Causal network models of SARS-CoV-2 expression and aging to identify candidates for drug repurposing

DeepDrug as an expert guided and AI driven drug repurposing methodology for selecting the lead combination of drugs for Alzheimer's disease

Alzheimer's Disease (AD) significantly aggravates human dignity and quality of life. While newly approved amyloid immunotherapy has been reported, effective AD drugs remain to be identified. Here, we propose a novel AI-driven drug-repurposing method, DeepDrug, to identify a lead combination of approved drugs to treat AD patients. DeepDrug advances drug-repurposing methodology in four aspects. Firstly, it incorporates expert knowledge to extend candidate targets to include long genes, immunological and aging pathways, and somatic mutation markers that are associated with AD. Secondly, it incorporates a signed directed heterogeneous biomedical graph encompassing a rich set of nodes and edges, and node/edge weighting to capture crucial pathways associated with AD. Thirdly, it encodes the weighted biomedical graph through a Graph Neural Network into a new embedding space to capture the granular relationships across different nodes. Fourthly, it systematically selects the high-order drug combinations via diminishing return-based thresholds. A five-drug lead combination, consisting of Tofacitinib, Niraparib, Baricitinib, Empagliflozin, and Doxercalciferol, has been selected from the top drug candidates based on DeepDrug scores to achieve the maximum synergistic effect. These five drugs target neuroinflammation, mitochondrial dysfunction, and glucose metabolism, which are all related to AD pathology. DeepDrug offers a novel AI-and-big-data, expert-guided mechanism for new drug combination discovery and drug-repurposing across AD and other neuro-degenerative diseases, with immediate clinical applications.

Artificial intelligence for personalized nanomedicine; from material selection to patient outcomes

INTRODUCTION

Artificial intelligence (AI) is changing the field of nanomedicine by exploring novel nanomaterials for developing therapies of high efficacy. AI works on larger datasets, finding sought-after nano-properties for different therapeutic aims and eventually enhancing nanomaterials' safety and effectiveness. AI leverages patient clinical and genetic data to predict outcomes, guide treatments, and optimize drug dosages and forms, enhancing benefits while minimizing side effects. AI-supported nanomedicine faces challenges like data fusion, ethics, and regulation, requiring better tools and interdisciplinary collaboration. This review highlights the importance of AI regarding patient care and urges scientists, medical professionals, and regulators to adopt AI for better outcomes.

AREAS COVERED

Personalized Nanomedicine, Material Discovery, AI-Driven Therapeutics, Data Integration, Drug Delivery, Patient Centric Care.

EXPERT OPINION

Today, AI can improve personalized health wellness through the discovery of new types of drug nanocarriers, nanomedicine of specific properties to tackle targeted medical needs, and an increment in efficacy along with safety. Nevertheless, problems such as ethical issues, data security, or unbalanced data sets need to be addressed. Potential future developments involve using AI and quantum computing together and exploring telemedicine i.e. the Internet-of-Medical-Things (IoMT) approach can enhance the quality of patient care in a personalized manner by timely decision-making.

Current and future directions in network biology

Summary

Network biology is an interdisciplinary field bridging computational and biological sciences that has proved pivotal in advancing the understanding of cellular functions and diseases across biological systems and scales. Although the field has been around for two decades, it remains nascent. It has witnessed rapid evolution, accompanied by emerging challenges. These stem from various factors, notably the growing complexity and volume of data together with the increased diversity of data types describing different tiers of biological organization. We discuss prevailing research directions in network biology, focusing on molecular/cellular networks but also on other biological network types such as biomedical knowledge graphs, patient similarity networks, brain networks, and social/contact networks relevant to disease spread. In more detail, we highlight areas of inference and comparison of biological networks, multimodal data integration and heterogeneous networks, higher-order network analysis, machine learning on networks, and network-based personalized medicine. Following the overview of recent breakthroughs across these five areas, we offer a perspective on future directions of network biology. Additionally, we discuss scientific communities, educational initiatives, and the importance of fostering diversity within the field. This article establishes a roadmap for an immediate and long-term vision for network biology.

Availability and implementation

Not applicable.

Invited commentary: where do the causal DAGS come from?

How do we construct our causal directed acyclic graphs (DAGs)-for example, for life-course modeling and analysis? In this commentary, I review how the data-driven construction of causal DAGs (causal discovery) has evolved, what promises it holds, and what limitations or caveats must be considered. I find that expert- or theory-driven model-building might benefit from some more checking against the data and that causal discovery could bring new ideas to old theories.

