D3Targets-2019-nCoV: a webserver for predicting drug targets and for multi-target and multi-site based virtual screening against COVID-19

Protective effect and mechanism of Qingfei Paidu decoction on myocardial damage mediated by influenza viruses

Introduction: Significant attention has been paid to myocardial damage mediated by the single-stranded RNA virus. Qingfei Paidu decoction (QFPDD) has been proved to protect the damage caused by the influenza virus A/PR/8/1934 (PR8), but its specific mechanism is unclear. Methods: Molecular biological methods, together with network pharmacology, were used to analyze the effects and underlying mechanism of QFPDD treatment on PR8-induced myocardial damage to obtain insights into the treatment of COVID-19-mediated myocardial damage. Results: Increased apoptosis and subcellular damage were observed in myocardial cells of mice infected by PR8. QFPDD treatment significantly inhibited the apoptosis and subcellular damage induced by the PR8 virus. The inflammatory factors IFN-β, TNF-α, and IL-18 were statistically increased in the myocardia of the mice infected by PR8, and the increase in inflammatory factors was prevented by QFPDD treatment. Furthermore, the expression levels or phosphorylation of necroptosis-related proteins RIPK1, RIPK3, and MLKL were abnormally elevated in the group of infected mice, while QFPDD restored the levels or phosphorylation of these proteins. Our study demonstrated that HIF-1α is a key target of QFPDD in the treatment of influenza virus-mediated injury. The HIF-α level was significantly increased by PR8 infection. Both the knockdown of HIF-1α and treatment of the myocardial cell with QFPDD significantly reversed the increased inflammatory factors during infection. Overexpression of HIF-1α reversed the inhibition effects of QFPDD on cytokine expression. Meanwhile, seven compounds from QFPDD may target HIF-1α. Conclusion: QFPDD can ameliorate influenza virus-mediated myocardial damage by reducing the degree of cell necroptosis and apoptosis, inhibiting inflammatory response and the expression of HIF-1α. Thus, our results provide new insights into the treatment of respiratory virus-mediated myocardial damage.

Applying the digital data and the bioinformatics tools in SARS-CoV-2 research

Bioinformatics has been playing a crucial role in the scientific progress to fight against the pandemic of the coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The advances in novel algorithms, mega data technology, artificial intelligence and deep learning assisted the development of novel bioinformatics tools to analyze daily increasing SARS-CoV-2 data in the past years. These tools were applied in genomic analyses, evolutionary tracking, epidemiological analyses, protein structure interpretation, studies in virus-host interaction and clinical performance. To promote the in-silico analysis in the future, we conducted a review which summarized the databases, web services and software applied in SARS-CoV-2 research. Those digital resources applied in SARS-CoV-2 research may also potentially contribute to the research in other coronavirus and non-coronavirus viruses.

The Coming of Age of AI/ML in Drug Discovery, Development, Clinical Testing, and Manufacturing: The FDA Perspectives

Artificial intelligence (AI) and machine learning (ML) represent significant advancements in computing, building on technologies that humanity has developed over millions of years-from the abacus to quantum computers. These tools have reached a pivotal moment in their development. In 2021 alone, the U.S. Food and Drug Administration (FDA) received over 100 product registration submissions that heavily relied on AI/ML for applications such as monitoring and improving human performance in compiling dossiers. To ensure the safe and effective use of AI/ML in drug discovery and manufacturing, the FDA and numerous other U.S. federal agencies have issued continuously updated, stringent guidelines. Intriguingly, these guidelines are often generated or updated with the aid of AI/ML tools themselves. The overarching goal is to expedite drug discovery, enhance the safety profiles of existing drugs, introduce novel treatment modalities, and improve manufacturing compliance and robustness. Recent FDA publications offer an encouraging outlook on the potential of these tools, emphasizing the need for their careful deployment. This has expanded market opportunities for retraining personnel handling these technologies and enabled innovative applications in emerging therapies such as gene editing, CRISPR-Cas9, CAR-T cells, mRNA-based treatments, and personalized medicine. In summary, the maturation of AI/ML technologies is a testament to human ingenuity. Far from being autonomous entities, these are tools created by and for humans designed to solve complex problems now and in the future. This paper aims to present the status of these technologies, along with examples of their present and future applications.

D3CARP: a comprehensive platform with multiple-conformation based docking, ligand similarity search and deep learning approaches for target prediction and virtual screening

Resource- and time-consuming biological experiments are unavoidable in traditional drug discovery, which have directly driven the evolution of various computational algorithms and tools for drug-target interaction (DTI) prediction. For improving the prediction reliability, a comprehensive platform is highly expected as some previously reported webservers are small in scale, single-method, or even out of service. In this study, we integrated the multiple-conformation based docking, 2D/3D ligand similarity search and deep learning approaches to construct a comprehensive webserver, namely D3CARP, for target prediction and virtual screening. Specifically, 9352 conformations with positive control of 1970 targets were used for molecular docking, and approximately 2 million target-ligand pairs were used for 2D/3D ligand similarity search and deep learning. Besides, the positive compounds were added as references, and related diseases of therapeutic targets were annotated for further disease-based DTI study. The accuracies of the molecular docking and deep learning approaches were 0.44 and 0.89, respectively. And the average accuracy of five ligand similarity searches was 0.94. The strengths of D3CARP encompass the support for multiple computational methods, ensemble docking, utilization of positive controls as references, cross-validation of predicted outcomes, diverse disease types, and broad applicability in drug discovery. The D3CARP is freely accessible at https://www.d3pharma.com/D3CARP/index.php.

A Computational Pipeline to Identify and Characterize Binding Sites and Interacting Chemotypes in SARS-CoV-2

Minimizing the human and economic costs of the COVID-19 pandemic and future pandemics requires the ability to develop and deploy effective treatments for novel pathogens as soon as possible after they emerge. To this end, we introduce a new computational pipeline for the rapid identification and characterization of binding sites in viral proteins along with the key chemical features, which we call chemotypes, of the compounds predicted to interact with those same sites. The composition of source organisms for the structural models associated with an individual binding site is used to assess the site's degree of structural conservation across different species, including other viruses and humans. We propose a search strategy for novel therapeutics that involves the selection of molecules preferentially containing the most structurally rich chemotypes identified by our algorithm. While we demonstrate the pipeline on SARS-CoV-2, it is generalizable to any new virus, as long as either experimentally solved structures for its proteins are available or sufficiently accurate predicted structures can be constructed.

Bioactive compounds from Huashi Baidu decoction possess both antiviral and anti-inflammatory effects against COVID-19

The coronavirus disease 2019 (COVID-19) pandemic is an ongoing global health concern, and effective antiviral reagents are urgently needed. Traditional Chinese medicine theory-driven natural drug research and development (TCMT-NDRD) is a feasible method to address this issue as the traditional Chinese medicine formulae have been shown effective in the treatment of COVID-19. Huashi Baidu decoction (Q-14) is a clinically approved formula for COVID-19 therapy with antiviral and anti-inflammatory effects. Here, an integrative pharmacological strategy was applied to identify the antiviral and anti-inflammatory bioactive compounds from Q-14. Overall, a total of 343 chemical compounds were initially characterized, and 60 prototype compounds in Q-14 were subsequently traced in plasma using ultrahigh-performance liquid chromatography with quadrupole time-of-flight mass spectrometry. Among the 60 compounds, six compounds (magnolol, glycyrrhisoflavone, licoisoflavone A, emodin, echinatin, and quercetin) were identified showing a dose-dependent inhibition effect on the SARS-CoV-2 infection, including two inhibitors (echinatin and quercetin) of the main protease (Mpro), as well as two inhibitors (glycyrrhisoflavone and licoisoflavone A) of the RNA-dependent RNA polymerase (RdRp). Meanwhile, three anti-inflammatory components, including licochalcone B, echinatin, and glycyrrhisoflavone, were identified in a SARS-CoV-2-infected inflammatory cell model. In addition, glycyrrhisoflavone and licoisoflavone A also displayed strong inhibitory activities against cAMP-specific 3',5'-cyclic phosphodiesterase 4 (PDE4). Crystal structures of PDE4 in complex with glycyrrhisoflavone or licoisoflavone A were determined at resolutions of 1.54 Å and 1.65 Å, respectively, and both compounds bind in the active site of PDE4 with similar interactions. These findings will greatly stimulate the study of TCMT-NDRD against COVID-19.

