Pharmacophore Model for SARS-CoV-2 3CLpro Small-Molecule Inhibitors and in Vitro Experimental Validation of Computationally Screened Inhibitors

In-vitro and in-silico evaluation of rue herb for SARS-CoV-2 treatment

SARS-CoV-2, a β-coronavirus responsible for the COVID-19 pandemic, has resulted in approximately 4.9 million fatalities worldwide. Despite the urgent need, there is currently no specific therapeutic developed for treating or preventing SARS-CoV-2 infections. The virus enters the host by engaging in a molecular interaction between the viral Spike glycoprotein (S protein) and the host ACE2 receptor, facilitating membrane fusion and initiating infection. Inhibiting this interaction could impede viral activity. Therefore, this study aimed to identify natural small molecules from perennial rue herb (Ruta graveolens) as potential inhibitors against the S protein, thus preventing virus infection. Initially, a screening process was conducted on 53 compounds identified from rue herbs, utilizing pharmacophore-based virtual screening approaches. This analysis resulted in the identification of 12 hit compounds. Four compounds, namely Amentoflavone (CID: 5281600), Agathisflavone (CID: 5281599), Vitamin P (CID: 24832108), and Daphnoretin (CID: 5281406), emerged as potential S protein inhibitors through molecular docking simulations, exhibiting binding energies in kcal/mol of -9.2, -8.8, -8.2, and -8.0, respectively. ADMET analysis revealed favorable pharmacokinetics and toxicity profiles for these compounds. The compounds' stability with respect to the target S protein was evaluated using MD simulation and MM-GBSA approaches. The analysis revealed the stability of the selected compounds with the target protein. Also, PCA revealed distinctive movement patterns in four selected compounds, offered valuable insights into their functional behaviors and potential interactions. In-vitro assays revealed that rue herb extracts containing these compounds displayed potential inhibitory properties against the virus, with an IC50 value of 1.299 mg/mL and a cytotoxic concentration (CC50) value of 11.991 mg/mL. The compounds derived from rue herb, specifically Amentoflavone, Agathisflavone, Vitamin P, and Daphnoretin, show promise as candidates for the therapeutic intervention of SARS-CoV-2-related complications.

Characterization of the symmetrical benzimidazole twin drug TL1228: the role as viral entry inhibitor for fighting COVID-19

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is reliably one of the largest pandemics the world has suffered in recent years. In the search for non-biological antivirals, special emphasis was placed on drug repurposing to accelerate the clinical implementation of effective drugs.The life cycle of the virus has been extensively investigated and many human targets have been identified, such as the molecular chaperone GRP78, representing a host auxiliary factor for SARS-CoV-2 entry. Here we report the inhibitor capacity of TL1228, a small molecule discovered through an in silico screening approach, which could interfere with the interaction of SARS-CoV-2 and its target cells, blocking the recognition of the GRP78 cellular receptor by the viral Spike protein. TL1228 showed in vitro the ability to reduce significantly both pseudoviral and authentic viral activity even through the reduction of GRP78/ACE2 transcript levels. Importantly, TL1228 acts in modulating expression levels of innate immunity and as inflammation markers.

A top-down approach for studying the in-silico effect of the novel phytocompound tribulusamide B on the inhibition of Nipah virus transmission through targeting fusion glycoprotein and matrix protein

