Discovery and development of safe-in-man broad-spectrum antiviral agents

DeepSeq2Drug: An expandable ensemble end-to-end anti-viral drug repurposing benchmark framework by multi-modal embeddings and transfer learning

Drug repurposing is promising in multiple scenarios, such as emerging viral outbreak controls and cost reductions of drug discovery. Traditional graph-based drug repurposing methods are limited to fast, large-scale virtual screens, as they constrain the counts for drugs and targets and fail to predict novel viruses or drugs. Moreover, though deep learning has been proposed for drug repurposing, only a few methods have been used, including a group of pre-trained deep learning models for embedding generation and transfer learning. Hence, we propose DeepSeq2Drug to tackle the shortcomings of previous methods. We leverage multi-modal embeddings and an ensemble strategy to complement the numbers of drugs and viruses and to guarantee the novel prediction. This framework (including the expanded version) involves four modal types: six NLP models, four CV models, four graph models, and two sequence models. In detail, we first make a pipeline and calculate the predictive performance of each pair of viral and drug embeddings. Then, we select the best embedding pairs and apply an ensemble strategy to conduct anti-viral drug repurposing. To validate the effect of the proposed ensemble model, a monkeypox virus (MPV) case study is conducted to reflect the potential predictive capability. This framework could be a benchmark method for further pre-trained deep learning optimization and anti-viral drug repurposing tasks. We also build software further to make the proposed model easier to reuse. The code and software are freely available at http://deepseq2drug.cs.cityu.edu.hk.

HKFGCN: A novel multiple kernel fusion framework on graph convolutional network to predict microbe-drug associations

Accumulating clinical studies have consistently demonstrated that the microbes in the human body closely interact with the human host, actively participating in the regulation of drug effectiveness. Identifying the associations between microbes and drugs can facilitate the development of drug discovery, and microbes have become a new target in antimicrobial drug development. However, the discovery of microbe-drug associations relies on clinical or biological experiments, which are not only time-consuming but also financially burdensome. Thus, the utilization of computational methods to predict microbe-drug associations holds promise for reducing costs and enhancing the efficiency of biological experiments. Here, we introduce a new computational method, called HKFGCN (Heterogeneous information Kernel Fusion Graph Convolution Network), to predict the microbe-drug associations. Instead of extracting feature from a single network in previous studies, HKFGCN separately extracts topological information features from different networks, and further refines them by generating Gaussian kernel features. HKFGCN consists of three main steps. Firstly, we constructed two similarity networks and a microbe-drug association network based on numerous biological data. Second, we employed two types of encoders to extract features from these networks. Next, Gaussian kernel features were obtained from the drug and microbe features at each layer. Finally, we reconstructed the bipartite microbe-drug graph based on the learned representations. Experimental results demonstrate the excellent performance of the HKFGCN model across different datasets using the cross-validation scheme. Additionally, we conduced case studies on human immunodeficiency virus, and the results were corroborated by existing literatures. The prediction model's code is available at https://github.com/roll-of-bubble/HKFGCN.

Prediction of microbe-drug associations based on a modified graph attention variational autoencoder and random forest

Introduction

The identification of microbe-drug associations can greatly facilitate drug research and development. Traditional methods for screening microbe-drug associations are time-consuming, manpower-intensive, and costly to conduct, so computational methods are a good alternative. However, most of them ignore the combination of abundant sequence, structural information, and microbe-drug network topology.

Methods

In this study, we developed a computational framework based on a modified graph attention variational autoencoder (MGAVAEMDA) to infer potential microbedrug associations by combining biological information with the variational autoencoder. In MGAVAEMDA, we first used multiple databases, which include microbial sequences, drug structures, and microbe-drug association databases, to establish two comprehensive feature matrices of microbes and drugs after multiple similarity computations, fusion, smoothing, and thresholding. Then, we employed a combination of variational autoencoder and graph attention to extract low-dimensional feature representations of microbes and drugs. Finally, the lowdimensional feature representation and graphical adjacency matrix were input into the random forest classifier to obtain the microbe-drug association score to identify the potential microbe-drug association. Moreover, in order to correct the model complexity and redundant calculation to improve efficiency, we introduced a modified graph convolutional neural network embedded into the variational autoencoder for computing low dimensional features.

Results

The experiment results demonstrate that the prediction performance of MGAVAEMDA is better than the five state-of-the-art methods. For the major measurements (AUC =0.9357, AUPR =0.9378), the relative improvements of MGAVAEMDA compared to the suboptimal methods are 1.76 and 1.47%, respectively.

Discussion

We conducted case studies on two drugs and found that more than 85% of the predicted associations have been reported in PubMed. The comprehensive experimental results validated the reliability of our models in accurately inferring potential microbe-drug associations.

Isolation, structural determination, and antiviral activities of a novel alanine-conjugated polyketide from Talaromyces sp

Antiviral agents are highly sought after. In this study, a novel alkylated decalin-type polyketide, alaspelunin, was isolated from the culture broth of the fungus Talaromyces speluncarum FMR 16671, and its structure was determined using spectroscopic analyses (1D/2D NMR and MS). The compound was condensed with alanine, and its absolute configuration was determined using Marfey's method. Furthermore, the antiviral activity of alaspelunin against various viruses was evaluated, and it was found to be effective against both severe acute respiratory syndrome coronavirus 2 and pseudorabies (Aujeszky's disease) virus, a pathogen affecting pigs. Our results suggest that this compound is a potential broad-spectrum antiviral agent.

LCASPMDA: a computational model for predicting potential microbe-drug associations based on learnable graph convolutional attention networks and self-paced iterative sampling ensemble

Introduction

Numerous studies show that microbes in the human body are very closely linked to the human host and can affect the human host by modulating the efficacy and toxicity of drugs. However, discovering potential microbe-drug associations through traditional wet labs is expensive and time-consuming, hence, it is important and necessary to develop effective computational models to detect possible microbe-drug associations.

Methods

In this manuscript, we proposed a new prediction model named LCASPMDA by combining the learnable graph convolutional attention network and the self-paced iterative sampling ensemble strategy to infer latent microbe-drug associations. In LCASPMDA, we first constructed a heterogeneous network based on newly downloaded known microbe-drug associations. Then, we adopted the learnable graph convolutional attention network to learn the hidden features of nodes in the heterogeneous network. After that, we utilized the self-paced iterative sampling ensemble strategy to select the most informative negative samples to train the Multi-Layer Perceptron classifier and put the newly-extracted hidden features into the trained MLP classifier to infer possible microbe-drug associations.

Results and discussion

Intensive experimental results on two different public databases including the MDAD and the aBiofilm showed that LCASPMDA could achieve better performance than state-of-the-art baseline methods in microbe-drug association prediction.