Application of microfluidic technologies on COVID-19 diagnosis and drug discovery

The ongoing coronavirus disease 2019 (COVID-19) pandemic has boosted the development of antiviral research. Microfluidic technologies offer powerful platforms for diagnosis and drug discovery for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) diagnosis and drug discovery. In this review, we introduce the structure of SARS-CoV-2 and the basic knowledge of microfluidic design. We discuss the application of microfluidic devices in SARS-CoV-2 diagnosis based on detecting viral nucleic acid, antibodies, and antigens. We highlight the contribution of lab-on-a-chip to manufacturing point-of-care equipment of accurate, sensitive, low-cost, and user-friendly virus-detection devices. We then investigate the efforts in organ-on-a-chip and lipid nanoparticles (LNPs) synthesizing chips in antiviral drug screening and mRNA vaccine preparation. Microfluidic technologies contribute to the ongoing SARS-CoV-2 research efforts and provide tools for future viral outbreaks.

Antiviral Activity of N1,N3-Disubstituted Uracil Derivatives against SARS-CoV-2 Variants of Concern

Despite the widespread use of the COVID-19 vaccines, the search for effective antiviral drugs for the treatment of patients infected with SARS-CoV-2 is still relevant. Genetic variability leads to the continued circulation of new variants of concern (VOC). There is a significant decrease in the effectiveness of antibody-based therapy, which raises concerns about the development of new antiviral drugs with a high spectrum of activity against VOCs. We synthesized new analogs of uracil derivatives where uracil was substituted at the N₁ and N₃ positions. Antiviral activity was studied in Vero E6 cells against VOC, including currently widely circulating SARS-CoV-2 Omicron. All synthesized compounds of the panel showed a wide antiviral effect. In addition, we determined that these compounds inhibit the activity of recombinant SARS-CoV-2 RdRp. Our study suggests that these non-nucleoside uracil-based analogs may be of future use as a treatment for patients infected with circulating SARS-CoV-2 variants.

Structures of the SARS-CoV-2 spike glycoprotein and applications for novel drug development

COVID-19 caused by SARS-CoV-2 has raised a health crisis worldwide. The high morbidity and mortality associated with COVID-19 and the lack of effective drugs or vaccines for SARS-CoV-2 emphasize the urgent need for standard treatment and prophylaxis of COVID-19. The receptor-binding domain (RBD) of the glycosylated spike protein (S protein) is capable of binding to human angiotensin-converting enzyme 2 (hACE2) and initiating membrane fusion and virus entry. Hence, it is rational to inhibit the RBD activity of the S protein by blocking the RBD interaction with hACE2, which makes the glycosylated S protein a potential target for designing and developing antiviral agents. In this study, the molecular features of the S protein of SARS-CoV-2 are highlighted, such as the structures, functions, and interactions of the S protein and ACE2. Additionally, computational tools developed for the treatment of COVID-19 are provided, for example, algorithms, databases, and relevant programs. Finally, recent advances in the novel development of antivirals against the S protein are summarized, including screening of natural products, drug repurposing and rational design. This study is expected to provide novel insights for the efficient discovery of promising drug candidates against the S protein and contribute to the development of broad-spectrum anti-coronavirus drugs to fight against SARS-CoV-2.

Understanding gilteritinib resistance to FLT3-F691L mutation through an integrated computational strategy

FMS-like tyrosine kinase 3 (FLT3) serves as an important drug target for acute myeloid leukemia (AML), and gene mutations of FLT3 have been closely associated with AML patients with an incidence rate of ~ 30%. However, the mechanism of the clinically relevant F691L gatekeeper mutation conferred resistance to the drug gilteritinib remained poorly understood. In this study, multiple microsecond molecular dynamics (MD) simulations, end-point free energy calculations, and dynamic correlated and network analyses were performed to investigate the molecular basis of gilteritinib resistance to the FLT3-F691L mutation. The simulations revealed that the resistant mutation largely induced the conformational changes of the activation loop (A-loop), the phosphate-binding loop, and the helix αC of the FLT3 protein. The binding abilities of the gilteritinib to the wild-type and the F691L mutant were different through the binding free energy prediction. The simulation results further indicated that the driving force to determine the binding affinity of gilteritinib was derived from the differences in the energy terms of electrostatic and van der Waals interactions. Moreover, the per-residue free energy decomposition suggested that the four residues (Phe803, Gly831, Leu832, and Ala833) located at the A-loop of FLT3 had a significant impact on the binding affinity of gilteritinib to the F691L mutant. This study may provide useful information for the design of novel FLT3 inhibitors specially targeting the F691L gatekeeper mutant.

The Pharmacological Mechanism of Xiyanping Injection for the Treatment of Novel Coronavirus Pneumonia (COVID-19): Based on Network Pharmacology Strategy

Purpose

The possible mechanism of Xiyanping injection treatment COVID-19 is discussed through the network pharmacology.

Methods

Obtaining the chemical structure of Xiyanping injection through the patent application and obtaining control compounds I, II, III, IV, V, Yanhuning injection (VI, VII), Chuanhuning injection (VIII, IX), 10 compounds were analyzed by D3Targets-2019-nCoV. The human anti-COVID-19 gene in COVID-19 DisGeNET was intersected with the CTD Andrographolide target gene and then combined with D3Targets-2019-nCoV, resulting in 93 genes, using the Venny 2.1 platform. The PPI network was constructed by the String platform and Cytoscape 3.8.2 platform. The GO, KEGG, and tissue of the target were analyzed using the Metascape platform and DAVID platform. The gene expression in the respiratory system was analyzed using the ePlant platform. The CB-Dock is used for the docking verification and degree values of the first 20 genes.

Results

Finally, 1599 GO and 291 KEGG results were obtained. GO is mostly associated with the cell stress response to chemicals, the cell response to oxidative stress, and the cell response to reactive oxygen species. In total, 218 KEGG pathway concentrations were related to infection and other diseases and 73 signaling pathways mostly related to inflammation and immune pathways, such as TNF signaling pathway and MAPK signaling pathway. The molecular docking results show that Xiyanping injection, compound III, has a good docking relationship with 20 target proteins such as HSP90AA1. Tissue has 22 genes that are pooled in the lungs.

Conclusion

Xiyanping injection may inhibit the release of various inflammatory factors by inhibiting intracellular pathways such as MAPK and TNF. It acts on protein targets such as HSP90AA1 and plays a potential therapeutic role in COVID-19. Thus, compound III may be treated as a potential new drug for the treatment of COVID-19 and the Xiyanping injection may treat patients with COVID-19 infection.

