Computational drug discovery and repurposing for the treatment of COVID-19: A systematic review

Empowering Graph Neural Network-Based Computational Drug Repositioning with Large Language Model-Inferred Knowledge Representation

Computational drug repositioning, through predicting drug-disease associations (DDA), offers significant potential for discovering new drug indications. Current methods incorporate graph neural networks (GNN) on drug-disease heterogeneous networks to predict DDAs, achieving notable performances compared to traditional machine learning and matrix factorization approaches. However, these methods depend heavily on network topology, hampered by incomplete and noisy network data, and overlook the wealth of biomedical knowledge available. Correspondingly, large language models (LLMs) excel in graph search and relational reasoning, which can possibly enhance the integration of comprehensive biomedical knowledge into drug and disease profiles. In this study, we first investigate the contribution of LLM-inferred knowledge representation in drug repositioning and DDA prediction. A zero-shot prompting template was designed for LLM to extract high-quality knowledge descriptions for drug and disease entities, followed by embedding generation from language models to transform the discrete text to continual numerical representation. Then, we proposed LLM-DDA with three different model architectures (LLM-DDANode Feat, LLM-DDADual GNN, LLM-DDAGNN-AE) to investigate the best fusion mode for LLM-based embeddings. Extensive experiments on four DDA benchmarks show that, LLM-DDAGNN-AE achieved the optimal performance compared to 11 baselines with the overall relative improvement in AUPR of 23.22%, F1-Score of 17.20%, and precision of 25.35%. Meanwhile, selected case studies of involving Prednisone and Allergic Rhinitis highlighted the model's capability to identify reliable DDAs and knowledge descriptions, supported by existing literature. This study showcases the utility of LLMs in drug repositioning with its generality and applicability in other biomedical relation prediction tasks.

Drug repositioning based on residual attention network and free multiscale adversarial training

BACKGROUND

Conducting traditional wet experiments to guide drug development is an expensive, time-consuming and risky process. Analyzing drug function and repositioning plays a key role in identifying new therapeutic potential of approved drugs and discovering therapeutic approaches for untreated diseases. Exploring drug-disease associations has far-reaching implications for identifying disease pathogenesis and treatment. However, reliable detection of drug-disease relationships via traditional methods is costly and slow. Therefore, investigations into computational methods for predicting drug-disease associations are currently needed.

RESULTS

This paper presents a novel drug-disease association prediction method, RAFGAE. First, RAFGAE integrates known associations between diseases and drugs into a bipartite network. Second, RAFGAE designs the Re_GAT framework, which includes multilayer graph attention networks (GATs) and two residual networks. The multilayer GATs are utilized for learning the node embeddings, which is achieved by aggregating information from multihop neighbors. The two residual networks are used to alleviate the deep network oversmoothing problem, and an attention mechanism is introduced to combine the node embeddings from different attention layers. Third, two graph autoencoders (GAEs) with collaborative training are constructed to simulate label propagation to predict potential associations. On this basis, free multiscale adversarial training (FMAT) is introduced. FMAT enhances node feature quality through small gradient adversarial perturbation iterations, improving the prediction performance. Finally, tenfold cross-validations on two benchmark datasets show that RAFGAE outperforms current methods. In addition, case studies have confirmed that RAFGAE can detect novel drug-disease associations.

CONCLUSIONS

The comprehensive experimental results validate the utility and accuracy of RAFGAE. We believe that this method may serve as an excellent predictor for identifying unobserved disease-drug associations.

Applying the bioisosterism strategy to obtain lead compounds against SARS-CoV-2 cysteine proteases: An in-silico approach

SARS-CoV-2 cysteine proteases are essential nonstructural proteins due to their role in the formation of the virus multiple enzyme replication-transcription complex. As a result, those functional proteins are extremely relevant targets in the development of a new drug candidate to fight COVID-19. Based on this fact and guided by the bioisosterism strategy, the present work has selected 126 out of 1050 ligands from DrugBank website. Subsequently, 831 chemical analogs containing bioisosteres, some of which became structurally simplified, were created using the MB-Isoster software, and molecular docking simulations were performed using AutoDock Vina. Finally, a study of physicochemical properties, along with pharmacokinetic profiles, was carried out through SwissADME and ADMETlab 2.0 platforms. The promising results obtained with the molecules encoded as DB00549_BI_005, DB04868_BI_003, DB11984_BI_002, DB12364_BI_006 and DB12805_BI_004 must be confirmed by molecular dynamics studies, followed by in vitro and in vivo empirical tests that ratify the advocated in-silico results.

Mental Healthcare in Pediatrics During the COVID-19 Pandemic: A Call for International Public Health Action

Public health measures associated with coronavirus disease 2019 (COVID-19), such as lockdowns and quarantine of suspected cases, can negatively affect children's mental health status. Although the current crisis provides personal growth and family cohesion opportunities, pitfalls appear to outweigh the benefits. The magnitude and quality of its impact on children depend on several factors, including anxiety, lack of social contact, and a reduced opportunity for stress regulation, along with an increased risk for parental mental health issues, child maltreatment, and domestic violence. Children with special needs and social disadvantages like trauma experiences, disabilities, pre-existing mental illness, e.g., autism spectrum disorders and hyperactivity, and low socioeconomic status, may be at higher risk in this context. Here, the potentials social support can provide for pediatrics, both healthy children and children with special needs, are reviewed after an overview of quarantine's adverse effects on this special population during a pandemic. The most common psychological issues associated with the COVID-19 pandemic are sleep disorders, mood swings, depression, anxiety, decreased attention, stress, irritability, anger, and fear. Moreover, the impact of COVID-19 on children's physical health includes weight gain, reduced physical activity, immune dysregulation, and cardiometabolic disorders. All support systems, involving parents, teachers/school counselors, pediatricians, mental healthcare workers, and Health and Art (HEART) groups, need to enter the scene and make their share of children's mental health care.

Global Challenges After a Global Challenge: Lessons Learned from the COVID-19 Pandemic

Coronavirus disease 2019 (COVID-19) has affected not only individual lives but also the world and global systems, both natural and human-made. Besides millions of deaths and environmental challenges, the rapid spread of the infection and its very high socioeconomic impact have affected healthcare, economic status and wealth, and mental health across the globe. To better appreciate the pandemic's influence, multidisciplinary and interdisciplinary approaches are needed. In this chapter, world-leading scientists from different backgrounds share collectively their views about the pandemic's footprint and discuss challenges that face the international community.

Naturally Occurring Isorhamnetin Glycosides as Potential Agents Against Influenza Viruses: Antiviral and Molecular Docking Studies

Influenza remains one of the most widespread infections, causing an annual illness in adults and children. Therefore, the search for new antiviral drugs is one of the priorities of practical health care. Eight isorhamnetin glycosides were purified from Persicaria species, characterized by nuclear magnetic resonance spectroscopy and mass spectrometry and then evaluated as potential agents against influenza virus. A comprehensive in vitro and in vivo assessment of the compounds revealed that compound 5 displayed the most potent inhibitory activity with an EC50 value of 1.2-1.3 μM, better than standard drugs (isorhamnetin 28.0-56.0 μM and oseltamivir 1.3-9.1 μM). Molecular docking results also revealed that compound 5 has the lowest binding energy (-10.7 kcal/mol) among the tested compounds and isorhamnetin (-8.1 kcal/mol). The ability of the isorhamnetin glycosides to suppress the reproduction of the influenza virus was studied on a model of a cell culture and chicken embryos. The ability of active compounds to influence the structure of the virion, as well as the activity of hemagglutinin and neuraminidase, has been demonstrated. Compound 1, 5, and 6 demonstrated the most effective inhibition of virus replication for all tested viruses. Molecular dynamics simulation techniques were run for 100 ns for compound 5 with two protein receptors Hem (1RUY) and Neu (3BEQ). These results revealed that the Hem-complex system acquired a relatively more stable conformation and even better descriptors than the other Neu-complex studied systems, suggesting that it can be an effective inhibiting drug toward hemagglutinin than neuraminidase inhibition. Based on the reported results, compound 5 can be a good candidate to be evaluated for effectiveness in preclinical testing.