OGNNMDA: a computational model for microbe-drug association prediction based on ordered message-passing graph neural networks

In recent years, many excellent computational models have emerged in microbe-drug association prediction, but their performance still has room for improvement. This paper proposed the OGNNMDA framework, which applied an ordered message-passing mechanism to distinguish the different neighbor information in each message propagation layer, and it achieved a better embedding ability through deeper network layers. Firstly, the method calculates four similarity matrices based on microbe functional similarity, drug chemical structure similarity, and their respective Gaussian interaction profile kernel similarity. After integrating these similarity matrices, it concatenates the integrated similarity matrix with the known association matrix to obtain the microbe-drug heterogeneous matrix. Secondly, it uses a multi-layer ordered message-passing graph neural network encoder to encode the heterogeneous network and the known association information adjacency matrix, thereby obtaining the final embedding features of the microbe-drugs. Finally, it inputs the embedding features into the bilinear decoder to get the final prediction results. The OGNNMDA method performed comparative experiments, ablation experiments, and case studies on the aBiofilm, MDAD and DrugVirus datasets using 5-fold cross-validation. The experimental results showed that OGNNMDA showed the strongest prediction performance on aBiofilm and MDAD and obtained sub-optimal results on DrugVirus. In addition, the case studies on well-known drugs and microbes also support the effectiveness of the OGNNMDA method. Source codes and data are available at: https://github.com/yyzg/OGNNMDA.

Two-way pharmacodynamic modeling of drug combinations and its application to pairs of repurposed Ebola and SARS-CoV-2 agents

Existing pharmacodynamic (PD) mathematical models for drug combinations discriminate antagonistic, additive, multiplicative, and synergistic effects, but fail to consider how concentration-dependent drug interaction effects may vary across an entire dose-response matrix. We developed a two-way pharmacodynamic (TWPD) model to capture the PD of two-drug combinations. TWPD captures interactions between upstream and downstream drugs that act on different stages of viral replication, by quantifying upstream drug efficacy and concentration-dependent effects on downstream drug pharmacodynamic parameters. We applied TWPD to previously published in vitro drug matrixes for repurposed potential anti-Ebola and anti-SARS-CoV-2 drug pairs. Depending on the drug pairing, the model recapitulated combined efficacies as or more accurately than existing models and can be used to infer efficacy at untested drug concentrations. TWPD fits the data slightly better in one direction for all drug pairs, meaning that we can tentatively infer the upstream drug. Based on its high accuracy, TWPD could be used in concert with PK models to estimate the therapeutic effects of drug pairs in vivo.

The combination of pleconaril, rupintrivir, and remdesivir efficiently inhibits enterovirus infections in vitro, delaying the development of drug-resistant virus variants

Enteroviruses are a significant global health concern, causing a spectrum of diseases from the common cold to more severe conditions like hand-foot-and-mouth disease, meningitis, myocarditis, pancreatitis, and poliomyelitis. Current treatment options for these infections are limited, underscoring the urgent need for effective therapeutic strategies. To find better treatment option we analyzed toxicity and efficacy of 12 known broad-spectrum anti-enterovirals both individually and in combinations against different enteroviruses in vitro. We identified several novel, synergistic two-drug and three-drug combinations that demonstrated significant inhibition of enterovirus infections in vitro. Specifically, the triple-drug combination of pleconaril, rupintrivir, and remdesivir exhibited remarkable efficacy against echovirus (EV) 1, EV6, EV11, and coxsackievirus (CV) B5, in human lung epithelial A549 cells. This combination surpassed the effectiveness of single-agent or dual-drug treatments, as evidenced by its ability to protect A549 cells from EV1-induced cytotoxicity across seven passages. Additionally, this triple-drug cocktail showed potent antiviral activity against EV-A71 in human intestinal organoids. Thus, our findings highlight the therapeutic potential of the pleconaril-rupintrivir-remdesivir combination as a broad-spectrum treatment option against a range of enterovirus infections. The study also paves the way towards development of strategic antiviral drug combinations with virus family coverage and high-resistance barriers.

A novel microbe-drug association prediction model based on graph attention networks and bilayer random forest

BACKGROUND

In recent years, the extensive use of drugs and antibiotics has led to increasing microbial resistance. Therefore, it becomes crucial to explore deep connections between drugs and microbes. However, traditional biological experiments are very expensive and time-consuming. Therefore, it is meaningful to develop efficient computational models to forecast potential microbe-drug associations.

RESULTS

In this manuscript, we proposed a novel prediction model called GARFMDA by combining graph attention networks and bilayer random forest to infer probable microbe-drug correlations. In GARFMDA, through integrating different microbe-drug-disease correlation indices, we constructed two different microbe-drug networks first. And then, based on multiple measures of similarity, we constructed a unique feature matrix for drugs and microbes respectively. Next, we fed these newly-obtained microbe-drug networks together with feature matrices into the graph attention network to extract the low-dimensional feature representations for drugs and microbes separately. Thereafter, these low-dimensional feature representations, along with the feature matrices, would be further inputted into the first layer of the Bilayer random forest model to obtain the contribution values of all features. And then, after removing features with low contribution values, these contribution values would be fed into the second layer of the Bilayer random forest to detect potential links between microbes and drugs.

CONCLUSIONS

Experimental results and case studies show that GARFMDA can achieve better prediction performance than state-of-the-art approaches, which means that GARFMDA may be a useful tool in the field of microbe-drug association prediction in the future. Besides, the source code of GARFMDA is available at https://github.com/KuangHaiYue/GARFMDA.git.

Antiviral effects of micafungin against pteropine orthoreovirus, an emerging zoonotic virus carried by bats

Bat-borne emerging zoonotic viruses cause major outbreaks, such as the Ebola virus, Nipah virus, and/or beta coronavirus. Pteropine orthoreovirus (PRV), whose spillover event occurred from fruits bats to humans, causes respiratory syndrome in humans widely in South East Asia. Repurposing approved drugs against PRV is an effective tool to confront future PRV pandemics. We screened 2,943 compounds in an FDA-approved drug library and identified eight hit compounds that reduce viral cytopathic effects on cultured Vero cells. Real-time quantitative PCR analysis revealed that six of eight hit compounds significantly inhibited PRV replication. Among them, micafungin used clinically as an antifungal drug, displayed a prominent antiviral effect on PRV. Secondly, the antiviral effects of micafungin on PRV infected human cell lines (HEK293T and A549), and their transcriptome changes by PRV infection were investigated, compared to four different bat-derived cell lines (FBKT1 (Ryukyu flying fox), DEMKT1 (Leschenault's rousette), BKT1 (Greater horseshoe bat), YUBFKT1 (Eastern bent-wing bats)). In two human cell lines, unlike bat cells that induce an IFN-γ response pathway, an endoplasmic reticulum stress response pathway was commonly activated. Additionally, micafungin inhibits viral release rather than suppressing PRV genome replication in human cells, although it was disturbed in Vero cells. The target of micafungin's action may vary depending on the animal species, but it must be useful for human purposes as a first choice of medical care.

An Update on SARS-CoV-2 Clinical Trial Results-What We Can Learn for the Next Pandemic

The coronavirus disease 2019 (COVID-19) pandemic has claimed over 7 million lives worldwide, providing a stark reminder of the importance of pandemic preparedness. Due to the lack of approved antiviral drugs effective against coronaviruses at the start of the pandemic, the world largely relied on repurposed efforts. Here, we summarise results from randomised controlled trials to date, as well as selected in vitro data of directly acting antivirals, host-targeting antivirals, and immunomodulatory drugs. Overall, repurposing efforts evaluating directly acting antivirals targeting other viral families were largely unsuccessful, whereas several immunomodulatory drugs led to clinical improvement in hospitalised patients with severe disease. In addition, accelerated drug discovery efforts during the pandemic progressed to multiple novel directly acting antivirals with clinical efficacy, including small molecule inhibitors and monoclonal antibodies. We argue that large-scale investment is required to prepare for future pandemics; both to develop an arsenal of broad-spectrum antivirals beyond coronaviruses and build worldwide clinical trial networks that can be rapidly utilised.