SuperPred 3.0: drug classification and target prediction-a machine learning approach

Since the last published update in 2014, the SuperPred webserver has been continuously developed to offer state-of-the-art models for drug classification according to ATC classes and target prediction. For the first time, a thoroughly filtered ATC dataset, that is suitable for accurate predictions, is provided along with detailed information on the achieved predictions. This aims to overcome the challenges in comparing different published prediction methods, since performance can vary greatly depending on the training dataset used. Additionally, both ATC and target prediction have been reworked and are now based on machine learning models instead of overall structural similarity, stressing the importance of functional groups for the mechanism of action of small molecule substances. Additionally, the dataset for the target prediction has been extensively filtered and is no longer only based on confirmed binders but also includes non-binding substances to reduce false positives. Using these methods, accuracy for the ATC prediction could be increased by almost 5% to 80.5% compared to the previous version, and additionally the scoring function now offers values which are easily assessable at first glance. SuperPred 3.0 is publicly available without the need for registration at: https://prediction.charite.de/index.php.

D3AI-CoV: a deep learning platform for predicting drug targets and for virtual screening against COVID-19

Target prediction and virtual screening are two powerful tools of computer-aided drug design. Target identification is of great significance for hit discovery, lead optimization, drug repurposing and elucidation of the mechanism. Virtual screening can improve the hit rate of drug screening to shorten the cycle of drug discovery and development. Therefore, target prediction and virtual screening are of great importance for developing highly effective drugs against COVID-19. Here we present D3AI-CoV, a platform for target prediction and virtual screening for the discovery of anti-COVID-19 drugs. The platform is composed of three newly developed deep learning-based models i.e., MultiDTI, MPNNs-CNN and MPNNs-CNN-R models. To compare the predictive performance of D3AI-CoV with other methods, an external test set, named Test-78, was prepared, which consists of 39 newly published independent active compounds and 39 inactive compounds from DrugBank. For target prediction, the areas under the receiver operating characteristic curves (AUCs) of MultiDTI and MPNNs-CNN models are 0.93 and 0.91, respectively, whereas the AUCs of the other reported approaches range from 0.51 to 0.74. For virtual screening, the hit rate of D3AI-CoV is also better than other methods. D3AI-CoV is available for free as a web application at http://www.d3pharma.com/D3Targets-2019-nCoV/D3AI-CoV/index.php, which can serve as a rapid online tool for predicting potential targets for active compounds and for identifying active molecules against a specific target protein for COVID-19 treatment.

Repurposing FDA-approved Drugs Targeting SARS-CoV2 3CLpro: a study by applying Virtual Screening, Molecular Dynamics, MM-PBSA Calculations and Covalent Docking

Background:

Since the end of 2019, the etiologic agent SAR-CoV-2 responsible for one of the most significant epidemics in history has caused severe global economic, social, and health damages. The drug repurposing approach and application of Structure-based Drug Discovery (SBDD) using in silico techniques are increasingly frequent, leading to the identification of several molecules that may represent promising potential.

Method:

In this context, here we use in silico methods of virtual screening (VS), pharmacophore modeling (PM), and fragment-based drug design (FBDD), in addition to molecular dynamics (MD), molecular mechanics/Poisson-Boltzmann surface area (MM -PBSA) calculations, and covalent docking (CD) for the identification of potential treatments against SARS-CoV-2. We initially validated the docking protocol followed by VS in 1,613 FDA-approved drugs obtained from the ZINC database. Thus, we identified 15 top hits, of which three of them were selected for further simulations. In parallel, for the compounds with a fit score value ≤ of 30, we performed the FBDD protocol, where we designed 12 compounds

Result:

By applying a PM protocol in the ZINC database, we identified three promising drug candidates. Then, the 9 top hits were evaluated in simulations of MD, MM-PBSA, and CD. Subsequently, MD showed that all identified hits showed stability at the active site without significant changes in the protein's structural integrity, as evidenced by the RMSD, RMSF, Rg, SASA graphics. They also showed interactions with the catalytic dyad (His41 and Cys145) and other essential residues for activity (Glu166 and Gln189) and high affinity for MM-PBSA, with possible covalent inhibition mechanism.

Conclution:

Finally, our protocol helped identify potential compounds wherein ZINC896717 (Zafirlukast), ZINC1546066 (Erlotinib), and ZINC1554274 (Rilpivirine) were more promising and could be explored in vitro, in vivo, and clinical trials to prove their potential as antiviral agents.

Databases, DrugBank, and virtual screening platforms for therapeutic development

The upsurge of the severe acute respiratory syndrome-Coronavirus-2 (SARS-CoV-2) has turned into a global health disaster. Many remodeled medications were suggested for treatment in the early stages of this pandemic, but these dosages afterward came across with distinct offshoots. Thus, these consequences compelled the scientists to develop new drugs using various antiviral, antiinflammatory, antibacterial, and phytochemical compounds. A handful of drugs have been scrutinized in silico, in vitro, plus through human trials such as anti-SARS-CoV-2 agents and made available as various databases by various scientific communities. The SARS-CoV-2 pandemic databases are designed to allay difficulties associated with this scenario. Some of the popular databases are GESS (global evaluation of SARS-CoV-2/HCoV-19 sequences) which gives a thorough study of data based on tenfold of thousands of complete coverage and quality of SARS-CoV-2 genomes, CORona Drug InTERactions (CORDITE) database for SARS-CoV-2 which profoundly combines the understanding of potential drugs and make it available for scientists and medicos. SARSCOVIDB set one’s sights to merge all differential gene expression data, at mRNA and protein levels, helping to accelerate analysis and research on the molecular impact of covid-19. This chapter aims to provide a piece of complete information about the SARS-CoV-2 virus databases, potentially available drugs, and virtual screening methods. And also provides a different webserver to reach out for information related to the COVID-19 pandemic and its future.

The chemical constituents of Sterculia comosa (wall) Roxb woods for arginase inhibitory, antioxidant activity, and molecular docking against SARS CoV-2 protein

Flavonoids and phenols have an arginase inhibitory and antioxidant activity. The Sterculia genus has phenols and flavonoids content. This study aimed to investigate the arginase inhibitory and antioxidant activity of the chemical constituent of Sterculia comosa (wall) Roxb and also their binding affinities to arginase. The most active extract was methanol extract. This active extract was determined for its arginase inhibitory and antioxidant activity, determined the total phenols and total flavonoids, and identified chemical compound. The methanol extract has IC50 2.787 μg/ml for arginase inhibitory activity and IC50 4,199 μg/ml for DPPH scavenging activity. The total phenols 723.61 mg GAE/gr, total flavonoids content 28.96 mg QE/gr extract. The chemical constituent: KC4.4.6 ((-)-2-(E)-caffeoyl-D-glyceric acid) and KC4.4.5.1 (trans-isoferulic acid) have an arginase inhibitory activity KC4.4.6: 98,03 μg/ml and KC4.4.5.1: 292,58 μg/ml. Antioxidant activity with DPPH methods KC4.4.6: 48,77 μg/ml and KC4.4.5.1: 88,08 μg/ml. Antioxidant by FRAP methods KC4.4.6: 16,4 FeEAC mol/g and KC4.4.5.1: 15,79 FeEAC mol/g. The isolate trans-isoferulic acid predicted has good interaction to arginase. Isolate KC4.4.6. Predicted has good interaction to PLPro of SARS CoV-2 PLpro. However, both isolates did not show good interaction to 3CLPro, nsp12, and Spike protein of SARS CoV-2.