The proteins of Nipah virus ascribe to its lifecycle and are crucial to infections caused by the virus. In the absence of approved therapeutics, these proteins can be considered as drug targets. This study examined the potential of fifty-three (53) natural compounds to inhibit Nipah virus fusion glycoprotein (NiV F) and matrix protein (NiV M) in silico. The molecular docking experiment, supported by the principal component analysis (PCA), showed that out of all the phytochemicals considered, Tribulusamide B had the highest inhibitory potential against the target proteins NiV F and NiV M (-9.21 and -8.66 kcal mol-1, respectively), when compared to the control drug, Ribavirin (-7.01 and -6.52 kcal mol-1, respectively). Furthermore, it was found that Tribulusamide B pharmacophores, namely, hydrogen donors, acceptors, aromatic and hydrophobic groups, contributed towards the effective residual interactions with the target proteins. The molecular dynamic simulation further validated the results of the docking studies and concluded that Tribulusamide B formed a stable complex with the target proteins. The data obtained from MM-PBSA study further explained that the phytochemical could strongly bind with NiV F (-31.26 kJ mol-1) and NiV M (-40.26 kJ mol-1) proteins in comparison with the control drug Ribavirin (-13.12 and -13.94 kJ mol-1, respectively). Finally, the results indicated that Tribulusamide B, a common inhibitor effective against multiple proteins, can be considered a potential therapeutic entity in treating the Nipah virus infection.

3-Chymotrypsin-like Protease (3CLpro) of SARS-CoV-2: Validation as a Molecular Target, Proposal of a Novel Catalytic Mechanism, and Inhibitors in Preclinical and Clinical Trials

Proteases represent common targets in combating infectious diseases, including COVID-19. The 3-chymotrypsin-like protease (3CLpro) is a validated molecular target for COVID-19, and it is key for developing potent and selective inhibitors for inhibiting viral replication of SARS-CoV-2. In this review, we discuss structural relationships and diverse subsites of 3CLpro, shedding light on the pivotal role of dimerization and active site architecture in substrate recognition and catalysis. Our analysis of bioinformatics and other published studies motivated us to investigate a novel catalytic mechanism for the SARS-CoV-2 polyprotein cleavage by 3CLpro, centering on the triad mechanism involving His41-Cys145-Asp187 and its indispensable role in viral replication. Our hypothesis is that Asp187 may participate in modulating the pKa of the His41, in which catalytic histidine may act as an acid and/or a base in the catalytic mechanism. Recognizing Asp187 as a crucial component in the catalytic process underscores its significance as a fundamental pharmacophoric element in drug design. Next, we provide an overview of both covalent and non-covalent inhibitors, elucidating advancements in drug development observed in preclinical and clinical trials. By highlighting various chemical classes and their pharmacokinetic profiles, our review aims to guide future research directions toward the development of highly selective inhibitors, underscore the significance of 3CLpro as a validated therapeutic target, and propel the progression of drug candidates through preclinical and clinical phases.

Bioengineered amyloid peptide for rapid screening of inhibitors against main protease of SARS-CoV-2

The coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has evoked a worldwide pandemic. As the emergence of variants has hampered the neutralization capacity of currently available vaccines, developing effective antiviral therapeutics against SARS-CoV-2 and its variants becomes a significant challenge. The main protease (Mpro) of SARS-CoV-2 has received increased attention as an attractive pharmaceutical target because of its pivotal role in viral replication and proliferation. Here, we generated a de novo Mpro-inhibitor screening platform to evaluate the efficacies of Mpro inhibitors based on Mpro cleavage site-embedded amyloid peptide (MCAP)-coated gold nanoparticles (MCAP-AuNPs). We fabricated MCAPs comprising an amyloid-forming sequence and Mpro-cleavage sequence, mimicking in vivo viral replication process mediated by Mpro. By measuring the proteolytic activity of Mpro and the inhibitory efficacies of various drugs, we confirmed that the MCAP-AuNP-based platform was suitable for rapid screening potential of Mpro inhibitors. These results demonstrated that our MCAP-AuNP-based platform has great potential for discovering Mpro inhibitors and may accelerate the development of therapeutics against COVID-19.