A Multifaceted Computational Approach to Understanding the MERS-CoV Main Protease and Brown Algae Compounds' Interaction

Middle East Respiratory Syndrome (MERS) is a viral respiratory disease caused b a special type of coronavirus called MERS-CoV. In the search for effective substances against the MERS-CoV main protease, we looked into compounds from brown algae, known for their medicinal benefits. From a set of 1212 such compounds, our computer-based screening highlighted four-CMNPD27819, CMNPD1843, CMNPD4184, and CMNPD3156. These showed good potential in how they might attach to the MERS-CoV protease, comparable to a known inhibitor. We confirmed these results with multiple computer tests. Studies on the dynamics and steadiness of these compounds with the MERS-CoV protease were performed using molecular dynamics (MD) simulations. Metrics like RMSD and RMSF showed their stability. We also studied how these compounds and the protease interact in detail. An analysis technique, PCA, showed changes in atomic positions over time. Overall, our computer studies suggest brown algae compounds could be valuable in fighting MERS. However, experimental validation is needed to prove their real-world effectiveness.

GAABind: a geometry-aware attention-based network for accurate protein-ligand binding pose and binding affinity prediction

Protein-ligand interactions are increasingly profiled at high-throughput, playing a vital role in lead compound discovery and drug optimization. Accurate prediction of binding pose and binding affinity constitutes a pivotal challenge in advancing our computational understanding of protein-ligand interactions. However, inherent limitations still exist, including high computational cost for conformational search sampling in traditional molecular docking tools, and the unsatisfactory molecular representation learning and intermolecular interaction modeling in deep learning-based methods. Here we propose a geometry-aware attention-based deep learning model, GAABind, which effectively predicts the pocket-ligand binding pose and binding affinity within a multi-task learning framework. Specifically, GAABind comprehensively captures the geometric and topological properties of both binding pockets and ligands, and employs expressive molecular representation learning to model intramolecular interactions. Moreover, GAABind proficiently learns the intermolecular many-body interactions and simulates the dynamic conformational adaptations of the ligand during its interaction with the protein through meticulously designed networks. We trained GAABind on the PDBbindv2020 and evaluated it on the CASF2016 dataset; the results indicate that GAABind achieves state-of-the-art performance in binding pose prediction and shows comparable binding affinity prediction performance. Notably, GAABind achieves a success rate of 82.8% in binding pose prediction, and the Pearson correlation between predicted and experimental binding affinities reaches up to 0.803. Additionally, we assessed GAABind's performance on the severe acute respiratory syndrome coronavirus 2 main protease cross-docking dataset. In this evaluation, GAABind demonstrates a notable success rate of 76.5% in binding pose prediction and achieves the highest Pearson correlation coefficient in binding affinity prediction compared with all baseline methods.

Indole-Based Compounds as Potential Drug Candidates for SARS-CoV-2

The COVID-19 pandemic has posed a significant threat to society in recent times, endangering human health, life, and economic well-being. The disease quickly spreads due to the highly infectious SARS-CoV-2 virus, which has undergone numerous mutations. Despite intense research efforts by the scientific community since its emergence in 2019, no effective therapeutics have been discovered yet. While some repurposed drugs have been used to control the global outbreak and save lives, none have proven universally effective, particularly for severely infected patients. Although the spread of the disease is generally under control, anti-SARS-CoV-2 agents are still needed to combat current and future infections. This study reviews some of the most promising repurposed drugs containing indolyl heterocycle, which is an essential scaffold of many alkaloids with diverse bio-properties in various biological fields. The study also discusses natural and synthetic indole-containing compounds with anti-SARS-CoV-2 properties and computer-aided drug design (in silico studies) for optimizing anti-SARS-CoV-2 hits/leads.

Virtual high-throughput screening: Potential inhibitors targeting aminopeptidase N (CD13) and PIKfyve for SARS-CoV-2

Since the outbreak of the novel coronavirus nearly 3 years ago, the world's public health has been under constant threat. At the same time, people's travel and social interaction have also been greatly affected. The study focused on the potential host targets of SARS-CoV-2, CD13, and PIKfyve, which may be involved in viral infection and the viral/cell membrane fusion stage of SARS-CoV-2 in humans. In this study, electronic virtual high-throughput screening for CD13 and PIKfyve was conducted using Food and Drug Administration-approved compounds in ZINC database. The results showed that dihydroergotamine, Saquinavir, Olysio, Raltegravir, and Ecteinascidin had inhibitory effects on CD13. Dihydroergotamine, Sitagliptin, Olysio, Grazoprevir, and Saquinavir could inhibit PIKfyve. After 50 ns of molecular dynamics simulation, seven compounds showed stability at the active site of the target protein. Hydrogen bonds and van der Waals forces were formed with target proteins. At the same time, the seven compounds showed good binding free energy after binding to the target proteins, providing potential drug candidates for the treatment and prevention of SARS-CoV-2 and SARS-CoV-2 variants.

Computer Analysis of the Inhibition of ACE2 by Flavonoids and Identification of Their Potential Antiviral Pharmacophore Site

In the present study, we investigated the antiviral activities of 17 flavonoids as natural products. These derivatives were evaluated for their in vitro antiviral activities against HIV and SARS-CoV-2. Their antiviral activity was evaluated for the first time based on POM (Petra/Osiris/Molispiration) theory and docking analysis. POM calculation was used to analyze the atomic charge and geometric characteristics. The side effects, drug similarities, and drug scores were also assumed for the stable structure of each compound. These results correlated with the experimental values. The bioinformatics POM analyses of the relative antiviral activities of these derivatives are reported for the first time.

Development of FRET and Stress Granule Dual-Based System to Screen for Viral 3C Protease Inhibitors

3C proteases (3Cpros) of picornaviruses and 3C-like proteases (3CLpros) of coronaviruses and caliciviruses represent a group of structurally and functionally related viral proteases that play pleiotropic roles in supporting the viral life cycle and subverting host antiviral responses. The design and screening for 3C/3CLpro inhibitors may contribute to the development broad-spectrum antiviral therapeutics against viral diseases related to these three families. However, current screening strategies cannot simultaneously assess a compound’s cytotoxicity and its impact on enzymatic activity and protease-mediated physiological processes. The viral induction of stress granules (SGs) in host cells acts as an important antiviral stress response by blocking viral translation and stimulating the host immune response. Most of these viruses have evolved 3C/3CLpro-mediated cleavage of SG core protein G3BP1 to counteract SG formation and disrupt the host defense. Yet, there are no SG-based strategies screening for 3C/3CLpro inhibitors. Here, we developed a fluorescence resonance energy transfer (FRET) and SG dual-based system to screen for 3C/3CLpro inhibitors in living cells. We took advantage of FRET to evaluate the protease activity of poliovirus (PV) 3Cpro and live-monitor cellular SG dynamics to cross-verify its effect on the host antiviral response. Our drug screen uncovered a novel role of Telaprevir and Trifluridine as inhibitors of PV 3Cpro. Moreover, Telaprevir and Trifluridine also modulated 3Cpro-mediated physiological processes, including the cleavage of host proteins, inhibition of the innate immune response, and consequent facilitation of viral replication. Taken together, the FRET and SG dual-based system exhibits a promising potential in the screening for inhibitors of viral proteases that cleave G3BP1.

Clinical efficacy of Azithromycin for COVID-19 management: A systematic meta-analysis of meta-analyses

Background

Azithromycin has been adopted as a component of the COVID-19 management protocol throughout the global healthcare settings but with a questionable if not downright unsubstantiated evidence base.

Objectives

In order to amalgamate and critically appraise the conflicting evidence around the clinical efficacy of Azithromycin (AZO) vis a vis COVID-19 management outcomes, a meta-analysis of meta-analyses was carried out to establish an evidence-based holistic status of AZO vis a vis its efficacy as a component-in-use of the COVID-19 management protocol.