Antiviral Potential of Azelastine against Major Respiratory Viruses

The Coronavirus Disease 2019 (COVID-19) pandemic and the subsequent increase in respiratory viral infections highlight the need for broad-spectrum antivirals to enable a quick and efficient reaction to current and emerging viral outbreaks. We previously demonstrated that the antihistamine azelastine hydrochloride (azelastine-HCl) exhibited in vitro antiviral activity against SARS-CoV-2. Furthermore, in a phase 2 clinical study, a commercial azelastine-containing nasal spray significantly reduced the viral load in SARS-CoV-2-infected individuals. Here, we evaluate the efficacy of azelastine-HCl against additional human coronaviruses, including the SARS-CoV-2 omicron variant and a seasonal human coronavirus, 229E, through in vitro infection assays, with azelastine showing a comparable potency against both. Furthermore, we determined that azelastine-HCl also inhibits the replication of Respiratory syncytial virus A (RSV A) in both prophylactic and therapeutic settings. In a human 3D nasal tissue model (MucilAirTM-Pool, Epithelix), azelastine-HCl protected tissue integrity and function from the effects of infection with influenza A H1N1 and resulted in a reduced viral load soon after infection. Our results suggest that azelastine-HCl has a broad antiviral effect and can be considered a safe option against the most common respiratory viruses to prevent or treat such infections locally in the form of a nasal spray that is commonly available globally.

Joint deep autoencoder and subgraph augmentation for inferring microbial responses to drugs

Exploring microbial stress responses to drugs is crucial for the advancement of new therapeutic methods. While current artificial intelligence methodologies have expedited our understanding of potential microbial responses to drugs, the models are constrained by the imprecise representation of microbes and drugs. To this end, we combine deep autoencoder and subgraph augmentation technology for the first time to propose a model called JDASA-MRD, which can identify the potential indistinguishable responses of microbes to drugs. In the JDASA-MRD model, we begin by feeding the established similarity matrices of microbe and drug into the deep autoencoder, enabling to extract robust initial features of both microbes and drugs. Subsequently, we employ the MinHash and HyperLogLog algorithms to account intersections and cardinality data between microbe and drug subgraphs, thus deeply extracting the multi-hop neighborhood information of nodes. Finally, by integrating the initial node features with subgraph topological information, we leverage graph neural network technology to predict the microbes' responses to drugs, offering a more effective solution to the 'over-smoothing' challenge. Comparative analyses on multiple public datasets confirm that the JDASA-MRD model's performance surpasses that of current state-of-the-art models. This research aims to offer a more profound insight into the adaptability of microbes to drugs and to furnish pivotal guidance for drug treatment strategies. Our data and code are publicly available at: https://github.com/ZZCrazy00/JDASA-MRD.

Weighted hypergraph learning and adaptive inductive matrix completion for SARS-CoV-2 drug repositioning

MOTIVATION

The outbreak of the human coronavirus (SARS-CoV-2) has placed a huge burden on public health and the world economy. Compared with de novo drug discovery, drug repurposing is a promising therapeutic strategy that facilitates rapid clinical treatment decisions, shortens the development process, and reduces costs.

RESULTS

In this study, we propose a weighted hypergraph learning and adaptive inductive matrix completion method, WHAIMC, for predicting potential virus-drug associations. Firstly, we integrate multi-source data to describe viruses and drugs from multiple perspectives, including drug chemical structures, drug targets, virus complete genome sequences, and virus-drug associations. Then, WHAIMC establishes an adaptive inductive matrix completion model to improve performance through adaptive learning of similarity relations. Finally, WHAIMC introduces weighted hypergraph learning into adaptive inductive matrix completion to capture higher-order relationships of viruses (or drugs). The results showed that WHAIMC had a strong predictive performance for new virus-drug associations, new viruses, and new drugs. The case study further demonstrates that WHAIMC is highly effective for repositioning antiviral drugs against SARS-CoV-2 and provides a new perspective for virus-drug association prediction. The code and data in this study is freely available at https://github.com/Mayingjun20179/WHAIMC.

Combating pan-coronavirus infection by indomethacin through simultaneously inhibiting viral replication and inflammatory response

Severe infections with coronaviruses are often accompanied with hyperinflammation, requiring therapeutic strategies to simultaneously tackle the virus and inflammation. By screening a safe-in-human broad-spectrum antiviral agents library, we identified that indomethacin can inhibit pan-coronavirus infection in human cell and airway organoids models. Combining indomethacin with oral antiviral drugs authorized for treating COVID-19 results in synergistic anti-coronavirus activity. Coincidentally, screening a library of FDA-approved drugs identified indomethacin as the most potent potentiator of interferon response through increasing STAT1 phosphorylation. Combining indomethacin with interferon-alpha exerted synergistic antiviral effects against multiple coronaviruses. The anti-coronavirus activity of indomethacin is associated with activating interferon response. In a co-culture system of lung epithelial cells with macrophages, indomethacin inhibited both viral replication and inflammatory response. Collectively, indomethacin is a pan-coronavirus inhibitor that can simultaneously inhibit virus-triggered inflammatory response. The therapeutic potential of indomethacin can be further augmented by combining it with oral antiviral drugs or interferon-alpha.

Hylin-a1: A Pan-Inhibitor against Emerging and Re-Emerging Respiratory Viruses

Pandemic and epidemic outbreaks of respiratory viruses are a challenge for public health and social care system worldwide, leading to high mortality and morbidity among the human populations. In light of the limited efficacy of current vaccines and antiviral drugs against respiratory viral infections and the emergence and re-emergence of new viruses, novel broad-spectrum antiviral drugs are needed for the prevention and treatment of these infections. Antimicrobial peptides with an antiviral effect, also known as AVPs, have already been reported as potent inhibitors of viral infections by affecting different stages of the virus lifecycle. In the present study, we analyzed the activity of the AVP Hylin-a1, secreted by the frog Hypsiboas albopunctatus, against a wide range of respiratory viruses, including the coronaviruses HCoV-229E and SARS-CoV-2, measles virus, human parainfluenza virus type 3, and influenza virus H1N1. We report a significant inhibitory effect on infectivity in all the enveloped viruses, whereas there was a lack of activity against the naked coxsackievirus B3. Considering the enormous therapeutic potential of Hylin-a1, further experiments are required to elucidate its mechanism of action and to increase its stability by modifying the native sequence.

Design and Synthesis of Novel 5-((3-(Trifluoromethyl)piperidin-1-yl)sulfonyl)indoline-2,3-dione Derivatives as Promising Antiviral Agents: In Vitro, In Silico, and Structure-Activity Relationship Studies

Herein, a series of new isatin derivatives was designed and synthesized (1-9) as broad-spectrum antiviral agents. Consequently, the antiviral activities of the synthesized compounds (1-9) were pursued against three viruses, namely influenza virus (H1N1), herpes simplex virus 1 (HSV-1), and coxsackievirus B3 (COX-B3). In particular, compounds 9, 5, and 4 displayed the highest antiviral activity against H1N1, HSV-1, and COX-B3 with IC50 values of 0.0027, 0.0022, and 0.0092 µM, respectively. Compound 7 was the safest, with a CC50 value of 315,578.68 µM. Moreover, a quantitative PCR (real-time PCR) assay was carried out for the most relevant compounds. The selected compounds exhibited a decrease in viral gene expression. Additionally, the conducted in silico studies emphasized the binding affinities of the synthesized compounds and their reliable pharmacokinetic properties as well. Finally, a structure-antiviral activity relationship study was conducted to anticipate the antiviral activity change upon future structural modification.