Molecular Docking and ADME-TOX Profiling of Moringa oleifera Constituents against SARS-CoV-2

The SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2019) etiological agent, which has a high contagiousness and is to blame for the outbreak of acute viral pneumonia, is the cause of the respiratory disease COVID-19. The use of natural products grew as an alternative treatment for various diseases due to the abundance of organic molecules with pharmacological properties. Many pharmaceutical studies have focused on investigating compounds with therapeutic potential. Therefore, this study aimed to identify potential antiviral compounds from a popular medicinal plant called Moringa oleifera Lam. against the spike, Mpro, ACE2, and RBD targets of SARS-CoV-2. For this, we use molecular docking to identify the molecules with the greatest affinity for the targets through the orientation of the ligand with the receptor in complex. For the best results, ADME-TOX predictions were performed to evaluate the pharmacokinetic properties of the compounds using the online tool pkCSM. The results demonstrate that among the 61 molecules of M. oleifera, 22 molecules showed promising inhibition results, where the compound ellagic acid showed significant molecular affinity (-9.3 kcal.mol-1) in interaction with the spike protein. These results highlight the relevance of investigating natural compounds from M. oleifera as potential antivirals against SARS-CoV-2; however, additional studies are needed to confirm the antiviral activity of the compounds.

Inhibition of the predicted allosteric site of the SARS-CoV-2 main protease through flavonoids

Since its emergence in 2019, coronavirus infection (COVID-19) has become a global pandemic and killed several million people worldwide. Even though several types of vaccines are available against the COVID-19 virus, SARS-CoV-2, new strains are emerging that pose a constant danger to vaccine effectiveness. In this computational study, we identified and predicted potent allosteric inhibitors of the SARS-CoV-2 main protease (Mpro). Via molecular docking and simulations, more than 100 distinct flavonoids were docked with the allosteric site of Mpro. Docking experiments revealed four top hit compounds (Hesperidin, Schaftoside, Brickellin, and Marein) that bound strongly to the Mpro predicted allosteric site. Simulation analyses further revealed that these continually interacted with the enzyme's allosteric region throughout the simulation time. ADMET and Lipinski drug likenesses were calculated to indicate the therapeutic value of the top four hits: They were non-toxic and exhibited high human intestinal absorption concentrations. These novel allosteric site inhibitors provide a higher chance of drugging SARS-CoV2 Mpro due to the rapid mutation rate of the viral enzyme's active sites. Our findings provide a new avenue for developing novel allosteric inhibitors of SARS-CoV-2 Mpro.Communicated by Ramaswamy H. Sarma.

Evaluating Scalable Supervised Learning for Synthesize-on-Demand Chemical Libraries

Traditional small-molecule drug discovery is a time-consuming and costly endeavor. High-throughput chemical screening can only assess a tiny fraction of drug-like chemical space. The strong predictive power of modern machine-learning methods for virtual chemical screening enables training models on known active and inactive compounds and extrapolating to much larger chemical libraries. However, there has been limited experimental validation of these methods in practical applications on large commercially available or synthesize-on-demand chemical libraries. Through a prospective evaluation with the bacterial protein-protein interaction PriA-SSB, we demonstrate that ligand-based virtual screening can identify many active compounds in large commercial libraries. We use cross-validation to compare different types of supervised learning models and select a random forest (RF) classifier as the best model for this target. When predicting the activity of more than 8 million compounds from Aldrich Market Select, the RF substantially outperforms a naïve baseline based on chemical structure similarity. 48% of the RF's 701 selected compounds are active. The RF model easily scales to score one billion compounds from the synthesize-on-demand Enamine REAL database. We tested 68 chemically diverse top predictions from Enamine REAL and observed 31 hits (46%), including one with an IC50 value of 1.3 μM.

Bio-activity prediction of drug candidate compounds targeting SARS-Cov-2 using machine learning approaches