Deciphering the Mechanism of Gilteritinib Overcoming Lorlatinib Resistance to the Double Mutant I1171N/F1174I in Anaplastic Lymphoma Kinase

Anaplastic lymphoma kinase (ALK) is validated as a therapeutic molecular target in multiple malignancies, such as non-small cell lung cancer (NSCLC). However, the feasibility of targeted therapies exerted by ALK inhibitors is inevitably hindered owing to drug resistance. The emergence of clinically acquired drug mutations has become a major challenge to targeted therapies and personalized medicines. Thus, elucidating the mechanism of resistance to ALK inhibitors is helpful for providing new therapeutic strategies for the design of next-generation drug. Here, we used molecular docking and multiple molecular dynamics simulations combined with correlated and energetical analyses to explore the mechanism of how gilteritinib overcomes lorlatinib resistance to the double mutant ALK I1171N/F1174I. We found that the conformational dynamics of the ALK kinase domain was reduced by the double mutations I1171N/F1174I. Moreover, energetical and structural analyses implied that the double mutations largely disturbed the conserved hydrogen bonding interactions from the hinge residues Glu1197 and Met1199 in the lorlatinib-bound state, whereas they had no discernible adverse impact on the binding affinity and stability of gilteritinib-bound state. These discrepancies created the capacity of the double mutant ALK I1171N/F1174I to confer drug resistance to lorlatinib. Our result anticipates to provide a mechanistic insight into the mechanism of drug resistance induced by ALK I1171N/F1174I that are resistant to lorlatinib treatment in NSCLC.

Comprehensive analyses of bioinformatics applications in the fight against COVID-19 pandemic

Novel coronavirus disease 2019 (COVID-19) is a global pandemic caused by severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2), which can be transmitted from person to person. As of September 21, 2021, over 228 million cases were diagnosed as COVID-19 infection in more than 200 countries and regions worldwide. The death toll is more than 4.69 million and the mortality rate has reached about 2.05% as it has gradually become a global plague, and the numbers are growing. Therefore, it is important to gain a deeper understanding of the genome and protein characteristics, clinical diagnostics, pathogenic mechanisms, and the development of antiviral drugs and vaccines against the novel coronavirus to deal with the COVID-19 pandemic. The traditional biology technologies are limited for COVID-19-related studies to understand the pandemic happening. Bioinformatics is the application of computational methods and analytical tools in the field of biological research which has obvious advantages in predicting the structure, product, function, and evolution of unknown genes and proteins, and in screening drugs and vaccines from a large amount of sequence information. Here, we comprehensively summarized several of the most important methods and applications relating to COVID-19 based on currently available reports of bioinformatics technologies, focusing on future research for overcoming the virus pandemic. Based on the next-generation sequencing (NGS) and third-generation sequencing (TGS) technology, not only virus can be detected, but also high quality SARS-CoV-2 genome could be obtained quickly. The emergence of data of genome sequences, variants, haplotypes of SARS-CoV-2 help us to understand genome and protein structure, variant calling, mutation, and other biological characteristics. After sequencing alignment and phylogenetic analysis, the bat may be the natural host of the novel coronavirus. Single-cell RNA sequencing provide abundant resource for discovering the mechanism of immune response induced by COVID-19. As an entry receptor, angiotensin-converting enzyme 2 (ACE2) can be used as a potential drug target to treat COVID-19. Molecular dynamics simulation, molecular docking and artificial intelligence (AI) technology of bioinformatics methods based on drug databases for SARS-CoV-2 can accelerate the development of drugs. Meanwhile, computational approaches are helpful to identify suitable vaccines to prevent COVID-19 infection through reverse vaccinology, Immunoinformatics and structural vaccinology.

Drug repurposing for COVID-19: Approaches, challenges and promising candidates

Traditional drug development and discovery has not kept pace with threats from emerging and re-emerging diseases such as Ebola virus, MERS-CoV and more recently, SARS-CoV-2. Among other reasons, the exorbitant costs, high attrition rate and extensive periods of time from research to market approval are the primary contributing factors to the lag in recent traditional drug developmental activities. Due to these reasons, drug developers are starting to consider drug repurposing (or repositioning) as a viable alternative to the more traditional drug development process. Drug repurposing aims to find alternative uses of an approved or investigational drug outside of its original indication. The key advantages of this approach are that there is less developmental risk, and it is less time-consuming since the safety and pharmacological profile of the repurposed drug is already established. To that end, various approaches to drug repurposing are employed. Computational approaches make use of machine learning and algorithms to model disease and drug interaction, while experimental approaches involve a more traditional wet-lab experiments. This review would discuss in detail various ongoing drug repurposing strategies and approaches to combat the current COVID-19 pandemic, along with the advantages and the potential challenges.

BioNet: a large-scale and heterogeneous biological network model for interaction prediction with graph convolution

Motivation

Understanding chemical–gene interactions (CGIs) is crucial for screening drugs. Wet experiments are usually costly and laborious, which limits relevant studies to a small scale. On the contrary, computational studies enable efficient in-silico exploration. For the CGI prediction problem, a common method is to perform systematic analyses on a heterogeneous network involving various biomedical entities. Recently, graph neural networks become popular in the field of relation prediction. However, the inherent heterogeneous complexity of biological interaction networks and the massive amount of data pose enormous challenges. This paper aims to develop a data-driven model that is capable of learning latent information from the interaction network and making correct predictions.

Results

We developed BioNet, a deep biological networkmodel with a graph encoder–decoder architecture. The graph encoder utilizes graph convolution to learn latent information embedded in complex interactions among chemicals, genes, diseases and biological pathways. The learning process is featured by two consecutive steps. Then, embedded information learnt by the encoder is then employed to make multi-type interaction predictions between chemicals and genes with a tensor decomposition decoder based on the RESCAL algorithm. BioNet includes 79 325 entities as nodes, and 34 005 501 relations as edges. To train such a massive deep graph model, BioNet introduces a parallel training algorithm utilizing multiple Graphics Processing Unit (GPUs). The evaluation experiments indicated that BioNet exhibits outstanding prediction performance with a best area under Receiver Operating Characteristic (ROC) curve of 0.952, which significantly surpasses state-of-theart methods. For further validation, top predicted CGIs of cancer and COVID-19 by BioNet were verified by external curated data and published literature.

How does nintedanib overcome cancer drug-resistant mutation of RET protein-tyrosine kinase: insights from molecular dynamics simulations

Targeted drug therapies represent a therapeutic breakthrough in the treatment of human cancer. However, the emergence of acquired resistance inevitably compromises therapeutic drugs. Rearranged during transfection (RET) proto-oncogene, which encodes a receptor tyrosine kinase, is a target for several kinds of human cancer such as thyroid, breast, and colorectal carcinoma. A single mutation L881V at the RET kinase domain was found in familial medullary thyroid carcinoma. Nintedanib can effectively inhibit the RET L881V mutant, whereas its analog compound 1 is unable to combat this mutant. However, the underlying mechanism was still unexplored. Here, molecular dynamics (MD) simulations, binding free energy calculations, and structural analysis were performed to uncover the mechanism of overcoming the resistance of RET L881V mutant to nintedanib. Energetic analysis revealed that the L881V mutant remained sensitive to the treatment of nintedanib, whereas it was insensitive to the compound 1. Structural analysis further showed that the distribution of K758, D892, and N879 network had a detrimental effect on the binding of compound 1 to the L881V mutant. The obtained results may provide insight into the mechanism of overcoming resistance in the RET kinase.