Nucleoside Derivatives of 2,6-Diaminopurine Antivirals: Base-Modified Nucleosides with Broad-Spectrum Antimicrobial Properties

The plethora of viral outbreaks experienced in the last decade, together with the widespread distribution of many re-emerging and newly emerging viruses, emphasize the urgent need for novel broad-spectrum antivirals as tools for early intervention in case of future epidemics. Non-natural nucleosides have been at the forefront for the treatment of infectious diseases for many years and still represent one of the most successful classes of antiviral molecules on the market. In the attempt to explore the biologically relevant chemical space of this class of antimicrobials, we describe herein the development of novel base-modified nucleosides by converting previously identified 2,6-diaminopurine antivirals into the corresponding D/L ribonucleosides, acyclic nucleosides and prodrug derivatives. A phenotypic screening against viruses belonging to different families (Flaviviridae, Coronaviridae, Retroviridae) and against a panel of Gram-positive and Gram-negative bacteria, allowed to identify a few interesting molecules with broad-spectrum antimicrobial activities.

Antiviral foods in the battle against viral infections: Understanding the molecular mechanism

Viruses produce a variety of illnesses, which may also cause acute respiratory syndrome. All viral infections, including COVID-19, are associated with the strength of the immune system. Till now, traditional medicine or vaccines for most viral diseases have not been effective. Antiviral and immune-boosting diets may provide defense against viral diseases by lowering the risk of infection and assisting rapid recovery. The purpose of this review was to gather, analyze, and present data based on scientific evidence in order to provide an overview of the mechanistic insights of antiviral bioactive metabolites. We have covered a wide range of food with antiviral properties in this review, along with their potential mechanism of action against viral infections. Additionally, the opportunities and challenges of using antiviral food have been critically reviewed. Bioactive plant compounds, not only help in maintaining the body's normal physiological mechanism and good health but are also essential for improving the body's immunity and therefore can be effective against viral diseases. These agents fight viral diseases either by incorporating the body's defense mechanism or by enhancing the cell's immune system. Regular intake of antiviral foods may prevent future pandemic and consumption of these antiviral agents with traditional medicine may reduce the severity of viral diseases. Therefore, the synergistic effect of antiviral foods and medication needs to be investigated.

Updates on drug designing approach through computational strategies: a review

The drug discovery and development (DDD) process in pursuit of novel drug candidates is a challenging procedure requiring lots of time and resources. Therefore, computer-aided drug design (CADD) methodologies are used extensively to promote proficiency in drug development in a systematic and time-effective manner. The point in reference is SARS-CoV-2 which has emerged as a global pandemic. In the absence of any confirmed drug moiety to treat the infection, the science fraternity adopted hit and trial methods to come up with a lead drug compound. This article is an overview of the virtual methodologies, which assist in finding novel hits and help in the progression of drug development in a short period with a specific medicinal solution.

A novel microbe-drug association prediction model based on stacked autoencoder with multi-head attention mechanism

Microbes are intimately tied to the occurrence of various diseases that cause serious hazards to human health, and play an essential role in drug discovery, clinical application, and drug quality control. In this manuscript, we put forward a novel prediction model named MDASAE based on a stacked autoencoder (SAE) with multi-head attention mechanism to infer potential microbe-drug associations. In MDASAE, we first constructed three kinds of microbe-related and drug-related similarity matrices based on known microbe-disease-drug associations respectively. And then, we fed two kinds of microbe-related and drug-related similarity matrices respectively into the SAE to learn node attribute features, and introduced a multi-head attention mechanism into the output layer of the SAE to enhance feature extraction. Thereafter, we further adopted the remaining microbe and drug similarity matrices to derive inter-node features by using the Restart Random Walk algorithm. After that, the node attribute features and inter-node features of microbes and drugs would be fused together to predict scores of possible associations between microbes and drugs. Finally, intensive comparison experiments and case studies based on different well-known public databases under 5-fold cross-validation and 10-fold cross-validation respectively, proved that MDASAE can effectively predict the potential microbe-drug associations.

Prediction of potential drug-microbe associations based on matrix factorization and a three-layer heterogeneous network

Microbes in the human body are closely linked to many complex human diseases and are emerging as new drug targets. These microbes play a crucial role in drug development and disease treatment. Traditional methods of biological experiments are not only time-consuming but also costly. Using computational methods to predict microbe-drug associations can effectively complement biological experiments. In this experiment, we constructed heterogeneity networks for drugs, microbes, and diseases using multiple biomedical data sources. Then, we developed a model with matrix factorization and a three-layer heterogeneous network (MFTLHNMDA) to predict potential drug-microbe associations. The probability of microbe-drug association was obtained by a global network-based update algorithm. Finally, the performance of MFTLHNMDA was evaluated in the framework of leave-one-out cross-validation (LOOCV) and 5-fold cross-validation (5-fold CV). The results showed that our model performed better than six state-of-the-art methods that had AUC of 0.9396 and 0.9385 + /- 0.0000, respectively. This case study further confirms the effectiveness of MFTLHNMDA in identifying potential drug-microbe associations and new drug-microbe associations.

Predicting microbe-drug associations with structure-enhanced contrastive learning and self-paced negative sampling strategy

MOTIVATION

Predicting the associations between human microbes and drugs (MDAs) is one critical step in drug development and precision medicine areas. Since discovering these associations through wet experiments is time-consuming and labor-intensive, computational methods have already been an effective way to tackle this problem. Recently, graph contrastive learning (GCL) approaches have shown great advantages in learning the embeddings of nodes from heterogeneous biological graphs (HBGs). However, most GCL-based approaches don't fully capture the rich structure information in HBGs. Besides, fewer MDA prediction methods could screen out the most informative negative samples for effectively training the classifier. Therefore, it still needs to improve the accuracy of MDA predictions.

RESULTS

In this study, we propose a novel approach that employs the Structure-enhanced Contrastive learning and Self-paced negative sampling strategy for Microbe-Drug Association predictions (SCSMDA). Firstly, SCSMDA constructs the similarity networks of microbes and drugs, as well as their different meta-path-induced networks. Then SCSMDA employs the representations of microbes and drugs learned from meta-path-induced networks to enhance their embeddings learned from the similarity networks by the contrastive learning strategy. After that, we adopt the self-paced negative sampling strategy to select the most informative negative samples to train the MLP classifier. Lastly, SCSMDA predicts the potential microbe-drug associations with the trained MLP classifier. The embeddings of microbes and drugs learning from the similarity networks are enhanced with the contrastive learning strategy, which could obtain their discriminative representations. Extensive results on three public datasets indicate that SCSMDA significantly outperforms other baseline methods on the MDA prediction task. Case studies for two common drugs could further demonstrate the effectiveness of SCSMDA in finding novel MDA associations.