The SARS-CoV-2 3CLpro protein is one of the key therapeutic targets of interest for COVID-19 due to its critical role in viral replication, various high-quality protein crystal structures, and as a basis for computationally screening for compounds with improved inhibitory activity, bioavailability, and ADMETox properties. The ChEMBL and PubChem database contains experimental data from screening small molecules against SARS-CoV-2 3CLpro, which expands the opportunity to learn the pattern and design a computational model that can predict the potency of any drug compound against coronavirus before in-vitro and in-vivo testing. In this study, Utilizing several descriptors, we evaluated 27 machine learning classifiers. We also developed a neural network model that can correctly identify bioactive and inactive chemicals with 91% accuracy, on CheMBL data and 93% accuracy on combined data on both CheMBL and Pubchem. The F1-score for inactive and active compounds was 93% and 94%, respectively. SHAP (SHapley Additive exPlanations) on XGB classifier to find important fingerprints from the PaDEL descriptors for this task. The results indicated that the PaDEL descriptors were effective in predicting bioactivity, the proposed neural network design was efficient, and the Explanatory factor through SHAP correctly identified the important fingertips. In addition, we validated the effectiveness of our proposed model using a large dataset encompassing over 100,000 molecules. This research employed various molecular descriptors to discover the optimal one for this task. To evaluate the effectiveness of these possible medications against SARS-CoV-2, more in-vitro and in-vivo research is required.

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.

Evaluation of therapeutic potentials of selected phytochemicals against Nipah virus, a multi-dimensional in silico study

The current study attempted to evaluate the potential of fifty-three (53) natural compounds as Nipah virus attachment glycoprotein (NiV G) inhibitors through in silico molecular docking study. Pharmacophore alignment of the four (4) selected compounds (Naringin, Mulberrofuran B, Rutin and Quercetin 3-galactoside) through Principal Component Analysis (PCA) revealed that common pharmacophores, namely four H bond acceptors, one H bond donor and two aromatic groups were responsible for the residual interaction with the target protein. Out of these four compounds, Naringin was found to have the highest inhibitory potential ( - 9.19 kcal mol^-1) against the target protein NiV G, when compared to the control drug, Ribavirin ( - 6.95 kcal mol^-1). The molecular dynamic simulation revealed that Naringin could make a stable complex with the target protein in the near-native physiological condition. Finally, MM-PBSA (Molecular Mechanics-Poisson-Boltzmann Solvent-Accessible Surface Area) analysis in agreement with our molecular docking result, showed that Naringin ( - 218.664 kJ mol^-1) could strongly bind with the target protein NiV G than the control drug Ribavirin ( - 83.812 kJ mol^-1).

Supplementary Information

The online version contains supplementary material available at 10.1007/s13205-023-03595-y.

Advances in the discovery of new chemotypes through ultra-large library docking

INTRODUCTION

The size and complexity of virtual screening libraries in drug discovery have skyrocketed in recent years, reaching up to multiple billions of accessible compounds. However, virtual screening of such ultra-large libraries poses several challenges associated with preparing the libraries, sampling, and pre-selection of suitable compounds. The utilization of artificial intelligence (AI)-assisted screening approaches, such as deep learning, poses a promising countermeasure to deal with this rapidly expanding chemical space. For example, various AI-driven methods were recently successfully used to identify novel small molecule inhibitors of the SARS-CoV-2 main protease (M^pro).

AREAS COVERED

This review focuses on presenting various kinds of virtual screening methods suitable for dealing with ultra-large libraries. Challenges associated with these computational methodologies are discussed, and recent advances are highlighted in the example of the discovery of novel M^pro inhibitors targeting the SARS-CoV-2 virus.

EXPERT OPINION

With the rapid expansion of the virtual chemical space, the methodologies for docking and screening such quantities of molecules need to keep pace. Employment of AI-driven screening compounds has already been shown to be effective in a range from a few thousand to multiple billion compounds, furthered by de novo generation of drug-like molecules without human interference.

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.