Methods

A comprehensive systematic search was carried out through PubMed/Medline, Cochrane and Epistemonikos with a subsequent appraisal of abstracts and full-texts, as required. The Quality of Reporting of Meta-analyses (QUOROM) checklist and the Assessment of Multiple Systematic Reviews (AMSTAR) methodology were adopted to assess the methodological quality of the included meta-analyses. Random-effects models were developed to calculate summarized pool Odds Ratios (with 95% confidence interval) for the afore determined primary and secondary outcomes.

Results

AZO, when compared with best available therapy (BAT) including or excluding Hydroxychloroquine, exhibited statistically insignificant reduction in mortality [(n= 27,204 patients) OR= 0.77 (95% CI: 0.51-1.16) (I2= 97%)], requirement of mechanical ventilation [(n= 14,908 patients) OR= 1.4 (95% CI: 0.58-3.35) (I²= 98%)], induction of arrhythmia [(n= 9,723 patients) OR= 1.21 (95% CI: 0.63-2.32) (I²= 92%)] and QTc prolongation (a surrogate for torsadogenic effect) [(n= 6,534 patients) OR= 0.62 (95% CI: 0.23-1.73) (I²= 96%)].

Conclusion

The meta-analysis of meta-analyses portrays AZO as a pharmacological agent that does not appear to have a comparatively superior clinical efficacy than BAT when it comes to COVID-19 management. Secondary to a very real threat of anti-bacterial resistance, it is suggested that AZO be discontinued and removed from COVID-19 management protocols.

Targeting viral proteins for restraining SARS-CoV-2: focusing lens on viral proteins beyond spike for discovering new drug targets

INTRODUCTION

Emergence of highly infectious SARS-CoV-2 variants are reducing protection provided by current vaccines, requiring constant updates in antiviral approaches. The virus encodes four structural and sixteen nonstructural proteins which play important roles in viral genome replication and transcription, virion assembly, release , entry into cells, and compromising host cellular defenses. As alien proteins to host cells, many viral proteins represent potential targets for combating the SARS-CoV-2.

AREAS COVERED

Based on literature from PubMed and Web of Science databases, the authors summarize the typical characteristics of SARS-CoV-2 from the whole viral particle to the individual viral proteins and their corresponding functions in virus life cycle. The authors also discuss the potential and emerging targeted interventions to curb virus replication and spread in detail to provide unique insights into SARS-CoV-2 infection and countermeasures against it.

EXPERT OPINION

Our comprehensive analysis highlights the rationale to focus on non-spike viral proteins that are less mutated but have important functions. Examples of this include: structural proteins (e.g. nucleocapsid protein, envelope protein) and extensively-concerned nonstructural proteins (e.g. NSP3, NSP5, NSP12) along with the ones with relatively less attention (e.g. NSP1, NSP10, NSP14 and NSP16), for developing novel drugs to overcome resistance of SARS-CoV-2 variants to preexisting vaccines and antibody-based treatments.

A dynamic Bayesian network-based emergency decision-making framework highlighting emergency propagations: Illustrated using the Fukushima nuclear accidents and the Covid-19 pandemic

When facing public emergencies, human societies need to make decisions rapidly in order to mitigate the problems. However, this process can be difficult due to complexity of the emergency scenarios and lack of systematic methods for analyzing them. In the work reported here, we develop a framework based upon dynamic Bayesian networks in order to simulate emergency scenarios and support corresponding decisions. In this framework, we highlight the importance of emergency propagation, which is a critical factor often ignored by decisionmakers. We illustrate that failure of considering emergency propagation can lead to suboptimal mitigation strategies. By incorporating this critical factor, our framework enables decisionmakers to identify optimal response strategies minimizing emergency impacts. Scenarios developed from two public emergencies: the 2011 Fukushima nuclear power plant accidents and the Covid-19 pandemic, are utilized to illustrate the framework in this paper. Capabilities of the framework in supporting decision making in both events illustrate its generality and adaptability when dealing with complex real-world situations. Our analysis results reveal many similarities between these two seemingly distinct events. This indicates that seemingly unrelated emergencies can share many common features beyond their idiosyncratic characteristics. Valuable mitigation insights can be obtained by analyzing a broad range of past emergencies systematically.

Multi-Targeting Approach in Selection of Potential Molecule for COVID-19 Treatment

The coronavirus disease (COVID-19) is a pandemic that started in the City of Wuhan, Hubei Province, China, caused by the spread of coronavirus (SARS-CoV-2). Drug discovery teams around the globe are in a race to develop a medicine for its management. It takes time for a novel molecule to enter the market, and the ideal way is to exploit the already approved drugs and repurpose them therapeutically. We have attempted to screen selected molecules with an affinity towards multiple protein targets in COVID-19 using the Schrödinger suit for in silico predictions. The proteins selected were angiotensin-converting enzyme-2 (ACE2), main protease (MPro), and spike protein. The molecular docking, prime MM-GBSA, induced-fit docking (IFD), and molecular dynamics (MD) simulations were used to identify the most suitable molecule that forms a stable interaction with the selected viral proteins. The ligand-binding stability for the proteins PDB-IDs 1ZV8 (spike protein), 5R82 (Mpro), and 6M1D (ACE2), was in the order of nintedanib > quercetin, nintedanib > darunavir, nintedanib > baricitinib, respectively. The MM-GBSA, IFD, and MD simulation studies imply that the drug nintedanib has the highest binding stability among the shortlisted. Nintedanib, primarily used for idiopathic pulmonary fibrosis, can be considered for repurposing for us against COVID-19.

Personalized predictions of adverse side effects of the COVID-19 vaccines

Background

Misconceptions about adverse side effects are thought to influence public acceptance of the Coronavirus disease 2019 (COVID-19) vaccines negatively. To address such perceived disadvantages of vaccines, a novel machine learning (ML) approach was designed to generate personalized predictions of the most common adverse side effects following injection of six different COVID-19 vaccines based on personal and health-related characteristics.

Methods

Prospective data of adverse side effects following COVID-19 vaccination in 19943 participants from Iran and Switzerland was utilized. Six vaccines were studied: The AZD1222, Sputnik V, BBIBP-CorV, COVAXIN, BNT162b2, and the mRNA-1273 vaccine. The eight side effects were considered as the model output: fever, fatigue, headache, nausea, chills, joint pain, muscle pain, and injection site reactions. The total input parameters for the first and second dose predictions were 46 and 54 features, respectively, including age, gender, lifestyle variables, and medical history. The performances of multiple ML models were compared using Area Under the Receiver Operating Characteristic Curve (ROC-AUC).

Results

The total number of people receiving the first dose of the AZD1222, Sputnik V, BBIBP-CorV, COVAXIN, BNT162b2, and mRNA-1273 were 6022, 7290, 5279, 802, 277, and 273, respectively. For the second dose, the numbers were 2851, 5587, 3841, 599, 242 and 228. The Logistic Regression model for predicting different side effects of the first dose achieved ROC-AUCs of 0.620-0.686, 0.685-0.716, 0.632-0.727, 0.527-0.598, 0.548-0.655, 0.545-0.712 for the AZD1222, Sputnik V, BBIBP-CorV, COVAXIN, BNT162b2 and mRNA-1273 vaccines, respectively. The second dose models yielded ROC-AUCs of 0.777-0.867, 0.795-0.848, 0.857-0.906, 0.788-0.875, 0.683-0.850, and 0.486-0.680, respectively.

Conclusions

Using a large cohort of recipients vaccinated with COVID-19 vaccines, a novel and personalized strategy was established to predict the occurrence of the most common adverse side effects with high accuracy. This technique can serve as a tool to inform COVID-19 vaccine selection and generate personalized factsheets to curb concerns about adverse side effects.