AVAILABILITY

The source code is publicly available on GitHub https://github.com/Yue-Yuu/SCSMDA-master.

Tyrosine Kinase Inhibitors Target B Lymphocytes

Autoimmune disorders and some types of blood cancer originate when B lymphocytes malfunction. In particular, when B cells produce antibodies recognizing the body’s proteins, it leads to various autoimmune disorders. Additionally, when B cells of various developmental stages transform into cancer cells, it results in blood cancers, including multiple myeloma, lymphoma, and leukemia. Thus, new methods of targeting B cells are required for various patient groups. Here, we used protein kinase inhibitors alectinib, brigatinib, ceritinib, crizotinib, entrectinib, and lorlatinib previously approved as drugs treating anaplastic lymphoma kinase (ALK)-positive lung cancer cells. We hypothesized that the same inhibitors will efficiently target leukocyte tyrosine kinase (LTK)-positive, actively protein-secreting mature B lymphocytes, including plasma cells. We isolated CD19-positive human B cells from the blood of healthy donors and used two alternative methods to stimulate cell maturation toward plasma cells. Using cell proliferation and flow cytometry assays, we found that ceritinib and entrectinib eliminate plasma cells from B cell populations. Alectinib, brigatinib, and crizotinib also inhibited B cell proliferation, while lorlatinib had no or limited effect on B cells. More generally, we concluded that several drugs previously developed to treat ALK-positive malignant cells can be also used to treat LTK-positive B cells.

Revisiting Cryptocyanine Dye, NK-4, as an Old and New Drug: Review and Future Perspectives

NK-4 plays a key role in the treatment of various diseases, such as in hay fever to expect anti-allergic effects, in bacterial infections and gum abscesses to expect anti-inflammatory effects, in scratches, cuts, and mouth sores from bites inside the mouth for enhanced wound healing, in herpes simplex virus (HSV)-1 infections for antiviral effects, and in peripheral nerve disease that causes tingling pain and numbness in hands and feet, while NK-4 is used also to expect antioxidative and neuroprotective effects. We review all therapeutic directions for the cyanine dye NK-4, as well as the pharmacological mechanism of NK-4 in animal models of related diseases. Currently, NK-4, which is sold as an over-the-counter drug in drugstores, is approved for treating allergic diseases, loss of appetite, sleepiness, anemia, peripheral neuropathy, acute suppurative diseases, wounds, heat injuries, frostbite, and tinea pedis in Japan. The therapeutic effects of NK-4's antioxidative and neuroprotective properties in animal models are now under development, and we hope to apply these pharmacological effects of NK-4 to the treatment of more diseases. All experimental data suggest that different kinds of utility of NK-4 in the treatment of diseases can be developed based on the various pharmacological properties of NK-4. It is expected that NK-4 could be developed in more therapeutic strategies to treat many types of diseases, such as neurodegenerative and retinal degenerative diseases.

GACNNMDA: a computational model for predicting potential human microbe-drug associations based on graph attention network and CNN-based classifier

As new drug targets, human microbes are proven to be closely related to human health. Effective computational methods for inferring potential microbe-drug associations can provide a useful complement to conventional experimental methods and will facilitate drug research and development. However, it is still a challenging work to predict potential interactions for new microbes or new drugs, since the number of known microbe-drug associations is very limited at present. In this manuscript, we first constructed two heterogeneous microbe-drug networks based on multiple measures of similarity of microbes and drugs, and known microbe-drug associations or known microbe-disease-drug associations, respectively. And then, we established two feature matrices for microbes and drugs through concatenating various attributes of microbes and drugs. Thereafter, after taking these two feature matrices and two heterogeneous microbe-drug networks as inputs of a two-layer graph attention network, we obtained low dimensional feature representations for microbes and drugs separately. Finally, through integrating low dimensional feature representations with two feature matrices to form the inputs of a convolutional neural network respectively, a novel computational model named GACNNMDA was designed to predict possible scores of microbe-drug pairs. Experimental results show that the predictive performance of GACNNMDA is superior to existing advanced methods. Furthermore, case studies on well-known microbes and drugs demonstrate the effectiveness of GACNNMDA as well. Source codes and supplementary materials are available at: https://github.com/tyqGitHub/TYQ/tree/master/GACNNMDA.

Isolation, structural determination, and antiviral activities of metabolites from vanitaracin A-producing Talaromyces sp

Vanitaracin A, an anti-hepatitis B virus polyketide, has been previously isolated from Talaromyces sp. In the present study, we searched for novel compounds in the culture broth obtained from a vanitaracin A-producing fungus under various conditions. Three novel compounds (vanitaracin C, vanitaraphilone A, and 2-hydroxy-4-(hydroxymethyl)-6-methylbenzaldehyde) were isolated, and their structures were determined using spectroscopic methods (1D/2D NMR and MS). In addition, the antiviral spectrum of vanitaracin A was examined by measuring its antiviral activities against rabies virus, Borna disease virus 1, and bovine leukemia virus. This compound exhibited antiviral activity against bovine leukemia virus, which is the causative agent of enzootic bovine leukosis. The anti-bovine leukemia virus effects of other compounds isolated from the vanitaracin A-producing fungus, namely, vanitaracins B and C, vanitaraphilone A, and 2-hydroxy-4-(hydroxymethyl)-6-methylbenzaldehyde, were also evaluated. Vanitaracin B, vanitaraphilone A and 2-hydroxy-4-(hydroxymethyl)-6-methylbenzaldehyde were also found to exhibit activity against bovine leukemia virus. These findings reveal the broad-spectrum antiviral activity of the vanitaracin scaffold and suggest several candidates for the development of anti-bovine leukemia virus drugs.

The life cycle of the alphaviruses: From an antiviral perspective

The alphaviruses are a widely distributed group of positive-sense, single stranded, RNA viruses. These viruses are largely arthropod-borne and can be found on all populated continents. These viruses cause significant human disease, and recently have begun to spread into new populations, such as the expansion of Chikungunya virus into southern Europe and the Caribbean, where it has established itself as endemic. The study of alphaviruses is an active and expanding field, due to their impacts on human health, their effects on agriculture, and the threat that some pose as potential agents of biological warfare and terrorism. In this systematic review we will summarize both historic knowledge in the field as well as recently published data that has potential to shift current theories in how alphaviruses are able to function. This review is comprehensive, covering all parts of the alphaviral life cycle as well as a brief overview of their pathology and the current state of research in regards to vaccines and therapeutics for alphaviral disease.

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.

Drug repositioning against COVID-19: a first line treatment

COVID-19 disease caused by the SARS-CoV-2 virus has shaken our health and wealth foundations. Although COVID-19 vaccines will become available allowing for attenuation of disease progression rates, distribution of vaccines can create other challenges and delays. Hence repurposed drugs against SARS-CoV-2 can be an attractive parallel strategy that can be integrated into routine clinical practice even in poorly-resourced countries. The present study was designed using knowledge of viral pathogenesis and pharmacodynamics of broad-spectrum antiviral agents (BSAAs). We carried out the virtual screening of BSAAs against the SARS-CoV-2 spike glycoprotein, RNA dependent RNA polymerase (RdRp), the main protease (Mpro) and the helicase enzyme of SARS-CoV-2. Imatinib (a tyrosine kinase inhibitor), Suramin (an anti-parasitic), Glycyrrhizin (an anti-inflammatory) and Bromocriptine (a dopamine agonist) showed higher binding affinity to multiple targets. Further through molecular dynamics simulation, critical conformational changes in the target protein molecules were revealed upon drug binding which illustrates the favorable binding conformations of antiviral drugs against SARS-CoV-2 target proteins. The resulting drugs from the present study in combination and in cocktails from the arsenal of existing drugs could reduce the translational distance and could offer substantial clinical benefit to decrease the burden of COVID-19 illness. This also creates a roadmap for subsequent viral diseases that emerge.Communicated by Ramaswamy H. Sarma.