Optimization of potential non-covalent inhibitors for the SARS-CoV-2 main protease inspected by a descriptor of the subpocket occupancy

The main protease is regarded as an essential drug target for treating Coronavirus Disease 2019. In the present study, 13 marketed drugs were investigated to explore the possible binding mechanism, utilizing molecular docking, molecular dynamics simulation, and MM-PB(GB)SA binding energy calculations. Our results suggest that fusidic acid, polydatin, SEN-1269, AZD6482, and UNC-2327 have high binding affinities of more than 23 kcal mol^-1. A descriptor was defined for the energetic occupancy of the subpocket, and it was found that S4 had a low occupancy of less than 10% on average. The molecular optimization of ADZ6482 via reinforcement learning algorithms was carried out to screen out three lead compounds, in which slight structural changes give more considerable binding energies and an occupancy of the S4 subpocket of up to 43%. The energetic occupancy could be a useful descriptor for evaluating the local binding affinity for drug design.

Interaction of GC376, a SARS-COV-2 MPRO inhibitor, with model lipid membranes

Partitioning and effect of antiviral GC376, a potential SARS-CoV-2 inhibitor, on model lipid membranes was studied using dynamic light scattering (DLS), UV–VIS spectrometry, Excimer fluorescence, Differential scanning calorimetry (DSC) and Small- and Wide-angle X-ray scattering (SAXS/WAXS). Partition coefficient of GC376 between lipid and water phase was found to be low, reaching K_P = 46.8 ± 18.2. Results suggest that GC376 partitions into lipid bilayers at the level of lipid head-groups, close to the polar/hydrophobic interface. Changes in structural and thermodynamic properties strongly depend on the GC376/lipid mole ratio. Already at lowest mole ratios GC376 induces increase of lateral pressures, mainly in the interfacial region of the bilayer. Hereby, the pre- and main-transition temperature of the lipid system increases, what is attributed to tighter packing of acyl chains induced by GC376. At GC376/DPPC ≥ 0.03 mol/mol we detected formation of domains with different GC376 content resulting in the lateral phase separation and changes in both, main transition temperature and enthalpy. The observed changes are attributed to the response of the system on the increased lateral stresses induced by partitioning of GC376. Obtained results are discussed in context of liposome-based drug delivery systems for GC376 and in context of indirect mechanism of virus replication inhibition.

Identification of repurposing therapeutics toward SARS-CoV-2 main protease by virtual screening

SARS-CoV-2 causes the current global pandemic coronavirus disease 2019. Widely-available effective drugs could be a critical factor in halting the pandemic. The main protease (3CL^pro) plays a vital role in viral replication; therefore, it is of great interest to find inhibitors for this enzyme. We applied the combination of virtual screening based on molecular docking derived from the crystal structure of the peptidomimetic inhibitors (N3, 13b, and 11a), and experimental verification revealed FDA-approved drugs that could inhibit the 3CL^pro of SARS-CoV-2. Three drugs were selected using the binding energy criteria and subsequently performed the 3CL^pro inhibition by enzyme-based assay. In addition, six common drugs were also chosen to study the 3CL^pro inhibition. Among these compounds, lapatinib showed high efficiency of 3CL^pro inhibition (IC₅₀ value of 35 ± 1 μM and K_i of 23 ± 1 μM). The binding behavior of lapatinib against 3CL^pro was elucidated by molecular dynamics simulations. This drug could well bind with 3CL^pro residues in the five subsites S1’, S1, S2, S3, and S4. Moreover, lapatinib’s key chemical pharmacophore features toward SAR-CoV-2 3CL^pro shared important HBD and HBA with potent peptidomimetic inhibitors. The rational design of lapatinib was subsequently carried out using the obtained results. Our discovery provides an effective repurposed drug and its newly designed analogs to inhibit SARS-CoV-2 3CL^pro.