Drug repurposing approach against chikungunya virus: an in vitro and in silico study

The chikungunya virus (CHIKV) is an alphavirus transmitted by Aedes mosquitoes. There are no licenced antivirals or vaccines for treatment or prevention. Drug repurposing approach has emerged as a novel concept to find alternative uses of therapeutics to battle pathogens. In the present study, anti CHIKV activity of fourteen FDA-approved drugs was investigated by in vitro and in silico approaches. Focus-forming unit assay, immunofluorescence test, and quantitative RT-PCR assay were used to assess the in vitro inhibitory effect of these drugs against CHIKV in Vero CCL-81 cells. The findings showed that nine compounds, viz., temsirolimus, 2-fluoroadenine, doxorubicin, felbinac, emetine, lomibuvir, enalaprilat, metyrapone and resveratrol exhibit anti chikungunya activity. Furthermore, in silico molecular docking studies performed by targeting CHIKV structural and non-structural proteins revealed that these drugs can bind to structural protein targets such as envelope protein, and capsid, and non-structural proteins NSP2, NSP3 and NSP4 (RdRp). Findings from in vitro and in silico studies reveal that these drugs can suppress the infection and replication of CHIKV and further in vivo studies followed by clinical trials are warranted.

Psychotropic drug repurposing for COVID-19: A Systematic Review and Meta-Analysis

Several psychotropic drugs, including antidepressants (AD), mood stabilizers, and antipsychotics (AP) have been suggested to have favorable effects in the treatment of COVID-19. The aim of this systematic review and meta-analysis was to collect evidence from studies concerning the scientific evidence for the repurposing of psychotropic drugs in COVID-19 treatment. Two independent authors searched PubMed-MEDLINE, Scopus, PsycINFO, and ClinicalTrials.gov databases, and reviewed the reference lists of articles for eligible articles published up to 13th December 2021. All computational, preclinical and clinical (observational and/or RCTs) studies on the effect of any psychotropic drug on Sars-CoV-2 or patients with COVID-19 were considered for inclusion. We conducted random effect meta-analyses on clinical studies reporting the effect of AD or AP on COVID-19 outcomes. 29 studies were included in the synthesis: 15 clinical, 9 preclinical, and 5 computational studies. 9 clinical studies could be included in the quantitative analyses. AD did not increase the risk of severe COVID-19 (RR= 1.71; CI 0.65-4.51) or mortality (RR=0.94; CI 0.81-1.09). Fluvoxamine was associated with a reduced risk of mortality for COVID-19 (OR=0.15; CI 0.02-0.95). AP increased the risk of severe COVID-19 (RR=3.66; CI 2.76-4.85) and mortality (OR=1.53; CI 1.15-2.03). Fluvoxamine might be a possible candidate for psychotropic drug repurposing in COVID-19 due to its anti-inflammatory and antiviral potential, while evidence on other AD is still controversial. Although AP are associated with worse COVID-19 outcomes, their use should be evaluated case to case and ongoing treatment with antipsychotics should be not discontinued in psychiatric patients.

Nonthermal Biocompatible Plasma Inactivation of Coronavirus SARS-CoV-2: Prospects for Future Antiviral Applications

The coronavirus disease (COVID-19) pandemic has placed a massive impact on global civilization. Finding effective treatments and drugs for these viral diseases was crucial. This paper outlined and highlighted key elements of recent advances in nonthermal biocompatible plasma (NBP) technology for antiviral applications. We searched for papers on NBP virus inactivation in PubMed ePubs, Scopus, and Web of Science databases. The data and relevant information were gathered in order to establish a mechanism for NBP-based viral inactivation. NBP has been developed as a new, effective, and safe strategy for viral inactivation. NBP may be used to inactivate viruses in an ecologically friendly way as well as activate animal and plant viruses in a number of matrices. The reactive species have been shown to be the cause of viral inactivation. NBP-based disinfection techniques provide an interesting solution to many of the problems since they are simply deployable and do not require the resource-constrained consumables and reagents required for traditional decontamination treatments. Scientists are developing NBP technology solutions to assist the medical community in dealing with the present COVID-19 outbreak. NBP is predicted to be the most promising strategy for battling COVID-19 and other viruses in the future.

Virtual Screening of Artemisia annua Phytochemicals as Potential Inhibitors of SARS-CoV-2 Main Protease Enzyme

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a human coronaviruses that emerged in China at Wuhan city, Hubei province during December 2019. Subsequently, SARS-CoV-2 has spread worldwide and caused millions of deaths around the globe. Several compounds and vaccines have been proposed to tackle this crisis. Novel recommended in silico approaches have been commonly used to screen for specific SARS-CoV-2 inhibitors of different types. Herein, the phytochemicals of Pakistani medicinal plants (especially Artemisia annua) were virtually screened to identify potential inhibitors of the SARS-CoV-2 main protease enzyme. The X-ray crystal structure of the main protease of SARS-CoV-2 with an N3 inhibitor was obtained from the protein data bank while A. annua phytochemicals were retrieved from different drug databases. The docking technique was carried out to assess the binding efficacy of the retrieved phytochemicals; the docking results revealed that several phytochemicals have potential to inhibit the SARS-CoV-2 main protease enzyme. Among the total docked compounds, the top-10 docked complexes were considered for further study and evaluated for their physiochemical and pharmacokinetic properties. The top-3 docked complexes with the best binding energies were as follows: the top-1 docked complex with a -7 kcal/mol binding energy score, the top-2 docked complex with a -6.9 kcal/mol binding energy score, and the top-3 docked complex with a -6.8 kcal/mol binding energy score. These complexes were subjected to a molecular dynamic simulation analysis for further validation to check the dynamic behavior of the selected top-complexes. During the whole simulation time, no major changes were observed in the docked complexes, which indicated complex stability. Additionally, the free binding energies for the selected docked complexes were also estimated via the MM-GB/PBSA approach, and the results revealed that the total delta energies of MMGBSA were -24.23 kcal/mol, -26.38 kcal/mol, and -25 kcal/mol for top-1, top-2, and top-3, respectively. MMPBSA calculated the delta total energy as -17.23 kcal/mol (top-1 complex), -24.75 kcal/mol (top-2 complex), and -24.86 kcal/mol (top-3 complex). This study explored in silico screened phytochemicals against the main protease of the SARS-CoV-2 virus; however, the findings require an experimentally based study to further validate the obtained results.

The New Era of Drug Discovery: The Power of Computer-aided Drug Design (CADD)

Abstract:

Drug design and discovery is a process that requires high financial costs and is timeconsuming. For many years, this process focused on empirical pharmacology. However, over the years, the target-based approach allowed a significant discovery in this field, initiating the rational design era. In view, to decrease the time and financial cost, rational drug design is benefited by increasing computer engineering and software development, and computer-aided drug design (CADD) emerges as a promising alternative. Since the 1970s, this approach has been able to identify many important and revolutionary compounds, like protease inhibitors, antibiotics, and others. Many anticancer compounds identified through this approach have shown their importance, being CADD essential in any drug discovery campaign. Thus, this perspective will present the prominent successful cases utilizing this approach and entering into the next stage of drug design. We believe that drug discovery will follow the progress in bioinformatics, using high-performance computing with molecular dynamics protocols faster and more effectively. In addition, artificial intelligence and machine learning will be the next process in the rational design of new drugs. Here, we hope that this paper generates new ideas and instigates research groups worldwide to use these methods and stimulate progress in drug design.

REDDA: Integrating multiple biological relations to heterogeneous graph neural network for drug-disease association prediction

Computational drug repositioning is an effective way to find new indications for existing drugs, thus can accelerate drug development and reduce experimental costs. Recently, various deep learning-based repurposing methods have been established to identify the potential drug-disease associations (DDA). However, effective utilization of the relations of biological entities to capture the biological interactions to enhance the drug-disease association prediction is still challenging. To resolve the above problem, we proposed a heterogeneous graph neural network called REDDA (Relations-Enhanced Drug-Disease Association prediction). Assembled with three attention mechanisms, REDDA can sequentially learn drug/disease representations by a general heterogeneous graph convolutional network-based node embedding block, a topological subnet embedding block, a graph attention block, and a layer attention block. Performance comparisons on our proposed benchmark dataset show that REDDA outperforms 8 advanced drug-disease association prediction methods, achieving relative improvements of 0.76% on the area under the receiver operating characteristic curve (AUC) score and 13.92% on the precision-recall curve (AUPR) score compared to the suboptimal method. On the other benchmark dataset, REDDA also obtains relative improvements of 2.48% on the AUC score and 4.93% on the AUPR score. Specifically, case studies also indicate that REDDA can give valid predictions for the discovery of -new indications for drugs and new therapies for diseases. The overall results provide an inspiring potential for REDDA in the in silico drug development. The proposed benchmark dataset and source code are available in https://github.com/gu-yaowen/REDDA.