SAS: A Platform of Spike Antigenicity for SARS-CoV-2

Since the outbreak of SARS-CoV-2, antigenicity concerns continue to linger with emerging mutants. As recent variants have shown decreased reactivity to previously determined monoclonal antibodies (mAbs) or sera, monitoring the antigenicity change of circulating mutants is urgently needed for vaccine effectiveness. Currently, antigenic comparison is mainly carried out by immuno-binding assays. Yet, an online predicting system is highly desirable to complement the targeted experimental tests from the perspective of time and cost. Here, we provided a platform of SAS (Spike protein Antigenicity for SARS-CoV-2), enabling predicting the resistant effect of emerging variants and the dynamic coverage of SARS-CoV-2 antibodies among circulating strains. When being compared to experimental results, SAS prediction obtained the consistency of 100% on 8 mAb-binding tests with detailed epitope covering mutational sites, and 80.3% on 223 anti-serum tests. Moreover, on the latest South Africa escaping strain (B.1.351), SAS predicted a significant resistance to reference strain at multiple mutated epitopes, agreeing well with the vaccine evaluation results. SAS enables auto-updating from GISAID, and the current version collects 867K GISAID strains, 15.4K unique spike (S) variants, and 28 validated and predicted epitope regions that include 339 antigenic sites. Together with the targeted immune-binding experiments, SAS may be helpful to reduce the experimental searching space, indicate the emergence and expansion of antigenic variants, and suggest the dynamic coverage of representative mAbs/vaccines among the latest circulating strains. SAS can be accessed at https://www.biosino.org/sas.

Recent progress in the development of potential drugs against SARS-CoV-2

SARS-CoV-2, a newly emerged and highly pathogenic coronavirus, is identified as the causal agent of Coronavirus Disease (2019) (COVID-19) in the late December 2019, in China. The virus has rapidly spread nationwide and spilled over to the other countries around the globe, resulting in more than 120 million infections and 2.6 million deaths until the time of this review. Unfortunately, there are still no specific drugs available against this disease, and it is very necessary to call upon more scientists to work together to stop a further spread. Hence, the recent progress in the development of drugs may help scientific community quickly understand current research status and further develop new effective drugs. Herein, we summarize the cellular entry and replication process of this virus and discuss the recent development of potential viral based drugs that target bio-macromolecules in different stages of the viral life cycle, especially S protein, 3CLPro, PLPro, RdRp and helicase.

Drug repurposing in COVID-19: A review with past, present and future

The coronavirus SARS-CoV-2 which causes the COVID-19 disease is a global public health emergency. Coronavirus are single-stranded positive-sense RNA viruses and their genome size is approximately 30 kb, which encodes some important structural proteins. The interaction between viral Spike protein and ACE2 on the host cell surface is of significant interest since it initiates the infection process. This review will focus on the effectiveness of reuse of currently used drugs against COVID-19, including clinical trials, molecular docking, and computational modelling approach.

Methods

A systematic search in Pubmed, MEDLINE, EMBASE was conducted from from January 2020 to July 2021.

Applying computational, clinical and experimental approaches, numerous drugs such as remdesivir, favipiravir, ribavirin, lopinavir, ritonavir, tocilizumab have been repurposed and have shown promising protection against SARS-CoV2 both in vitro and in clinical conditions. Although there is only one repurposed drug approved by the U.S. Food and Drug Administration (FDA) to treat coronavirus disease 2019 (COVID-19), i.e, Remdesivir. However, the FDA withdrew the authorization of the drugs Hydroxychloroquine and chloroquine,that are not effective for COVID-19 and can also cause serious heart problems. Molecular coupling would be the ideal technique to identify such therapeutic agents against COVID19.

Molecular Dynamics Simulations Reveal the Interaction Fingerprint of Remdesivir Triphosphate Pivotal in Allosteric Regulation of SARS-CoV-2 RdRp

The COVID-19 pandemic has now strengthened its hold on human health and coronavirus' lethal existence does not seem to be going away soon. In this regard, the optimization of reported information for understanding the mechanistic insights that facilitate the discovery towards new therapeutics is an unmet need. Remdesivir (RDV) is established to inhibit RNA-dependent RNA polymerase (RdRp) in distinct viral families including Ebola and SARS-CoV-2. Therefore, its derivatives have the potential to become a broad-spectrum antiviral agent effective against many other RNA viruses. In this study, we performed comparative analysis of RDV, RMP (RDV monophosphate), and RTP (RDV triphosphate) to undermine the inhibition mechanism caused by RTP as it is a metabolically active form of RDV. The MD results indicated that RTP rearranges itself from its initial RMP-pose at the catalytic site towards NTP entry site, however, RMP stays at the catalytic site. The thermodynamic profiling and free-energy analysis revealed that a stable pose of RTP at NTP entrance site seems critical to modulate the inhibition as its binding strength improved more than its initial RMP-pose obtained from docking at the catalytic site. We found that RTP not only occupies the residues K545, R553, and R555, essential to escorting NTP towards the catalytic site, but also interacts with other residues D618, P620, K621, R624, K798, and R836 that contribute significantly to its stability. From the interaction fingerprinting it is revealed that the RTP interact with basic and conserved residues that are detrimental for the RdRp activity, therefore it possibly perturbed the catalytic site and blocked the NTP entrance site considerably. Overall, we are highlighting the RTP binding pose and key residues that render the SARS-CoV-2 RdRp inactive, paving crucial insights towards the discovery of potent inhibitors.

Identification of Potential Inhibitors against Epstein-Barr Virus Nuclear Antigen 1 (EBNA1): An Insight from Docking and Molecular Dynamic Simulations

Epstein-Barr virus (EBV), a known tumorigenic virus, is associated with various neuropathies, including multiple sclerosis (MS). However, there is no anti-EBV FDA-approved drug available in the market. Our study targeted EBV protein EBV nuclear antigen 1 (EBNA1), crucial in virus replication and expressed in all the stages of viral latencies. This dimeric protein binds to an 18 bp palindromic DNA sequence and initiates the process of viral replication. We chose phytochemicals and FDA-approved MS drugs based on literature survey followed by their evaluation efficacies as anti-EBNA1 molecules. Molecular docking revealed FDA drugs ozanimod, siponimod, teriflunomide, and phytochemicals; emodin; protoapigenone; and EGCG bound to EBNA1 with high affinities. ADMET and Lipinski's rule analysis of the phytochemicals predicted favorable druggability. We supported our assessments of pocket druggability with molecular dynamics simulations and binding affinity predictions by the molecular mechanics generalized Born surface area (MM/GBSA) method. Our results establish a stable binding for siponimod and ozanimod with EBNA1 mainly via van der Waals interactions. We identified hot spot residues like I481', K477', L582', and K586' in the binding of ligands. In particular, K477' at the amino terminal of EBNA1 is known to establish interaction with two bases at the major groove of the DNA. Siponimod bound to EBNA1 engaging K477', thus plausibly making it unavailable for DNA interaction. Computational alanine scanning further supported the significant roles of K477', I481', and K586' in the binding of ligands with EBNA1. Conclusively, the compounds showed promising results to be used against EBNA1.

Molecular Modeling Targeting Transmembrane Serine Protease 2 (TMPRSS2) as an Alternative Drug Target Against Coronaviruses

Since November 2019, the new Coronavirus disease (COVID-19) caused by the etiological agent SARS-CoV-2 has been responsible for several cases worldwide, becoming pandemic in March 2020. Pharmaceutical industries and academics have joined their efforts to discover new therapies to control the disease, since there are no specific drugs to combat this emerging virus. Thus, several targets have been explored, among them the transmembrane protease serine 2 (TMPRSS2) has gained greater interest in the scientific community. In this context, this review will describe the importance of TMPRSS2 protease and the significant advances in virtual screening focused on discovering new inhibitors. In this review, it was observed that molecular modeling methods could be powerful tools in identifying new molecules against SARS-CoV-2. Thus, this review could be used to guide researchers worldwide to explore the biological and clinical potential of compounds that could be promising drug candidates against SARS-CoV-2, acting by inhibition of TMPRSS2 protein.