Commercially Available Flavonols Are Better SARS-CoV-2 Inhibitors Than Isoflavone and Flavones

Despite the fast development of vaccines, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is still circulating and generating variants of concern (VoC) that escape the humoral immune response. In this context, the search for anti-SARS-CoV-2 compounds is still essential. A class of natural polyphenols known as flavonoids, frequently available in fruits and vegetables, is widely explored in the treatment of different diseases and used as a scaffold for the design of novel drugs. Therefore, herein we evaluate seven flavonoids divided into three subclasses, isoflavone (genistein), flavone (apigenin and luteolin) and flavonol (fisetin, kaempferol, myricetin, and quercetin), for COVID-19 treatment using cell-based assays and in silico calculations validated with experimental enzymatic data. The flavonols were better SARS-CoV-2 inhibitors than isoflavone and flavones. The increasing number of hydroxyl groups in ring B of the flavonols kaempferol, quercetin, and myricetin decreased the 50% effective concentration (EC₅₀) value due to their impact on the orientation of the compounds inside the target. Myricetin and fisetin appear to be preferred candidates; they are both anti-inflammatory (decreasing TNF-α levels) and inhibit SARS-CoV-2 mainly by targeting the processability of the main protease (M^pro) in a non-competitive manner, with a potency comparable to the repurposed drug atazanavir. However, fisetin and myricetin might also be considered hits that are amenable to synthetic modification to improve their anti-SARS-CoV-2 profile by inhibiting not only M^pro, but also the 3′–5′ exonuclease (ExoN).

Human Superantibodies to 3CLpro Inhibit Replication of SARS-CoV-2 across Variants

Broadly effective and safe anti-coronavirus agent is existentially needed. Major protease (3CL^pro) is a highly conserved enzyme of betacoronaviruses. The enzyme plays pivotal role in the virus replication cycle. Thus, it is a good target of a broadly effective anti-Betacoronavirus agent. In this study, human single-chain antibodies (HuscFvs) of the SARS-CoV-2 3CL^pro were generated using phage display technology. The 3CL^pro-bound phages were used to infect Escherichia coli host for the production the 3CL^pro-bound HuscFvs. Computerized simulation was used to guide the selection of the phage infected-E. coli clones that produced HuscFvs with the 3CL^pro inhibitory potential. HuscFvs of three phage infected-E. coli clones were predicted to form contact interface with residues for 3CL^pro catalytic activity, substrate binding, and homodimerization. These HuscFvs were linked to a cell-penetrating peptide to make them cell-penetrable, i.e., became superantibodies. The superantibodies blocked the 3CL^pro activity in vitro, were not toxic to human cells, traversed across membrane of 3CL^pro-expressing cells to co-localize with the intracellular 3CL^pro and most of all, they inhibited replication of authentic SARS-CoV-2 Wuhan wild type and α, β, δ, and Omicron variants that were tested. The superantibodies should be investigated further towards clinical application as a safe and broadly effective anti-Betacoronavirus agent.

Visualization of Topological Pharmacophore Space with Graph Edit Distance

A topological pharmacophore (TP) is a chemical graph-based pharmacophore representation, where nodes are pharmacophoric features (PF) and edges are topological distances between PFs. Previously proposed sparse pharmacophore graphs (SPhGs) for TPs were shown to be effective in identifying structurally different active compounds while maintaining the interpretability of the graphs. However, one limitation of using SPhGs as queries is that many structurally similar SPhGs can be identified from a set of active compounds, requiring the classification and visualization of SPhGs, followed by an understanding of the pharmacophore hypotheses. In this study, we propose a scheme for SPhG analysis based on dimensionality reduction techniques with the graph edit distance (GED) metric. This metric enables measuring similarities among SPhGs in a quantitative manner. The visualization of SPhGs, which themselves are the graphs shared by active compounds, can help us understand the pharmacophore hypotheses as well as the data set. As a proof-of-concept study, we generated two-dimensional SPhG-maps using three dimensionality reduction techniques for six biological targets. A comparison with other pharmacophore representations was also conducted. We demonstrated knowledge extraction (interpretation of the data set) from the generated maps. Our findings include a suitable mapping algorithm as well as a pharmacophore hypothesis analysis procedure using an SPhG-map.