Optimal COVID-19 therapeutic candidate discovery using the CANDO platform

The worldwide outbreak of SARS-CoV-2 in early 2020 caused numerous deaths and unprecedented measures to control its spread. We employed our Computational Analysis of Novel Drug Opportunities (CANDO) multiscale therapeutic discovery, repurposing, and design platform to identify small molecule inhibitors of the virus to treat its resulting indication, COVID-19. Initially, few experimental studies existed on SARS-CoV-2, so we optimized our drug candidate prediction pipelines using results from two independent high-throughput screens against prevalent human coronaviruses. Ranked lists of candidate drugs were generated using our open source cando.py software based on viral protein inhibition and proteomic interaction similarity. For the former viral protein inhibition pipeline, we computed interaction scores between all compounds in the corresponding candidate library and eighteen SARS-CoV proteins using an interaction scoring protocol with extensive parameter optimization which was then applied to the SARS-CoV-2 proteome for prediction. For the latter similarity based pipeline, we computed interaction scores between all compounds and human protein structures in our libraries then used a consensus scoring approach to identify candidates with highly similar proteomic interaction signatures to multiple known anti-coronavirus actives. We published our ranked candidate lists at the very beginning of the COVID-19 pandemic. Since then, 51 of our 276 predictions have demonstrated anti-SARS-CoV-2 activity in published clinical and experimental studies. These results illustrate the ability of our platform to rapidly respond to emergent pathogens and provide greater evidence that treating compounds in a multitarget context more accurately describes their behavior in biological systems.

regCOVID: Tracking publications of registered COVID-19 studies

Background

In response to the COVID-19 pandemic many clinical studies have been initiated leading to the need for efficient ways to track and analyze study results. We expanded our previous project that tracked registered COVID-19 clinical studies to also track result articles generated from these studies. Our objective was to develop a data science approach to identify and analyze all publications linked to COVID-19 clinical studies and generate a prioritized list of publications for efficient understanding of the state of COVID-19 clinical research.

Methods

We conducted searches of ClinicalTrials.gov and PubMed to identify articles linked to COVID-19 studies, and developed criteria based on the trial phase, intervention, location, and record recency to develop a prioritized list of result publications.

Results

The performed searchers resulted in 1 022 articles linked to 565 interventional trials (17.8% of all 3 167 COVID-19 interventional trials as of 31 January 2022). 609 publications were identified via abstract-link in PubMed and 413 via registry-link in ClinicalTrials.gov, with 27 articles linked from both sources. Of the 565 trials publishing at least one article, 197 (34.9%) had multiple linked publications. An attention score was assigned to each publication to develop a prioritized list of all publications linked to COVID-19 trials and 83 publications were identified that are result articles from late phase (Phase 3) trials with at least one US site and multiple study record updates. For COVID-19 vaccine trials, 108 linked result articles for 64 trials (14.7% of 436 total COVID-19 vaccine trials) were found.

Conclusions

Our method allows for the efficient identification of important COVID-19 articles that report results of registered clinical trials and are connected via a structured article-trial link. Our data science methodology also allows for consistent and as needed data updates and is generalizable to other conditions of interest.

Molecular dynamics and docking studies on potentially active natural phytochemicals for targeting SARS-CoV-2 main protease

In the present study, we screened eighty seven novel phytochemical compounds from four popular herbs, such as, Aegle Marmelos, Coleus Amboinicus, Aerva Lanata and Biophytum Sensitivum and identified the best three for targeting the main protease (M^pro) receptor of SARS-CoV-2. After categorizing all the phytochemicals based upon LibDock scores and hydrogen bonding interactions, the top ranked 26 compounds were further subjected for detailed Molecular dynamics (MD) study. From these screening we identified that Aegelinosides B leads the list with a high LibDock value of 142.50 (binding energy: -8.54 kcal/mol), which is better than several popular reference compounds namely, Tipranavir (LibDock score, 141.50), Saquinavir (125.34), Zopicole (122.9), Pirenepine (122.70), (115.37), Metixene (109.18), Oxiconazole Pimozide (138.00) and Rimonabant (91.88). Detailed analysis for structural stability (RMSD), Cα fluctuations (RMSF), intermolecular hydrogen bond interactions, effect of solvent accessibility (SASA) and compactness (Rg) factors were performed for the best six compounds and it is found that they are very stable and exhibit folding behavior. Apart from the docking and MD tests, through further drug-likeness and toxicity tests, three compounds, such as, Aegelinosides B, Epicatechin, and Feruloyltyramine (LibDock scores, respectively, 142.50, 124.33 and 129.06) can be suggested for fighting SARS-CoV-2.Communicated by Ramaswamy H. Sarma.

Misconceptions and associated factors of COVID-19 infection among internally displaced persons in Sudan

Coronavirus disease 2019 (COVID-19) is a global public health threat that has spread rapidly and caused morbidity and mortality worldwide. Reducing the myths about infectious diseases is vital for controlling transmission. This study explored the level of misconceptions and associated factors of COVID-19 among internally displaced persons in Sudan. This study is a cross-sectional, descriptive design and community-based study. We collected the data using a self-administered questionnaire via the convenience sampling technique among internally displaced persons in the camps of Zalingei town in the central Darfur region of Sudan. The total mean score of the respondents’ misconception was 3.1725 (SD=0.59) with 63.2%, indicating moderate misunderstanding of COVID- 19. Multiple linear regression revealed the independent variables together had a significant impact on a misconception, F(14,116)=2.429, p<0.005. The regression model explains 22.7% of the variance in misunderstanding. Analysis of the influence of single factors on the dependent variable showed that people aged 31–40 years had significantly higher levels of misconception, 0.381 (t=2.116, p<0.037), than those aged over 60 years, and university graduates had considerably lower levels of misunderstanding, −0.061 (t=−2.091, p<0.03) than non-graduates. This study found a moderate level of misconception of COVID-19. Non-graduates had higher levels of misunderstanding than graduates. The results suggest that an education campaign should focus on people with low levels of education to correct their misconceptions regarding the prevention of COVID-19 infection.

Potential COVID-19 Therapies from Computational Repurposing of Drugs and Natural Products against the SARS-CoV-2 Helicase

Repurposing of existing drugs is a rapid way to find potential new treatments for SARS-CoV-2. Here, we applied a virtual screening approach using Autodock Vina and molecular dynamic simulation in tandem to screen and calculate binding energies of repurposed drugs against the SARS-CoV-2 helicase protein (non-structural protein nsp13). Amongst the top hits from our study were antivirals, antihistamines, and antipsychotics, plus a range of other drugs. Approximately 30% of our top 87 hits had published evidence indicating in vivo or in vitro SARS-CoV-2 activity. Top hits not previously reported to have SARS-CoV-2 activity included the antiviral agents, cabotegravir and RSV-604; the NK1 antagonist, aprepitant; the trypanocidal drug, aminoquinuride; the analgesic, antrafenine; the anticancer intercalator, epirubicin; the antihistamine, fexofenadine; and the anticoagulant, dicoumarol. These hits from our in silico SARS-CoV-2 helicase screen warrant further testing as potential COVID-19 treatments.

Development and optimization of a high-throughput screening assay for in vitro anti-SARS-CoV-2 activity: Evaluation of 5676 Phase 1 Passed Structures

Although vaccines are currently used to control the coronavirus disease 2019 (COVID-19) pandemic, treatment options are urgently needed for those who cannot be vaccinated and for future outbreaks involving new severe acute respiratory syndrome coronavirus virus 2 (SARS-CoV-2) strains or coronaviruses not covered by current vaccines. Thus far, few existing antivirals are known to be effective against SARS-CoV-2 and clinically successful against COVID-19. As part of an immediate response to the COVID-19 pandemic, a high-throughput, high content imaging-based SARS-CoV-2 infection assay was developed in VeroE6 African green monkey kidney epithelial cells expressing a stable enhanced green fluorescent protein (VeroE6-eGFP cells) and was used to screen a library of 5676 compounds that passed Phase 1 clinical trials. Eight drugs (nelfinavir, RG-12915, itraconazole, chloroquine, hydroxychloroquine, sematilide, remdesivir, and doxorubicin) were identified as inhibitors of in vitro anti-SARS-CoV-2 activity in VeroE6-eGFP and/or Caco-2 cell lines. However, apart from remdesivir, toxicity and pharmacokinetic data did not support further clinical development of these compounds for COVID-19 treatment.