Tracking the pipeline: immunoinformatics and the COVID-19 vaccine design

With the onset of the COVID-19 pandemic, the amount of data on genomic and proteomic sequences of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) stored in various databases has exponentially grown. A large volume of these data has led to the production of equally immense sets of immunological data, which require rigorous computational approaches to sort through and make sense of. Immunoinformatics has emerged in the recent decades as a field capable of offering this approach by bridging experimental and theoretical immunology with state-of-the-art computational tools. Here, we discuss how immunoinformatics can assist in the development of high-performance vaccines and drug discovery needed to curb the spread of SARS-CoV-2. Immunoinformatics can provide a set of computational tools to extract meaningful connections from the large sets of COVID-19 patient data, which can be implemented in the design of effective vaccines. With this in mind, we represent a pipeline to identify the role of immunoinformatics in COVID-19 treatment and vaccine development. In this process, a number of free databases of protein sequences, structures and mutations are introduced, along with docking web servers for assessing the interaction between antibodies and the SARS-CoV-2 spike protein segments as most commonly considered antigens in vaccine design.

Finding Druggable Sites in Proteins Using TACTICS

Structure-based drug discovery efforts require knowledge of where drug-binding sites are located on target proteins. To address the challenge of finding druggable sites, we developed a machine-learning algorithm called TACTICS (trajectory-based analysis of conformations to identify cryptic sites), which uses an ensemble of molecular structures (such as molecular dynamics simulation data) as input. First, TACTICS uses k-means clustering to select a small number of conformations that represent the overall conformational heterogeneity of the data. Then, TACTICS uses a random forest model to identify potentially bindable residues in each selected conformation, based on protein motion and geometry. Lastly, residues in possible binding pockets are scored using fragment docking. As proof-of-principle, TACTICS was applied to the analysis of simulations of the SARS-CoV-2 main protease and methyltransferase and the Yersinia pestis aryl carrier protein. Our approach recapitulates known small-molecule binding sites and predicts the locations of sites not previously observed in experimentally determined structures. The TACTICS code is available at https://github.com/Albert-Lau-Lab/tactics_protein_analysis.

Systematic review on role of structure based drug design (SBDD) in the identification of anti-viral leads against SARS-Cov-2

The outbreak of existing public health distress is threatening the entire world with emergence and rapid spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The novel coronavirus disease 2019 (COVID-19) is mild in most people. However, in some elderly people with co-morbid conditions, it may progress to pneumonia, acute respiratory distress syndrome (ARDS) and multi organ dysfunction leading to death. COVID-19 has caused global panic in the healthcare sector and has become one of the biggest threats to the global economy. Drug discovery researchers are expected to contribute rapidly than ever before. The complete genome sequence of coronavirus had been reported barely a month after the identification of first patient. Potential drug targets to combat and treat the coronavirus infection have also been explored. The iterative structure-based drug design (SBDD) approach could significantly contribute towards the discovery of new drug like molecules for the treatment of COVID-19. The existing antivirals and experiences gained from SARS and MERS outbreaks may pave way for identification of potential drug molecules using the approach. SBDD has gained momentum as the essential tool for faster and costeffective lead discovery of antivirals in the past. The discovery of FDA approved human immunodeficiency virus type 1 (HIV-1) inhibitors represent the foremost success of SBDD. This systematic review provides an overview of the novel coronavirus, its pathology of replication, role of structure based drug design, available drug targets and recent advances in in-silico drug discovery for the prevention of COVID-19. SARSCoV- 2 main protease, RNA dependent RNA polymerase (RdRp) and spike (S) protein are the potential targets, which are currently explored for the drug development.

A review on drug repurposing applicable to COVID-19

Drug repurposing involves the identification of new applications for existing drugs at a lower cost and in a shorter time. There are different computational drug-repurposing strategies and some of these approaches have been applied to the coronavirus disease 2019 (COVID-19) pandemic. Computational drug-repositioning approaches applied to COVID-19 can be broadly categorized into (i) network-based models, (ii) structure-based approaches and (iii) artificial intelligence (AI) approaches. Network-based approaches are divided into two categories: network-based clustering approaches and network-based propagation approaches. Both of them allowed to annotate some important patterns, to identify proteins that are functionally associated with COVID-19 and to discover novel drug–disease or drug–target relationships useful for new therapies. Structure-based approaches allowed to identify small chemical compounds able to bind macromolecular targets to evaluate how a chemical compound can interact with the biological counterpart, trying to find new applications for existing drugs. AI-based networks appear, at the moment, less relevant since they need more data for their application.

Ligand-based approach for predicting drug targets and for virtual screening against COVID-19

Discovering efficient drugs and identifying target proteins are still an unmet but urgent need for curing coronavirus disease 2019 (COVID-19). Protein structure-based docking is a widely applied approach for discovering active compounds against drug targets and for predicting potential targets of active compounds. However, this approach has its inherent deficiency caused by e.g. various different conformations with largely varied binding pockets adopted by proteins, or the lack of true target proteins in the database. This deficiency may result in false negative results. As a complementary approach to the protein structure-based platform for COVID-19, termed as D3Docking in our previous work, we developed in this study a ligand-based method, named D3Similarity, which is based on the molecular similarity evaluation between the submitted molecule(s) and those in an active compound database. The database is constituted by all the reported bioactive molecules against the coronaviruses, viz., severe acute respiratory syndrome coronavirus (SARS), Middle East respiratory syndrome coronavirus (MERS), severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), human betacoronavirus 2c EMC/2012 (HCoV-EMC), human CoV 229E (HCoV-229E) and feline infectious peritonitis virus (FIPV), some of which have target or mechanism information but some do not. Based on the two-dimensional (2D) and three-dimensional (3D) similarity evaluation of molecular structures, virtual screening and target prediction could be performed according to similarity ranking results. With two examples, we demonstrated the reliability and efficiency of D3Similarity by using 2D × 3D value as score for drug discovery and target prediction against COVID-19. The database, which will be updated regularly, is available free of charge at https://www.d3pharma.com/D3Targets-2019-nCoV/D3Similarity/index.php.

Identifying the natural polyphenol catechin as a multi-targeted agent against SARS-CoV-2 for the plausible therapy of COVID-19: an integrated computational approach

The global pandemic crisis, coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has claimed the lives of millions of people across the world. Development and testing of anti-SARS-CoV-2 drugs or vaccines have not turned to be realistic within the timeframe needed to combat this pandemic. Here, we report a comprehensive computational approach to identify the multi-targeted drug molecules against the SARS-CoV-2 proteins, whichare crucially involved in the viral-host interaction, replication of the virus inside the host, disease progression and transmission of coronavirus infection. Virtual screening of 75 FDA-approved potential antiviral drugs against the target proteins, spike (S) glycoprotein, human angiotensin-converting enzyme 2 (hACE2), 3-chymotrypsin-like cysteine protease (3CLpro), cathepsin L (CTSL), nucleocapsid protein, RNA-dependent RNA polymerase (RdRp) and non-structural protein 6 (NSP6), resulted in the selection of seven drugs which preferentially bind to the target proteins. Further, the molecular interactions determined by molecular dynamics simulation revealed that among the 75 drug molecules, catechin can effectively bind to 3CLpro, CTSL, RBD of S protein, NSP6 and nucleocapsid protein. It is more conveniently involved in key molecular interactions, showing binding free energy (ΔGbind) in the range of -5.09 kcal/mol (CTSL) to -26.09 kcal/mol (NSP6). At the binding pocket, catechin is majorly stabilized by the hydrophobic interactions, displays ΔEvdW values: -7.59 to -37.39 kcal/mol. Thus, the structural insights of better binding affinity and favorable molecular interaction of catechin toward multiple target proteins signify that catechin can be potentially explored as a multi-targeted agent against COVID-19.