Model exploration for discovering COVID-19 targeted traditional Chinese medicine

In terms of treatment, a particularly targeted drug is needed to combat the COVID-19 pandemic. Although there are currently no specific drugs for COVID-19, traditional Chinese medicine(TCM) is clearly effective. It is recommended that through data analysis and mining of TCM cases (expert experience) and population evidence (RCT and cohort studies), core prescriptions for various efficacy can be obtained. Starting from a multidimensional model of regulating immunity, improving inflammation, and protecting multiple organs, this paper constructs a multidimensional model of targeted drug discovery, integrating molecular, cellular, and animal efficacy evaluation. Through functional activity testing, biophysical detection of compound binding to target proteins, multidimensional pharmacodynamic evaluation systems of cells (Vero E6, Vero, Vero81, Huh7, and caca2) and animals (mice infected with the new coronavirus, rhesus macaques, and hamsters), the effectiveness of effective preparations was evaluated, and various efficacy effects including lung moisturizing, dehumidification and detoxification were obtained. Using modern technology, it is now possible to understand how the immune system is controlled, how inflammation is reduced, and how various organs are protected. Complete early drug characterization and finally obtain effective targeted TCM. This article provides a demonstration resource for the development of new drugs specifically for TCM.

Human Maternal-Fetal Interface Cellular Models to Assess Antiviral Drug Toxicity during Pregnancy

Pregnancy is a period of elevated risk for viral disease severity, resulting in serious health consequences for both the mother and the fetus; yet antiviral drugs lack comprehensive safety and efficacy data for use among pregnant women. In fact, pregnant women are systematically excluded from therapeutic clinical trials to prevent potential fetal harm. Current FDA-recommended reproductive toxicity assessments are studied using small animals which often do not accurately predict the human toxicological profiles of drug candidates. Here, we review the potential of human maternal-fetal interface cellular models in reproductive toxicity assessment of antiviral drugs. We specifically focus on the 2- and 3-dimensional maternal placental models of different gestational stages and those of fetal embryogenesis and organ development. Screening of drug candidates in physiologically relevant human maternal-fetal cellular models will be beneficial to prioritize selection of safe antiviral therapeutics for clinical trials in pregnant women.

Tackling the Future Pandemics: Broad-Spectrum Antiviral Agents (BSAAs) Based on A-Type Proanthocyanidins

A-type proanthocyanidins (PAC-As) are plant-derived natural polyphenols that occur as oligomers or polymers of flavan-3-ol monomers, such as (+)-catechin and (-)-epicatechin, connected through an unusual double A linkage. PAC-As are present in leaves, seeds, flowers, bark, and fruits of many plants, and are thought to exert protective natural roles against microbial pathogens, insects, and herbivores. Consequently, when tested in isolation, PAC-As have shown several biological effects, through antioxidant, antibacterial, immunomodulatory, and antiviral activities. PAC-As have been observed in fact to inhibit replication of many different human viruses, and both enveloped and non-enveloped DNA and RNA viruses proved sensible to their inhibitory effect. Mechanistic studies revealed that PAC-As cause reduction of infectivity of viral particles they come in contact with, as a result of their propensity to interact with virion surface capsid proteins or envelope glycoproteins essential for viral attachment and entry. As viral infections and new virus outbreaks are a major public health concern, development of effective Broad-Spectrum Antiviral Agents (BSAAs) that can be rapidly deployable even against future emerging viruses is an urgent priority. This review summarizes the antiviral activities and mechanism of action of PAC-As, and their potential to be deployed as BSAAs against present and future viral infections.

Antiviral activity of amiodarone in SARS-CoV-2 disease

Amiodarone seems to exhibit some antiviral activity in the disease caused by SARS-CoV-2. Here we have examined the SARS-CoV-2 disease course in the entire population of the Czech Republic and compared it with the course of the disease in patients treated with amiodarone in two major Prague's hospitals. In the whole population of the Czech Republic SARS-CoV-2 infected 1665070 persons (15.6 %) out of 10694000 (100 %) between 1 April 2020 and 30 June 2021. In the same time period only 35 patients (3.4 %) treated with amiodarone were infected with SARS-CoV-2 virus out of 1032 patients (100 %) who received amiodarone. It appears that amiodarone can prevent SARS-CoV-2 virus infection by multiple mechanisms. In in-vitro experiments it exhibits SARS-CoV-2 virus replication inhibitions. Due to its anti-inflammatory and antioxidant properties, it may have beneficial effect on the complications caused by SARS-CoV-2 as well. Additionally, inorganic iodine released from amiodarone can be converted to hypoiodite (IO-), which has antiviral and antibacterial activity, and thus can affect the life cycle of the virus.

Cyclic Phthalate Esters as Liver X Receptor Antagonists with Anti-hepatitis C Virus and Anti-severe Acute Respiratory Syndrome Coronavirus 2 Properties

The liver X receptor is a nuclear hormone receptor that regulates lipid metabolism. Previously, we had demonstrated the antiviral properties of a liver X receptor antagonist associated with the hepatitis C virus and severe acute respiratory syndrome coronavirus 2. In this study, we screened a chemical library and identified two potential liver X receptor antagonists. Spectroscopic analysis revealed that the structures of both antagonists (compounds 1 and 2) were cyclic dimer and trimer of esters, respectively, that consisted of phthalate and 1,6-hexane diol. This study is the first to report the structure of the cyclic trimer of phthalate ester. Further experiments revealed that the compounds were impurities of solvents used for purification, although their source could not be traced. Both phthalate esters exhibited anti-hepatitis C virus activity, whereas the cyclic dimer showed anti-severe acute respiratory syndrome coronavirus 2 activity. Cyclic phthalate derivatives may constitute a novel class of liver X receptor antagonists and broad-spectrum antivirals.

Antiviral Immunoglobulins of Chicken Egg Yolk for Potential Prevention of SARS-CoV-2 Infection

Background: Some viruses cause outbreaks, which require immediate attention. Neutralizing antibodies could be developed for viral outbreak management. However, the development of monoclonal antibodies is often long, laborious, and unprofitable. Here, we report the development of chicken polyclonal neutralizing antibodies against SARS-CoV-2 infection. Methods: Layers were immunized twice with 14-day intervals using the purified receptor-binding domain (RBD) of the S protein of SARS-CoV-2/Wuhan or SARS-CoV-2/Omicron. Eggs were harvested 14 days after the second immunization. Polyclonal IgY antibodies were extracted. Binding of anti-RBD IgYs was analyzed by immunoblot and indirect ELISA. Furthermore, the neutralization capacity of anti-RBD IgYs was measured in Vero-E6 cells infected with SARS-CoV-2-mCherry/Wuhan and SARS-CoV-2/Omicron using fluorescence and/or cell viability assays. In addition, the effect of IgYs on the expression of SARS-CoV-2 and host cytokine genes in the lungs of Syrian Golden hamsters was examined using qRT-PCR. Results: Anti-RBD IgYs efficiently bound viral RBDs in situ, neutralized the virus variants in vitro, and lowered viral RNA amplification, with minimal alteration of virus-mediated immune gene expression in vivo. Conclusions: Altogether, our results indicate that chicken polyclonal IgYs can be attractive targets for further pre-clinical and clinical development for the rapid management of outbreaks of emerging and re-emerging viruses.