Network for network concept offers new insights into host- SARS-CoV-2 protein interactions and potential novel targets for developing antiviral drugs

SARS-CoV-2, the causal agent of COVID-19, is primarily a pulmonary virus that can directly or indirectly infect several organs. Despite many studies carried out during the current COVID-19 pandemic, some pathological features of SARS-CoV-2 have remained unclear. It has been recently attempted to address the current knowledge gaps on the viral pathogenicity and pathological mechanisms via cellular-level tropism of SARS-CoV-2 using human proteomics, visualization of virus-host protein-protein interactions (PPIs), and enrichment analysis of experimental results. The synergistic use of models and methods that rely on graph theory has enabled the visualization and analysis of the molecular context of virus/host PPIs. We review current knowledge on the SARS-COV-2/host interactome cascade involved in the viral pathogenicity through the graph theory concept and highlight the hub proteins in the intra-viral network that create a subnet with a small number of host central proteins, leading to cell disintegration and infectivity. Then we discuss the putative principle of the "gene-for-gene and "network for network" concepts as platforms for future directions toward designing efficient anti-viral therapies.

Memory augmented recurrent neural networks for de-novo drug design

A recurrent neural network (RNN) is a machine learning model that learns the relationship between elements of an input series, in addition to inferring a relationship between the data input to the model and target output. Memory augmentation allows the RNN to learn the interrelationships between elements of the input over a protracted length of the input series. Inspired by the success of stack augmented RNN (StackRNN) to generate strings for various applications, we present two memory augmented RNN-based architectures: the Neural Turing Machine (NTM) and the Differentiable Neural Computer (DNC) for the de-novo generation of small molecules. We trained a character-level convolutional neural network (CNN) to predict the properties of a generated string and compute a reward or loss in a deep reinforcement learning setup to bias the Generator to produce molecules with the desired property. Further, we compare the performance of these architectures to gain insight to their relative merits in terms of the validity and novelty of the generated molecules and the degree of property bias towards the computational generation of de-novo drugs. We also compare the performance of these architectures with simpler recurrent neural networks (Vanilla RNN, LSTM, and GRU) without an external memory component to explore the impact of augmented memory in the task of de-novo generation of small molecules.

Long-Acting Protective Ocular Surface by Instilling Adhesive Dual-Antiviral Nanoparticles

The eye is susceptible to viral infections, causing severe ocular symptoms or even respiratory diseases. Methods capable of protecting the eye from external viral invasion in a long-term and highly effective way are urgently needed but have been proved to be extremely challenging. Here, a strategy of forming a long-acting protective ocular surface is described by instilling adhesive dual-antiviral nanoparticles. Taking pseudotyped severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) as a model virus, antiviral agent-loaded nanoparticles are coated with a "double-lock" hybrid cell membrane abundant with integrin-β1 and angiotensin converting enzyme II (ACE2). After instillation, the presence of integrin-β1 endows coated nanoparticles with steady adhesion via specific binding to Arg-Gly-Asp sequence on the fibronectin of ocular epithelium, achieving durable retention on the ocular surface. In addition to loaded inhibitors, the exposure of ACE2 can trap SARS-CoV-2 and subsequently neutralize the associated spike protein, playing a dual antiviral effect of the resulting nanoparticles. Adhesive dual-antiviral nanoparticles enabled by coating with a "double-lock" hybrid cell membrane could be a versatile platform for topical long-acting protection against viral infection of the eye.

Drug Repurposing for COVID-19: A Review and a Novel Strategy to Identify New Targets and Potential Drug Candidates

In December 2019, the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of coronavirus disease 2019 (COVID-19) was first identified in the province of Wuhan, China. Since then, there have been over 400 million confirmed cases and 5.8 million deaths by COVID-19 reported worldwide. The urgent need for therapies against SARS-CoV-2 led researchers to use drug repurposing approaches. This strategy allows the reduction in risks, time, and costs associated with drug development. In many cases, a repurposed drug can enter directly to preclinical testing and clinical trials, thus accelerating the whole drug discovery process. In this work, we will give a general overview of the main developments in COVID-19 treatment, focusing on the contribution of the drug repurposing paradigm to find effective drugs against this disease. Finally, we will present our findings using a new drug repurposing strategy that identified 11 compounds that may be potentially effective against COVID-19. To our knowledge, seven of these drugs have never been tested against SARS-CoV-2 and are potential candidates for in vitro and in vivo studies to evaluate their effectiveness in COVID-19 treatment.

Design, Synthesis, and Development of 4‐[(7‐Chloroquinoline‐4‐yl)amino]phenol as a Potential SARS‐CoV‐2 Mpro Inhibitor

A series of chloroquine analogs were designed to search for a less toxic chloroquine derivative as a potential SARS‐CoV‐2 Mpro inhibitor. Herein, an ANN‐based QSAR model was built to predict the IC₅₀ values of each analog using the experimental values of other 4‐aminoquinolines as the training set. Subsequently, molecular docking was used to evaluate each analog's binding affinity to Mpro. The analog that showed the greatest affinity and lowest IC₅₀ values was synthesized and characterized for its posterior incorporation into a polycaprolactone‐based nanoparticulate system. After characterizing the loaded nanoparticles, an in vitro drug release assay was carried out, and the cytotoxicity of the analog and loaded nanoparticles was evaluated using murine fibroblast (L929) and human lung adenocarcinoma (A549) cell lines. Results show that the synthesized analog is much less toxic than chloroquine and that the nanoparticulate system allowed for the prolonged release of the analog without evidence of adverse effects on the cell lines used; therefore, suggesting that the analog could be a potential therapeutic option for COVID‐19.

Drug repositioning: Progress and challenges in drug discovery for various diseases

Compared with traditional de novo drug discovery, drug repurposing has become an attractive drug discovery strategy due to its low-cost and high efficiency. Through a comprehensive analysis of the candidates that have been identified with drug repositioning potentials, it is found that although some drugs do not show obvious advantages in the original indications, they may exert more obvious effects in other diseases. In addition, some drugs have a synergistic effect to exert better clinical efficacy if used in combination. Particularly, it has been confirmed that drug repositioning has benefits and values on the current public health emergency such as the COVID-19 pandemic, which proved the great potential of drug repositioning. In this review, we systematically reviewed a series of representative drugs that have been repositioned for different diseases and illustrated successful cases in each disease. Especially, the mechanism of action for the representative drugs in new indications were explicitly explored for each disease, we hope this review can provide important insights for follow-up research.

Prioritizing Global Public Health Investments for COVID-19 Response in Real Time: Results from a Delphi Exercise

In the first months of the COVID-19 pandemic, there was a lack of guidance on how to channel the unprecedented amount of health financing toward the pandemic response. We employed a multistep, interactive Delphi process to reach consensus on a “menu” of priority COVID-19 response interventions. In all, 27 health security experts—representing national governments, bilateral and multilateral organizations, academia, technical agencies, and nongovernmental organizations—participated in the exercise. The experts rated 11 technical investment areas and 37 interventions on a 5-point scale in terms of their importance to COVID-19 response. Initial findings were discussed at a virtual meeting where experts suggested modifications. A group of 19 experts then rated a revised list of 11 technical areas and 39 interventions. Consensus was defined as at least 80% of experts agreeing on the importance of a technical area or intervention; stability of scores across the rounds was identified using Wilcoxon matched-pairs and unpaired signed rank tests. Between the initial and final menu, 3 technical areas and 7 interventions were slightly modified, 3 interventions were added, and 1 intervention was removed. Consensus was reached on all 11 technical areas and 35 of the final 39 interventions, and between 34 and 37 interventions were stable across rounds depending on the test used. In this exercise, the health security experts agreed that COVID-19 response financing should prioritize interventions that enhance a country's capacity to test, trace, and treat high-risk populations. Simultaneously, supportive systems (eg, risk communication, community engagement, public health infrastructure, information systems, policy and coordination, workforce capacity, other social protections) should be developed to ensure that nonpharmaceutical and medical interventions can maximize the effectiveness of these systems.

