and Calu-3 cells. Compound 818 was active in both cell models with an EC50 value comparable to its measured IC50 value. On the other hand, compounds 737 and 183 were only active in Calu-3, a preclinical model of respiratory cells, showing selective indexes twice as high as those for compound 818. We also show that our in silico methodology was successful in identifying both reversible and covalent inhibitors. For instance, compound 818 is a reversible chloromethylamide analogue of 8-methyl-γ-carboline, while compound 737 is an N-pyridyl-isatin that covalently inhibits Mpro. Given the small molecular weights of these fragments, their high binding efficiency in vitro and efficacy in blocking viral replication, these compounds represent good starting points for the development of potent lead molecules targeting the Mpro of SARS-CoV-2.

Past, Present, and Future Perspectives on Computer-Aided Drug Design Methodologies

The application of computational approaches in drug discovery has been consolidated in the last decades. These families of techniques are usually grouped under the common name of "computer-aided drug design" (CADD), and they now constitute one of the pillars in the pharmaceutical discovery pipelines in many academic and industrial environments. Their implementation has been demonstrated to tremendously improve the speed of the early discovery steps, allowing for the proficient and rational choice of proper compounds for a desired therapeutic need among the extreme vastness of the drug-like chemical space. Moreover, the application of CADD approaches allows the rationalization of biochemical and interactive processes of pharmaceutical interest at the molecular level. Because of this, computational tools are now extensively used also in the field of rational 3D design and optimization of chemical entities starting from the structural information of the targets, which can be experimentally resolved or can also be obtained with other computer-based techniques. In this work, we revised the state-of-the-art computer-aided drug design methods, focusing on their application in different scenarios of pharmaceutical and biological interest, not only highlighting their great potential and their benefits, but also discussing their actual limitations and eventual weaknesses. This work can be considered a brief overview of computational methods for drug discovery.