Inferences of actinobacterial metabolites to combat Corona virus

The entire globe is reeling under the magnitude of the current corona virus pandemic. This menace has proposed severe health and economic threats for all, thereby challenging our human existence itself. Since its outbreak, it has raised the concern and imperative need of developing novel and effective agents to combat viral diseases and now its variants as well. Despite the sincere and concerted efforts of scientists and pharma giants all over the world, there seems to be no ideal recourse found till date. Natural products are rich sources of novel compounds used in the treatment of infectious and non-infectious diseases. There are reports on natural products from microbes, plants and marine organisms that are active against viral targets. Actinobacteria, the largest phylum under the bacterial kingdom, is known for its secondary metabolite production with diverse bioactive potentials. Nearly 65% of antibiotics used in medicine are contributed by Actinobacteria. Compared to antibacterial and antifungal agents, antiviral compounds from Actinobacteria are less studied. In recent years Actinobacteria from under studied/extreme ecosystems are explored for their antiviral properties. Ivermectin and teicoplanin are examples of Actinobacteria-derived antiviral drugs available for commercial use. This review highlights the importance of actinobacteria as future sources of antiviral drug discovery.

Identification and semisynthesis of (-)-anisomelic acid as oral agent against SARS-CoV-2 in mice

(-)-Anisomelic acid, isolated from Anisomeles indica (L.) Kuntze (Labiatae) leaves, is a macrocyclic cembranolide with a trans-fused α-methylene-γ-lactone motif. Anisomelic acid effectively inhibits SARS-CoV-2 replication and viral-induced cytopathic effects with an EC₅₀ of 1.1 and 4.3 μM, respectively. Challenge studies of SARS-CoV-2-infected K18-hACE2 mice showed that oral administration of anisomelic acid and subcutaneous dosing of remdesivir can both reduce the viral titers in the lung tissue at the same level. To facilitate drug discovery, we used a semisynthetic approach to shorten the project timelines. The enantioselective semisynthesis of anisomelic acid from the naturally enriched and commercially available starting material (+)-costunolide was achieved in five steps with a 27% overall yield. The developed chemistry provides opportunities for developing anisomelic-acid-based novel ligands for selectively targeting proteins involved in viral infections.

Novel Synergistic Anti-Enteroviral Drug Combinations

Background: Enterovirus infections affect people around the world, causing a range of illnesses, from mild fevers to severe, potentially fatal conditions. There are no approved treatments for enterovirus infections. Methods: We have tested our library of broad-spectrum antiviral agents (BSAs) against echovirus 1 (EV1) in human adenocarcinoma alveolar basal epithelial A549 cells. We also tested combinations of the most active compounds against EV1 in A549 and human immortalized retinal pigment epithelium RPE cells. Results: We confirmed anti-enteroviral activities of pleconaril, rupintrivir, cycloheximide, vemurafenib, remdesivir, emetine, and anisomycin and identified novel synergistic rupintrivir–vemurafenib, vemurafenib–pleconaril and rupintrivir–pleconaril combinations against EV1 infection. Conclusions: Because rupintrivir, vemurafenib, and pleconaril require lower concentrations to inhibit enterovirus replication in vitro when combined, their cocktails may have fewer side effects in vivo and, therefore, should be further explored in preclinical and clinical trials against EV1 and other enterovirus infections.

Neighborhood-based inference and restricted Boltzmann machine for microbe and drug associations prediction

Background

Efficient identification of microbe-drug associations is critical for drug development and solving problem of antimicrobial resistance. Traditional wet-lab method requires a lot of money and labor in identifying potential microbe-drug associations. With development of machine learning and publication of large amounts of biological data, computational methods become feasible.

Methods

In this article, we proposed a computational model of neighborhood-based inference (NI) and restricted Boltzmann machine (RBM) to predict potential microbe-drug association (NIRBMMDA) by using integrated microbe similarity, integrated drug similarity and known microbe-drug associations. First, NI was used to obtain a score matrix of potential microbe-drug associations by using different thresholds to find similar neighbors for drug or microbe. Second, RBM was employed to obtain another score matrix of potential microbe-drug associations based on contrastive divergence algorithm and sigmoid function. Because generalization ability of individual method is poor, we used an ensemble learning to integrate two score matrices for predicting potential microbe-drug associations more accurately. In particular, NI can fully utilize similar (neighbor) information of drug or microbe and RBM can learn potential probability distribution hid in known microbe-drug associations. Moreover, ensemble learning was used to integrate individual predictor for obtaining a stronger predictor.

Results

In global leave-one-out cross validation (LOOCV), NIRBMMDA gained the area under the receiver operating characteristics curve (AUC) of 0.8666, 0.9413 and 0.9557 for datasets of DrugVirus, MDAD and aBiofilm, respectively. In local LOOCV, AUCs of 0.8512, 0.9204 and 0.9414 were obtained for NIRBMMDA based on datasets of DrugVirus, MDAD and aBiofilm, respectively. For five-fold cross validation, NIRBMMDA acquired AUC and standard deviation of 0.8569 ± -0.0027, 0.9248 ± -0.0014 and 0.9369 ± -0.0020 on the basis of datasets of DrugVirus, MDAD and aBiofilm, respectively. Moreover, case study for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) showed that 13 out of the top 20 predicted drugs were verified by searching literature. The other two case studies indicated that 17 and 17 out of the top 20 predicted microbes for the drug of ciprofloxacin and minocycline were confirmed by identifying published literature, respectively.

Metapath Aggregated Graph Neural Network and Tripartite Heterogeneous Networks for Microbe-Disease Prediction

More and more studies have shown that understanding microbe-disease associations cannot only reveal the pathogenesis of diseases, but also promote the diagnosis and prognosis of diseases. Because traditional medical experiments are time-consuming and expensive, many computational methods have been proposed in recent years to identify potential microbe-disease associations. In this study, we propose a method based on heterogeneous network and metapath aggregated graph neural network (MAGNN) to predict microbe-disease associations, called MATHNMDA. First, we introduce microbe-drug interactions, drug-disease associations, and microbe-disease associations to construct a microbe-drug-disease heterogeneous network. Then we take the heterogeneous network as input to MAGNN. Second, for each layer of MAGNN, we carry out intra-metapath aggregation with a multi-head attention mechanism to learn the structural and semantic information embedded in the target node context, the metapath-based neighbor nodes, and the context between them, by encoding the metapath instances under the metapath definition mode. We then use inter-metapath aggregation with an attention mechanism to combine the semantic information of all different metapaths. Third, we can get the final embedding of microbe nodes and disease nodes based on the output of the last layer in the MAGNN. Finally, we predict potential microbe-disease associations by reconstructing the microbe-disease association matrix. In addition, we evaluated the performance of MATHNMDA by comparing it with that of its variants, some state-of-the-art methods, and different datasets. The results suggest that MATHNMDA is an effective prediction method. The case studies on asthma, inflammatory bowel disease (IBD), and coronavirus disease 2019 (COVID-19) further validate the effectiveness of MATHNMDA.