A Benchmark Dataset for Evaluating Practical Performance of Model Quality Assessment of Homology Models

Protein structure prediction is an important issue in structural bioinformatics. In this process, model quality assessment (MQA), which estimates the accuracy of the predicted structure, is also practically important. Currently, the most commonly used dataset to evaluate the performance of MQA is the critical assessment of the protein structure prediction (CASP) dataset. However, the CASP dataset does not contain enough targets with high-quality models, and thus cannot sufficiently evaluate the MQA performance in practical use. Additionally, most application studies employ homology modeling because of its reliability. However, the CASP dataset includes models generated by de novo methods, which may lead to the mis-estimation of MQA performance. In this study, we created new benchmark datasets, named a homology models dataset for model quality assessment (HMDM), that contain targets with high-quality models derived using homology modeling. We then benchmarked the performance of the MQA methods using the new datasets and compared their performance to that of the classical selection based on the sequence identity of the template proteins. The results showed that model selection by the latest MQA methods using deep learning is better than selection by template sequence identity and classical statistical potentials. Using HMDM, it is possible to verify the MQA performance for high-accuracy homology models.

Task-driven knowledge graph filtering improves prioritizing drugs for repurposing

Background

Drug repurposing aims at finding new targets for already developed drugs. It becomes more relevant as the cost of discovering new drugs steadily increases. To find new potential targets for a drug, an abundance of methods and existing biomedical knowledge from different domains can be leveraged. Recently, knowledge graphs have emerged in the biomedical domain that integrate information about genes, drugs, diseases and other biological domains. Knowledge graphs can be used to predict new connections between compounds and diseases, leveraging the interconnected biomedical data around them. While real world use cases such as drug repurposing are only interested in one specific relation type, widely used knowledge graph embedding models simultaneously optimize over all relation types in the graph. This can lead the models to underfit the data that is most relevant for the desired relation type. For example, if we want to learn embeddings to predict links between compounds and diseases but almost the entirety of relations in the graph is incident to other pairs of entity types, then the resulting embeddings are likely not optimised to predict links between compounds and diseases. We propose a method that leverages domain knowledge in the form of metapaths and use them to filter two biomedical knowledge graphs (Hetionet and DRKG) for the purpose of improving performance on the prediction task of drug repurposing while simultaneously increasing computational efficiency.

Results

We find that our method reduces the number of entities by 60% on Hetionet and 26% on DRKG, while leading to an improvement in prediction performance of up to 40.8% on Hetionet and 14.2% on DRKG, with an average improvement of 20.6% on Hetionet and 8.9% on DRKG. Additionally, prioritization of antiviral compounds for SARS CoV-2 improves after task-driven filtering is applied.

Conclusion

Knowledge graphs contain facts that are counter productive for specific tasks, in our case drug repurposing. We also demonstrate that these facts can be removed, resulting in an improved performance in that task and a more efficient learning process.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-022-04608-y.

Stem cell therapy for COVID-19 pneumonia

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) virus is a highly contagious microorganism, and despite substantial investigation, no progress has been achieved in treating post-COVID complications. However, the virus has made various mutations and has spread around the world. Researchers have tried different treatments to reduce the side effects of the COVID-19 symptoms. One of the most common and effective treatments now used is steroid therapy to reduce the complications of this disease. Long-term steroid therapy for chronic inflammation following COVID-19 is harmful and increases the risk of secondary infection, and effective treatment remains challenging owing to fibrosis and severe inflammation and infection. Sometimes our immune system can severely damage ourselves in disease. In the past, many researchers have conducted various studies on the immunomodulatory properties of stem cells. This property of stem cells led them to modulate the immune system of autoimmune diseases like diabetes, multiple sclerosis, and Parkinson's. Because of their immunomodulatory properties, stem cell-based therapy employing mesenchymal or hematopoietic stem cells may be a viable alternative treatment option in some patients. By priming the immune system and providing cytokines, chemokines, and growth factors, stem cells can be employed to build a long-term regenerative and protective response. This review addresses the latest trends and rapid progress in stem cell treatment for Acute Respiratory Distress Syndrome (ARDS) following COVID-19.

Systematic virtual screening in search of SARS CoV-2 inhibitors against spike glycoprotein: pharmacophore screening, molecular docking, ADMET analysis and MD simulations

In the absence of efficient anti-viral medications, the coronavirus disease 2019 (COVID-19), stemming from severe acute respiratory syndrome coronavirus-2 (SARS CoV-2), has spawned a worldwide catastrophe and global emergency. Amidst several anti-viral targets of COVID-19, spike glycoprotein has been recognized as an essential target for the viral entry into the host cell. In the search of effective SARS CoV-2 inhibitors acting against spike glycoprotein, the virtual screening of 175,851 ligands from the 2020.1 Asinex BioDesign library has been performed using in silico tools like SiteMap analysis, pharmacophore-based screening, molecular docking using different levels of precision, such as high throughput virtual screening, standard precision and extra precision, followed by absorption, distribution, metabolism, excretion and toxicity analysis, and molecular dynamics (MD) simulation. Following a molecular docking study, seventeen molecules (with a docking score of less than - 6.0) were identified having the substantial interactions with the catalytic amino acid and nucleic acid residues of spike glycoprotein at the binding site. In investigations using MD simulations for 10 ns, the hit molecules (1 and 2) showed adequate compactness and uniqueness, as well as satisfactory stability. These computational research findings have offered a key starting point in the field of design and development of novel SARS CoV-2 entry inhibitors with appropriate drug likeliness.

Potential phytochemical inhibitors of SARS-CoV-2 helicase Nsp13: a molecular docking and dynamic simulation study

The SARS-CoV-2 helicase Nsp13 is a promising target for developing anti-COVID drugs. In the present study, we have identified potential natural product inhibitors of SARS-CoV-2 Nsp13 targeting the ATP-binding site using molecular docking and molecular dynamics (MD) simulations. MD simulation of the prepared crystal structure of SARS-CoV-2 Nsp13 was performed to generate an ensemble of structures of helicase Nsp13 capturing the conformational diversity of the ATP-binding site. A natural product library of more than 14,000 phytochemicals from Indian medicinal plants was used to perform virtual screening against the ensemble of Nsp13 structures. Subsequently, a two-stage filter, first based on protein-ligand docking binding energy value and second based on protein residues in the ligand-binding site and non-covalent interactions between the protein residues and the ligand in the best-docked pose, was used to identify 368 phytochemicals as potential inhibitors of SARS-CoV-2 helicase Nsp13. MD simulations of the top inhibitors complexed with protein were performed to confirm stable binding, and to compute MM-PBSA based binding energy. From among the 368 potential phytochemical inhibitors, the top identified potential inhibitors of SARS-CoV-2 helicase Nsp13 namely, Picrasidine M, (+)-Epiexcelsin, Isorhoeadine, Euphorbetin and Picrasidine N, can be taken up initially for experimental studies.

Structural biology of SARS-CoV-2: open the door for novel therapies

Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) is the causative agent of the pandemic disease COVID-19, which is so far without efficacious treatment. The discovery of therapy reagents for treating COVID-19 are urgently needed, and the structures of the potential drug-target proteins in the viral life cycle are particularly important. SARS-CoV-2, a member of the Orthocoronavirinae subfamily containing the largest RNA genome, encodes 29 proteins including nonstructural, structural and accessory proteins which are involved in viral adsorption, entry and uncoating, nucleic acid replication and transcription, assembly and release, etc. These proteins individually act as a partner of the replication machinery or involved in forming the complexes with host cellular factors to participate in the essential physiological activities. This review summarizes the representative structures and typically potential therapy agents that target SARS-CoV-2 or some critical proteins for viral pathogenesis, providing insights into the mechanisms underlying viral infection, prevention of infection, and treatment. Indeed, these studies open the door for COVID therapies, leading to ways to prevent and treat COVID-19, especially, treatment of the disease caused by the viral variants are imperative.