Bioinformatics resources facilitate understanding and harnessing clinical research of SARS-CoV-2

The coronavirus disease 2019 (COVID-19) pandemic, caused by the coronavirus severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has created an unprecedented threat to public health. The pandemic has been sweeping the globe, impacting more than 200 countries, with more outbreaks still lurking on the horizon. At the time of the writing, no approved drugs or vaccines are available to treat COVID-19 patients, prompting an urgent need to decipher mechanisms underlying the pathogenesis and develop curative treatments. To fight COVID-19, researchers around the world have provided specific tools and molecular information for SARS-CoV-2. These pieces of information can be integrated to aid computational investigations and facilitate clinical research. This paper reviews current knowledge, the current status of drug development and various resources for key steps toward effective treatment of COVID-19, including the phylogenetic characteristics, genomic conservation and interaction data. The final goal of this paper is to provide information that may be utilized in bioinformatics approaches and aid target prioritization and drug repurposing. Several SARS-CoV-2-related tools/databases were reviewed, and a web-portal named OverCOVID (http://bis.zju.edu.cn/overcovid/) is constructed to provide a detailed interpretation of SARS-CoV-2 basics and share a collection of resources that may contribute to therapeutic advances. These information could improve researchers' understanding of SARS-CoV-2 and help to accelerate the development of new antiviral treatments.

Data science in unveiling COVID-19 pathogenesis and diagnosis: evolutionary origin to drug repurposing

MOTIVATION

The outbreak of novel severe acute respiratory syndrome coronavirus (SARS-CoV-2, also known as COVID-19) in Wuhan has attracted worldwide attention. SARS-CoV-2 causes severe inflammation, which can be fatal. Consequently, there has been a massive and rapid growth in research aimed at throwing light on the mechanisms of infection and the progression of the disease. With regard to this data science is playing a pivotal role in in silico analysis to gain insights into SARS-CoV-2 and the outbreak of COVID-19 in order to forecast, diagnose and come up with a drug to tackle the virus. The availability of large multiomics, radiological, bio-molecular and medical datasets requires the development of novel exploratory and predictive models, or the customisation of existing ones in order to fit the current problem. The high number of approaches generates the need for surveys to guide data scientists and medical practitioners in selecting the right tools to manage their clinical data.

RESULTS

Focusing on data science methodologies, we conduct a detailed study on the state-of-the-art of works tackling the current pandemic scenario. We consider various current COVID-19 data analytic domains such as phylogenetic analysis, SARS-CoV-2 genome identification, protein structure prediction, host-viral protein interactomics, clinical imaging, epidemiological research and drug discovery. We highlight data types and instances, their generation pipelines and the data science models currently in use. The current study should give a detailed sketch of the road map towards handling COVID-19 like situations by leveraging data science experts in choosing the right tools. We also summarise our review focusing on prime challenges and possible future research directions.

CONTACT

hguzzi@unicz.it, sroy01@cus.ac.in.

Drug design and repurposing with DockThor-VS web server focusing on SARS-CoV-2 therapeutic targets and their non-synonym variants

The COVID-19 caused by the SARS-CoV-2 virus was declared a pandemic disease in March 2020 by the World Health Organization (WHO). Structure-Based Drug Design strategies based on docking methodologies have been widely used for both new drug development and drug repurposing to find effective treatments against this disease. In this work, we present the developments implemented in the DockThor-VS web server to provide a virtual screening (VS) platform with curated structures of potential therapeutic targets from SARS-CoV-2 incorporating genetic information regarding relevant non-synonymous variations. The web server facilitates repurposing VS experiments providing curated libraries of currently available drugs on the market. At present, DockThor-VS provides ready-for-docking 3D structures for wild type and selected mutations for Nsp3 (papain-like, PLpro domain), Nsp5 (Mpro, 3CLpro), Nsp12 (RdRp), Nsp15 (NendoU), N protein, and Spike. We performed VS experiments of FDA-approved drugs considering the therapeutic targets available at the web server to assess the impact of considering different structures and mutations to identify possible new treatments of SARS-CoV-2 infections. The DockThor-VS is freely available at www.dockthor.lncc.br .

Black tea bioactives as inhibitors of multiple targets of SARS-CoV-2 (3CLpro, PLpro and RdRp): a virtual screening and molecular dynamic simulation study

The global pandemic due to the novel Severe Acute Respiratory Syndrome Corona Virus 2 (SARS-CoV-2) has taken more than a million lives. Lack of definitive vaccine/drugs against this highly contagious virus has accelerated exploratory research on novel natural and synthetic inhibitors. Tea is a rich source of bioactives and known to have antiviral properties. In this study, an in silico strategy involving ADMET property screening, receptor-ligand docking and molecular dynamic (MD) simulation was employed to screen potential tea bio-active inhibitors against three selected targets (RdRp, 3CLpro and PLpro) of SARS-CoV-2. Among the 70 tea bioactives screened, theaflavin 3,3'-di-gallate (TF3), Procyanidin B2 and Theaflavin 3-gallate (TF2a) exhibited highest binding affinities towards RdRp, 3CLpro/Mpro and PLpro targets of SARS-CoV-2 with low docking scores of -14.92, -11.68 and -10.90 kcal/mol, respectively. All of them showed a substantial number of hydrogen bonds along with other interactions in and around the active sites. Interestingly, the top bioactives in our study showed higher binding affinities compared with known antiviral drugs. Further, the top protein-ligand complexes showed less conformational changes during binding when subjected to MD simulation for 100 nanoseconds. The MMPBSA results revealed that RdRp-TF3, 3CLpro-Procyanidin B2 and PLpro-TF2a complexes were stable with binding free energies of -93.59 ± 43.97, -139.78 ± 16.51 and -96.88 ± 25.39 kJ/mol, respectively. Our results suggest that theaflavin 3,3'-digallate, Theaflavin 3-gallate and Procyanidin B2 found in black tea have the potential to act as inhibitors for selected targets of SARS-CoV-2 and can be considered as drug candidates in future studies against COVID-19.

Artificial intelligence, machine learning, and drug repurposing in cancer

Introduction: Drug repurposing provides a cost-effective strategy to re-use approved drugs for new medical indications. Several machine learning (ML) and artificial intelligence (AI) approaches have been developed for systematic identification of drug repurposing leads based on big data resources, hence further accelerating and de-risking the drug development process by computational means.Areas covered: The authors focus on supervised ML and AI methods that make use of publicly available databases and information resources. While most of the example applications are in the field of anticancer drug therapies, the methods and resources reviewed are widely applicable also to other indications including COVID-19 treatment. A particular emphasis is placed on the use of comprehensive target activity profiles that enable a systematic repurposing process by extending the target profile of drugs to include potent off-targets with therapeutic potential for a new indication.Expert opinion: The scarcity of clinical patient data and the current focus on genetic aberrations as primary drug targets may limit the performance of anticancer drug repurposing approaches that rely solely on genomics-based information. Functional testing of cancer patient cells exposed to a large number of targeted therapies and their combinations provides an additional source of repurposing information for tissue-aware AI approaches.