Exploration of Ultralarge Compound Collections for Drug Discovery

Designing new medicines more cheaply and quickly is tightly linked to the quest of exploring chemical space more widely and efficiently. Chemical space is monumentally large, but recent advances in computer software and hardware have enabled researchers to navigate virtual chemical spaces containing billions of chemical structures. This review specifically concerns collections of many millions or even billions of enumerated chemical structures as well as even larger chemical spaces that are not fully enumerated. We present examples of chemical libraries and spaces and the means used to construct them, and we discuss new technologies for searching huge libraries and for searching combinatorially in chemical space. We also cover space navigation techniques and consider new approaches to de novo drug design and the impact of the "autonomous laboratory" on synthesis of designed compounds. Finally, we summarize some other challenges and opportunities for the future.

Computationally driven discovery of SARS-CoV-2 Mpro inhibitors: from design to experimental validation

We report a fast-track computationally driven discovery of new SARS-CoV-2 main protease (Mpro) inhibitors whose potency ranges from mM for the initial non-covalent ligands to sub-μM for the final covalent compound (IC50 = 830 ± 50 nM). The project extensively relied on high-resolution all-atom molecular dynamics simulations and absolute binding free energy calculations performed using the polarizable AMOEBA force field. The study is complemented by extensive adaptive sampling simulations that are used to rationalize the different ligand binding poses through the explicit reconstruction of the ligand-protein conformation space. Machine learning predictions are also performed to predict selected compound properties. While simulations extensively use high performance computing to strongly reduce the time-to-solution, they were systematically coupled to nuclear magnetic resonance experiments to drive synthesis and for in vitro characterization of compounds. Such a study highlights the power of in silico strategies that rely on structure-based approaches for drug design and allows the protein conformational multiplicity problem to be addressed. The proposed fluorinated tetrahydroquinolines open routes for further optimization of Mpro inhibitors towards low nM affinities.

Autophagy Modulators in Coronavirus Diseases: A Double Strike in Viral Burden and Inflammation

Coronaviruses are the etiologic agents of several diseases. Coronaviruses of critical medical importance are characterized by highly inflammatory pathophysiology, involving severe pulmonary impairment and infection of multiple cell types within the body. Here, we discuss the interplay between coronaviruses and autophagy regarding virus life cycle, cell resistance, and inflammation, highlighting distinct mechanisms by which autophagy restrains inflammatory responses, especially those involved in coronavirus pathogenesis. We also address different autophagy modulators available and the rationale for drug repurposing as an attractive adjunctive therapy. We focused on pharmaceuticals being tested in clinical trials with distinct mechanisms but with autophagy as a common target. These autophagy modulators act in cell resistance to virus infection and immunomodulation, providing a double-strike to prevent or treat severe disease development and death from coronaviruses diseases.

Artificial Intelligence Technologies for COVID-19 De Novo Drug Design

The recent covid crisis has provided important lessons for academia and industry regarding digital reorganization. Among the fascinating lessons from these times is the huge potential of data analytics and artificial intelligence. The crisis exponentially accelerated the adoption of analytics and artificial intelligence, and this momentum is predicted to continue into the 2020s and beyond. Drug development is a costly and time-consuming business, and only a minority of approved drugs generate returns exceeding the research and development costs. As a result, there is a huge drive to make drug discovery cheaper and faster. With modern algorithms and hardware, it is not too surprising that the new technologies of artificial intelligence and other computational simulation tools can help drug developers. In only two years of covid research, many novel molecules have been designed/identified using artificial intelligence methods with astonishing results in terms of time and effectiveness. This paper reviews the most significant research on artificial intelligence in de novo drug design for COVID-19 pharmaceutical research.