Artecanin of Laurus nobilis is a novel inhibitor of SARS-CoV-2 main protease with highly desirable druglikeness

Main protease (M^pro) is a critical enzyme in the life cycle of severe acute respiratory syndrome Coronavirus -2 (SARS-CoV-2). Due to its essential role in the maturation of the polyproteins, the necessity to inhibit M^pro is one of the essential means to prevent the outbreak of COVID-19. In this context, this study was conducted on the natural compounds of medicinal plants that are commonly available in the Middle East to find out the most potent one to inhibit M^pro with the best bioavailability and druglikeness properties. A total of 3392 compounds of sixty-six medicinal plants were retrieved from PubChem database and docked against M^pro. Thirty compounds with the highest docking scores with M^pro were chosen for further virtual screening. Variable druglikeness and toxicity potentials of these compounds were evaluated using SwissADME and Protox servers respectively. Out of these virtually screened compounds, artecanin was predicted to exhibit the most favourable druglikeness potentials, accompanied by no predicted hepatoxicity, carcinogenicity, mutagenicity, and cytotoxicity. Molecular dynamics (MD) simulations showed that M^pro-artecanin complex exhibited comparable stability with that observed in the ligand-free M^pro. This study revealed for the first time that artecanin from Laurus nobilis provided a novel static and dynamic inhibition for M^pro with excellent safety, oral bioavailability, and pharmacokinetic profile. This study suggested the ability of artecanin to be used as a potential natural inhibitor that can be used to block or at least counteract the SARS-CoV-2 invasion.Communicated by Ramaswamy H. Sarma.

Autoimmune complications of COVID-19

Coronavirus disease 2019 (COVID-19) is still propagating a year after the start of the pandemic. Besides the complications patients face during the COVID-19 disease period, there is an accumulating body of evidence concerning the late-onset complications of COVID-19, of which autoimmune manifestations have attracted remarkable attention from the first months of the pandemic. Autoimmune hemolytic anemia, immune thrombocytopenic purpura, autoimmune thyroid diseases, Kawasaki disease, Guillain-Barre syndrome, and the detection of autoantibodies are the cues to the discovery of the potential of COVID-19 in inducing autoimmunity. Clarification of the pathophysiology of COVID-19 injuries to the host, whether it is direct viral injury or autoimmunity, could help to develop appropriate treatment.

SARS-CoV-2 RNA-dependent RNA polymerase as a target for high-throughput drug screening

The ongoing COVID-19 pandemic caused by the SARS-CoV-2 has necessitated rapid development of drug screening tools. RNA-dependent RNA polymerase (RdRp) is a promising target due to its essential functions in replication and transcription of viral genome. To date, through minimal RNA synthesizing machinery established from cryo-electron microscopy structural data, there has been development of high-throughput screening assays for directly screening inhibitors that target the SARS-CoV-2 RdRp. Here, we analyze and present verified techniques that could be used to discover potential anti-RdRp agents or repurposing of approved drugs to target the SARS-CoV-2 RdRp. In addition, we highlight the characteristics and application value of cell-free or cell-based assays in drug discovery.

VDA-RWLRLS: An anti-SARS-CoV-2 drug prioritizing framework combining an unbalanced bi-random walk and Laplacian regularized least squares

BACKGROUND

A new coronavirus disease named COVID-19, caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), is rapidly spreading worldwide. However, there is currently no effective drug to fight COVID-19.

METHODS

In this study, we developed a Virus-Drug Association (VDA) identification framework (VDA-RWLRLS) combining unbalanced bi-Random Walk, Laplacian Regularized Least Squares, molecular docking, and molecular dynamics simulation to find clues for the treatment of COVID-19. First, virus similarity and drug similarity are computed based on genomic sequences, chemical structures, and Gaussian association profiles. Second, an unbalanced bi-random walk is implemented on the virus network and the drug network, respectively. Third, the results of the random walks are taken as the input of Laplacian regularized least squares to compute the association score for each virus-drug pair. Fourth, the final associations are characterized by integrating the predictions from the virus network and the drug network. Finally, molecular docking and molecular dynamics simulation are implemented to measure the potential of screened anti-COVID-19 drugs and further validate the predicted results.

RESULTS

In comparison with six state-of-the-art association prediction models (NGRHMDA, SMiR-NBI, LRLSHMDA, VDA-KATZ, VDA-RWR, and VDA-BiRW), VDA-RWLRLS demonstrates superior VDA prediction performance. It obtains the best AUCs of 0.885 8, 0.835 5, and 0.862 5 on the three VDA datasets. Molecular docking and dynamics simulations demonstrated that remdesivir and ribavirin may be potential anti-COVID-19 drugs.

CONCLUSIONS

Integrating unbalanced bi-random walks, Laplacian regularized least squares, molecular docking, and molecular dynamics simulation, this work initially screened a few anti-SARS-CoV-2 drugs and may contribute to preventing COVID-19 transmission.

In search of SARS CoV-2 replication inhibitors: Virtual screening, molecular dynamics simulations and ADMET analysis

Severe acute respiratory syndrome has relapsed recently as novel coronavirus causing a life threat to the entire world in the absence of an effective therapy. To hamper the replication of the deadly SARS CoV-2 inside the host cells, systematic in silico virtual screening of total 267,324 ligands from Asinex EliteSynergy and BioDesign libraries has been performed using AutoDock Vina against RdRp. The molecular modeling studies revealed the identification of twenty-one macrocyclic hits (2-22) with better binding energy than remdesivir (1), marketed SARS CoV-2 inhibitor. Further, the analysis using rules for drug-likeness and their ADMET profile revealed the candidature of these hits due to superior oral bioavailability and druggability. Further, the MD simulation studies of top two hits (2 and 3) performed using GROMACS 2020.1 for 10 ns revealed their stability into the docked complexes. These results provide an important breakthrough in the design of macrocyclic hits as SARS CoV-2 RNA replicase inhibitor.

Structural modification of antineoplastic drug carmofur designed to the inhibition of SARS-CoV-2 main protease: A theoretical investigation

A coherent account of the reaction mechanistic details, structural modifications, and inhibition potentials of antineoplastic drug carmofur and its modified analogs to inhibition of SARS-CoV-2 main protease (M^pro) is reported. The survey is performed by integrating the density functional based tight binding (DFTB3) with density functional theory (DFT) calculations. The inhibition process commences with nucleophilic attack from the sulfur atom on the carbonyl group, yielding a C-S bond formation, followed by a bond formation of the H-O9 by 2.07 Å, which results in a transition state contains a ring of six atoms. We found that although the direct addition of sulfhydryl group hydrogen to the N3 position is likely to happen, the proper position of the hydrogen to O9 decreases its accessibility. The thermodynamic stability of the complex was calculated to be highly sensitive to the substituent on the N11 position. Compounds with CH₂NH₂ and CH₂F at N11 positions of carmofur revealed high thermodynamic stability to complexation with M^pro but induced no change in substrate-binding pocket comparable to carmofur. Replacing the N11 of carmofur with carbon (C-carmofur) was effective in terms of complexation stability at CH₂CH₂CH₂F and CH₂CH₂CH₂OH substitutions and occupation of S1 subsite by these structures in addition to the S2 subsite. Based on the resulted data, increasing the length of the carbon chain at introduced substitutions in N-carmofur almost decreases the complexation stability while in C-carmofur the trend is reversed. Throughout these information outputs, it was suggested that compounds d, e, i′, and k′ might be novel and more efficacious drug candidates instead of carmofur. We believe that our characterization of mechanistic details and structural modification on M^pro/carmofur complex will significantly intensify researchers' understanding of this system, and consequently help them to take advantage of results into practice and design various valuable derivatives for inhibition of SARS-CoV-2 main protease.