Conserved coronavirus proteins as targets of broad-spectrum antivirals

Coronaviruses are a class of single-stranded, positive-sense RNA viruses that have caused three major outbreaks over the past two decades: Middle East respiratory syndrome–related coronavirus (MERS-CoV), severe acute respiratory syndrome coronavirus (SARS-CoV), and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). All outbreaks have been associated with significant morbidity and mortality. In this study, we have identified and explored conserved binding sites in the key coronavirus proteins for the development of broad-spectrum direct acting anti-coronaviral compounds and validated the significance of this conservation for drug discovery with existing experimental data. We have identified four coronaviral proteins with highly conserved binding site sequence and 3D structure similarity: PL^pro, M^pro, nsp10-nsp16 complex(methyltransferase), and nsp15 endoribonuclease. We have compiled all available experimental data for known antiviral medications inhibiting these targets and identified compounds active against multiple coronaviruses. The identified compounds representing potential broad-spectrum antivirals include: GC376, which is active against six viral M^pro (out of six tested, as described in research literature); mycophenolic acid, which is active against four viral PL^pro (out of four); and emetine, which is active against four viral RdRp (out of four). The approach described in this study for coronaviruses, which combines the assessment of sequence and structure conservation across a viral family with the analysis of accessible chemical structure – antiviral activity data, can be explored for the development of broad-spectrum drugs for multiple viral families.

Antiviral Activity and Mechanisms of Seaweeds Bioactive Compounds on Enveloped Viruses-A Review

In the last decades, the interest in seaweed has significantly increased. Bioactive compounds from seaweed’s currently receive major attention from pharmaceutical companies as they express several interesting biological activities which are beneficial for humans. The structural diversity of seaweed metabolites provides diverse biological activities which are expressed through diverse mechanisms of actions. This review mainly focuses on the antiviral activity of seaweed’s extracts, highlighting the mechanisms of actions of some seaweed molecules against infection caused by different types of enveloped viruses: influenza, Lentivirus (HIV-1), Herpes viruses, and coronaviruses. Seaweed metabolites with antiviral properties can act trough different pathways by increasing the host’s defense system or through targeting and blocking virus replication before it enters host cells. Several studies have already established the large antiviral spectrum of seaweed’s bioactive compounds. Throughout this review, antiviral mechanisms and medical applications of seaweed’s bioactive compounds are analyzed, suggesting seaweed’s potential source of antiviral compounds for the formulation of novel and natural antiviral drugs.

Synthesis, in silico study (DFT, ADMET) and crystal structure of novel sulfamoyloxy-oxazolidinones: Interaction with SARS-CoV-2

A new series of sulfamoyloxyoxazolidinone (SOO) derivatives have been synthesized and characterized by single-crystal X-ray diffraction, NMR, IR, MS and EA. Chemical reactivity and geometrical characteristics of the target compounds were investigated using DFT method. The possible binding mode between SOO and Main protease (Mpro) of SARS-CoV-2 and their reactivity were studied using molecular docking simulation. Single crystal X-ray diffraction showed that SOO crystallizes in a monoclinic system with P 2 1 space group. The binding energy of the SARS-CoV-2/Mpro-SOO complex and the calculated inhibition constant using docking simulation showed that the active SOO molecule has the ability to inhibit SARS-CoV2. We studied the prediction of absorption, distribution, properties of metabolism, excretion and toxicity (ADMET) of the synthesized molecules.

A comprehensive review of Artificial Intelligence and Network based approaches to drug repurposing in Covid-19

Conventional drug discovery and development is tedious and time-taking process; because of which it has failed to keep the required pace to mitigate threats and cater demands of viral and re-occurring diseases, such as Covid-19. The main reasons of this delay in traditional drug development are: high attrition rates, extensive time requirements, and huge financial investment with significant risk. The effective solution to de novo drug discovery is drug repurposing. Previous studies have shown that the network-based approaches and analysis are versatile platform for repurposing as the network biology is used to model the interactions between variety of biological concepts. Herein, we provide a comprehensive background of machine learning and deep learning in drug repurposing while specifically focusing on the applications of network-based approach to drug repurposing in Covid-19, data sources, and tools used. Furthermore, use of network proximity, network diffusion, and AI on network-based drug repurposing for Covid-19 is well-explained. Finally, limitations of network-based approaches in general and specific to network are stated along with future recommendations for better network-based models.

DrugVirus.info 2.0: an integrative data portal for broad-spectrum antivirals (BSA) and BSA-containing drug combinations (BCCs)

Viruses can cross species barriers and cause unpredictable outbreaks in man with substantial economic and public health burdens. Broad-spectrum antivirals, (BSAs, compounds inhibiting several human viruses), and BSA-containing drug combinations (BCCs) are deemed as immediate therapeutic options that fill the void between virus identification and vaccine development. Here, we present DrugVirus.info 2.0 (https://drugvirus.info), an integrative interactive portal for exploration and analysis of BSAs and BCCs, that greatly expands the database and functionality of DrugVirus.info 1.0 webserver. Through the data portal that now expands the spectrum of BSAs and provides information on BCCs, we developed two modules for (i) interactive analysis of users' own antiviral drug and combination screening data and their comparison with published datasets, and (ii) exploration of the structure-activity relationship between various BSAs. The updated portal provides an essential toolbox for antiviral drug development and repurposing applications aiming to identify existing and novel treatments of emerging and re-emerging viral threats.

Synthesis of antiviral drugs by using carbon–carbon and carbon–heteroatom bond formation under greener conditions

Antiviral medications are a branch of medicines notably used to treat that cause many significant diseases in humans and animals. This monograph mainly focuses on recent developments and synthesis of antiviral drugs using carbon-carbon and carbon–hetero bond cross-coupling chemistry. Viral infections exact several severe human diseases, accounting for remarkably high mortality rates. In this sense, academia and the pharmaceutical industry continuously search for novel compounds with better antiviral activity. The researchers face the challenge of developing greener and economical ways to synthesize these compounds and make significant progress.

MDGNN: Microbial Drug Prediction Based on Heterogeneous Multi-Attention Graph Neural Network

Human beings are now facing one of the largest public health crises in history with the outbreak of COVID-19. Traditional drug discovery could not keep peace with newly discovered infectious diseases. The prediction of drug-virus associations not only provides insights into the mechanism of drug–virus interactions, but also guides the screening of potential antiviral drugs. We develop a deep learning algorithm based on the graph convolutional networks (MDGNN) to predict potential antiviral drugs. MDGNN is consisted of new node-level attention and feature-level attention mechanism and shows its effectiveness compared with other comparative algorithms. MDGNN integrates the global information of the graph in the process of information aggregation by introducing the attention at node and feature level to graph convolution. Comparative experiments show that MDGNN achieves state-of-the-art performance with an area under the curve (AUC) of 0.9726 and an area under the PR curve (AUPR) of 0.9112. In this case study, two drugs related to SARS-CoV-2 were successfully predicted and verified by the relevant literature. The data and code are open source and can be accessed from https://github.com/Pijiangsheng/MDGNN.