Pharmacogenetics and Precision Medicine Approaches for the Improvement of COVID-19 Therapies

Many drugs are being administered to tackle coronavirus disease 2019 (COVID-19) pandemic situations without establishing clinical effectiveness or tailoring safety. A repurposing strategy might be more effective and successful if pharmacogenetic interventions are being considered in future clinical studies/trials. Although it is very unlikely that there are almost no pharmacogenetic data for COVID-19 drugs, however, from inferring the pharmacokinetic (PK)/pharmacodynamic(PD) properties and some pharmacogenetic evidence in other diseases/clinical conditions, it is highly likely that pharmacogenetic associations are also feasible in at least some COVID-19 drugs. We strongly mandate to undertake a pharmacogenetic assessment for at least these drug-gene pairs (atazanavir-UGT1A1, ABCB1, SLCO1B1, APOA5; efavirenz-CYP2B6; nevirapine-HLA, CYP2B6, ABCB1; lopinavir-SLCO1B3, ABCC2; ribavirin-SLC28A2; tocilizumab-FCGR3A; ivermectin-ABCB1; oseltamivir-CES1, ABCB1; clopidogrel-CYP2C19, ABCB1, warfarin-CYP2C9, VKORC1; non-steroidal anti-inflammatory drugs (NSAIDs)-CYP2C9) in COVID-19 patients for advancing precision medicine. Molecular docking and computational studies are promising to achieve new therapeutics against SARS-CoV-2 infection. The current situation in the discovery of anti-SARS-CoV-2 agents at four important targets from in silico studies has been described and summarized in this review. Although natural occurring compounds from different herbs against SARS-CoV-2 infection are favorable, however, accurate experimental investigation of these compounds is warranted to provide insightful information. Moreover, clinical considerations of drug-drug interactions (DDIs) and drug-herb interactions (DHIs) of the existing repurposed drugs along with pharmacogenetic (e.g., efavirenz and CYP2B6) and herbogenetic (e.g., andrographolide and CYP2C9) interventions, collectively called multifactorial drug-gene interactions (DGIs), may further accelerate the development of precision COVID-19 therapies in the real-world clinical settings.

The current state of validated small molecules inhibiting SARS-CoV-2 nonstructural proteins

The current COVID-19 outbreak has had a profound influence on public health and daily life. Despite all restrictions and vaccination programs, COVID-19 still can lead to fatality due to a lack of COVID-19-specific treatments. A number of studies have demonstrated the feasibility to develop therapeutics by targeting underlying components of the viral proteome. Here we reviewed recently developed and validated small molecule inhibitors of SARS-CoV-2's nonstructural proteins. We described the validation level of identified compounds specific for SARS-CoV-2 in the presence of in vitro and in vivo supporting data. The mechanisms of pharmacological activity, as well as approaches for developing improved SARS-CoV-2 NSP inhibitors have been emphasized.

Potential SARS-CoV-2 3CLpro inhibitors from chromene, flavonoid and hydroxamic acid compound based on FRET assay, docking and pharmacophore studies

This present study reports some natural products and one hydroxamic acid synthetic compound which were previously reported as matrix metalloproteinase-9 (MMP-9) inhibitors to be evaluated for their inhibition toward severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) 3-chymotrypsin-like protease (3CLpro). This enzyme is one of the proteins responsible for this coronaviral replication. Two herbal methanolic extracts i.e., Averrhoa carambola leaves and Ageratum conyzoides aerial part demonstrate >50% inhibition at 1000 µg/mL. Interestingly, apigenin, one of flavonoids, demonstrates 92% inhibition at 250 µg/mL (925 µM) as well as hydroxamic acid compound, N-isobutyl-N-(4-methoxyphenylsulfonyl)glycyl hydroxamic acid (NNGH), which shows 69% inhibition at 100 µM. The in vitro results are supported by the docking studies revealing that the binding mode of both compounds is mainly by interacting with GLU166 residue in the hydrophobic pocket of the 3CLpro. Pharmacophore mapping further supported the results by confirming that the in vitro activities of both compounds are due to their pharmacophore features employing hydrogen bond acceptor (HBA), hydrogen bond donor (HBD) and hydrophobic. Gas Chromatography-Mass Spectrometry (GC-MS) analysis reported chromene compounds in Ageratum conyzoides aerial part methanolic extract are potential to be this enzyme inhibitor candidate. These all results reflect their potencies to be SARS-CoV-2 inhibitors through 3CLpro inhibition mechanism.