Screening possible drug molecules for Covid-19. The example of vanadium (III/IV/V) complex molecules with computational chemistry and molecular docking

We are still facing a Covid-19 pandemic these days and after the aggressively infection control measures taken by the governments in the whole world, there is a need of a rapid pharmaceutical solution in order to control this crisis. The computer aided chemistry and molecular docking is a rapid tool for drug screening and investigation. Moreover, more metal-based drugs are tested daily by research institutes for their antiviral activity. Here, we make use of theoretical studies on previously published biological active complex molecules of vanadium as an example of evaluating possible drug candidates before entering the laboratory. We used DFT calculation studies for structural elucidation and optimization of the molecules and molecular docking studies on several Covid-19 related proteins. Our findings suggest that drug discovery should always be computer -aided. Additionally, it is found that Vtocdea and VXn molecules are seem to be good candidates for further studies as antiviral agents.

Machine and Deep Learning towards COVID-19 Diagnosis and Treatment: Survey, Challenges, and Future Directions

With many successful stories, machine learning (ML) and deep learning (DL) have been widely used in our everyday lives in a number of ways. They have also been instrumental in tackling the outbreak of Coronavirus (COVID-19), which has been happening around the world. The SARS-CoV-2 virus-induced COVID-19 epidemic has spread rapidly across the world, leading to international outbreaks. The COVID-19 fight to curb the spread of the disease involves most states, companies, and scientific research institutions. In this research, we look at the Artificial Intelligence (AI)-based ML and DL methods for COVID-19 diagnosis and treatment. Furthermore, in the battle against COVID-19, we summarize the AI-based ML and DL methods and the available datasets, tools, and performance. This survey offers a detailed overview of the existing state-of-the-art methodologies for ML and DL researchers and the wider health community with descriptions of how ML and DL and data can improve the status of COVID-19, and more studies in order to avoid the outbreak of COVID-19. Details of challenges and future directions are also provided.

Tracing the driving forces responsible for the remarkable infectivity of 2019-nCoV: 1. Receptor binding domain in its bound and unbound states

The pandemic of COVID-19 has posed an urgent need to learn the dynamics of the virus and the mechanism of its contagion of host cells. By means of molecular dynamics simulations, this work addressed the behavior of 2019-nCoV in two aspects: the binding affinity of its receptor binding domain (RBD) with ACE2, and its potential conformation preferences in its unbound state. The results showed that the RBD of 2019-nCov bound much stronger with ACE2 than that of SARS-CoV due to a better organized hydrogen bond network between the former pair with most of the residues at the contact interface sharing the responsibility to hold the pair tightly. This is in contrast to the case of SARS-CoV, which strongly relied on the residues at the ends of the cleft. In its unbound state, the RBD of 2019-nCoV was found to fold part of its receptor binding motif (RBM) into a helical conformation and flip into a concave to minimize its contact with the external environment. This has the biological implication that the virus may achieve higher translational motion in the condensed phase and have a higher chance of survival by avoiding capture by the immune system before reaching its target receptor.

Therapeutic targets and potential agents for the treatment of COVID-19

The outbreak of coronavirus disease 2019 (COVID-19), which is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, has become a global crisis. As of November 9, COVID-19 has already spread to more than 190 countries with 50,000,000 infections and 1,250,000 deaths. Effective therapeutics and drugs are in high demand. The structure of SARS-CoV-2 is highly conserved with those of SARS-CoV and Middle East respiratory syndrome-CoV. Enzymes, including RdRp, M^pro /3CL^pro , and PL^pro , which play important roles in viral transcription and replication, have been regarded as key targets for therapies against coronaviruses, including SARS-CoV-2. The identification of readily available drugs for repositioning in COVID-19 therapy is a relatively rapid approach for clinical treatment, and a series of approved or candidate drugs have been proven to be efficient against COVID-19 in preclinical or clinical studies. This review summarizes recent progress in the development of drugs against SARS-CoV-2 and the targets involved.

COVID-19 drug repurposing: A review of computational screening methods, clinical trials, and protein interaction assays

The situation of coronavirus disease 2019 (COVID-19) pandemic is rapidly evolving, and medical researchers around the globe are dedicated to finding cures for the disease. Drug repurposing, as an efficient way for drug development, has received a lot of attention. However, the huge amount of studies makes it challenging to keep up to date with the literature on COVID-19 therapeutic development. This review addresses this challenge by grouping the COVID-19 drug repurposing research into three large groups, including clinical trials, computational research, and in vitro protein-binding experiments. Particularly, to facilitate future drug discovery and the creation of effective drug combinations, drugs are organized by their mechanisms of action and reviewed by their efficacy measured by clinical trials. Providing this subtyping information, we hope this review would serve the scientists, clinicians, and the pharmaceutical industry who are looking at the new therapeutics for COVID-19 treatment.

Next-generation computational tools and resources for coronavirus research: From detection to vaccine discovery

The COVID-19 pandemic has affected 215 countries and territories around the world with 60,187,347 coronavirus cases and 17,125,719 currently infected patients confirmed as of the November 25, 2020. Currently, many countries are working on developing new vaccines and therapeutic drugs for this novel virus strain, and a few of them are in different phases of clinical trials. The advancement in high-throughput sequence technologies, along with the application of bioinformatics, offers invaluable knowledge on genomic characterization and molecular pathogenesis of coronaviruses. Recent multi-disciplinary studies using bioinformatics methods like sequence-similarity, phylogenomic, and computational structural biology have provided an in-depth understanding of the molecular and biochemical basis of infection, atomic-level recognition of the viral-host receptor interaction, functional annotation of important viral proteins, and evolutionary divergence across different strains. Additionally, various modern immunoinformatic approaches are also being used to target the most promiscuous antigenic epitopes from the SARS-CoV-2 proteome for accelerating the vaccine development process. In this review, we summarize various important computational tools and databases available for systematic sequence-structural study on coronaviruses. The features of these public resources have been comprehensively discussed, which may help experimental biologists with predictive insights useful for ongoing research efforts to find therapeutics against the infectious COVID-19 disease.

An Up-to-Date Overview of Therapeutic Agents for the Treatment of COVID-19 Disease

Acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic has a great potential to overwhelm the world healthcare systems that may lead to high morbidity and mortality. It also affects world economic development in the future. Currently, no proven effective drugs or vaccines are available for the management of COVID-19 disease. The pace of normal drug development progression is unacceptable in the context of the current pandemic. Therefore, repurposing the existing drugs that were used for the treatment of malaria, Ebola, and influenza helps rapid drug development for COVID-19. Currently, several repurposing candidate drugs are in a clinical trial including, chloroquine monoclonal antibodies, convalescent plasma, interferon, and antiviral therapies. Antiviral drugs like arbidol, remdesiv and favirnavir are the most promising due to the similarities of the viruses regarding viral entry, fusion, uncoating, and replication. This review article provides an overview of the potential therapeutic agent, which displayed better clinical treatment outcomes. Moreover, with further understanding of the SARS-CoV-2 virus, new drugs targeting specific SARS-CoV-2 viral components arise, and investigations on these novels anti-SARSCoV- 2 agents are also reviewed.

Druggable targets from coronaviruses for designing new antiviral drugs

Severe respiratory infections were highlighted in the SARS-CoV outbreak in 2002, as well as MERS-CoV, in 2012. Recently, the novel CoV (COVID-19) has led to severe respiratory damage to humans and deaths in Asia, Europe, and Americas, which allowed the WHO to declare the pandemic state. Notwithstanding all impacts caused by Coronaviruses, it is evident that the development of new antiviral agents is an unmet need. In this review, we provide a complete compilation of all potential antiviral agents targeting macromolecular structures from these Coronaviruses (Coronaviridae), providing a medicinal chemistry viewpoint that could be useful for designing new therapeutic agents.