Valproic acid use is associated with diminished risk of contracting COVID-19, and diminished disease severity: Epidemiologic and in vitro analysis reveal mechanistic insights

The SARS-CoV-2 pandemic has caused unprecedented worldwide infections from persistent mutant variants with various degrees of infectivity and virulence. The elusiveness of a highly penetrant, worldwide vaccination strategy suggests that the complete eradication of SARS-CoV-2 is unlikely. Even with the advent of new antiviral agents, the disease burden worldwide continues to exceed current preventative and therapeutic strategies. Greater interest has been placed towards the development of affordable,broadly effective antiviral therapeutics. Here, we report that the small branched-chain fatty acid Valproic acid (VPA), approved for maintenance of seizure and bipolar disorder, has a novel anti- coronavirus activity that can be augmented with the addition of a long-chain, polyunsaturated omega-3 fatty acid, Docosahexaenoic acid (DHA). An EMR-based epidemiological study of patients tested for COVID-19 demonstrated a correlation exists between a reduced infection rate in patients treated withVPA of up to 25%, as well as a decreased risk of emergency room visits, hospitalization, ICU admission,and use of mechanical ventilation. In vitro studies have demonstrated that VPA modifies gene expression in MRC5 cells. Interestingly, VPA correlates with the inhibition of several SARS-CoV2 interacting genes and the greater inhibition of alpha-coronavirus HCoV-229E (a "common cold" virus) and SARS-CoV2. The VPA-DHA combination activates pre-existing intracellular antiviral mechanisms normally repressed by coronaviruses. Gene expression profiles demonstrate subtle differences in overall gene expression between VPA-treated and VPA-DHA-treated cells. HCoV-229E infection caused an intensely different response with a marked induction of multiple intracellular inflammatory genes. Changes in gene expression took at least 24 hours to manifest and most likely why prior drug screens failed to identify any antiviral VPA activity despite in silico predictions. This report demonstrates an interaction between HDAC inhibition and the potent activation of cellular antiviral responses. A foundation now exists for a low-cost, highly effective antiviral strategy when supplemented with DHA.

Local patterns of genetic sharing challenge the boundaries between neuropsychiatric and insulin resistance-related conditions

The co-occurrence of insulin resistance (IR)-related metabolic conditions with neuropsychiatric disorders is a complex public health challenge. Evidence of the genetic links between these phenotypes is emerging, but little is currently known about the genomic regions and biological functions that are involved. To address this, we performed Local Analysis of [co]Variant Association (LAVA) using large-scale (N=9,725-933,970) genome-wide association studies (GWASs) results for three IR-related conditions (type 2 diabetes mellitus, obesity, and metabolic syndrome) and nine neuropsychiatric disorders. Subsequently, positional and expression quantitative trait locus (eQTL)-based gene mapping and downstream functional genomic analyses were performed on the significant loci. Patterns of negative and positive local genetic correlations (|rg|=0.21-1, pFDR<0.05) were identified at 109 unique genomic regions across all phenotype pairs. Local correlations emerged even in the absence of global genetic correlations between IR-related conditions and Alzheimer's disease, bipolar disorder, and Tourette's syndrome. Genes mapped to the correlated regions showed enrichment in biological pathways integral to immune-inflammatory function, vesicle trafficking, insulin signalling, oxygen transport, and lipid metabolism. Colocalisation analyses further prioritised 10 genetically correlated regions for likely harbouring shared causal variants, displaying high deleterious or regulatory potential. These variants were found within or in close proximity to genes, such as SLC39A8 and HLA-DRB1, that can be targeted by supplements and already known drugs, including omega-3/6 fatty acids, immunomodulatory, antihypertensive, and cholesterol-lowering drugs. Overall, our findings underscore the complex genetic landscape of IR-neuropsychiatric multimorbidity, advocating for an integrated disease model and offering novel insights for research and treatment strategies in this domain.

Identification of novel anti-ZIKV drugs from viral-infection temporal gene expression profiles

Zika virus (ZIKV) infections are typically asymptomatic but cause severe neurological complications (e.g. Guillain-Barré syndrome in adults, and microcephaly in newborns). There are currently no specific therapy or vaccine options available to prevent ZIKV infections. Temporal gene expression profiles of ZIKV-infected human brain microvascular endothelial cells (HBMECs) were used in this study to identify genes essential for viral replication. These genes were then used to identify novel anti-ZIKV agents and validated in publicly available data and functional wet-lab experiments. Here, we found that ZIKV effectively evaded activation of immune response-related genes and completely reprogrammed cellular transcriptional architectures. Knockdown of genes, which gradually upregulated during viral infection but showed distinct expression patterns between ZIKV- and mock infection, discovered novel proviral and antiviral factors. One-third of the 74 drugs found through signature-based drug repositioning and cross-reference with the Drug Gene Interaction Database (DGIdb) were known anti-ZIKV agents. In cellular assays, two promising antiviral candidates (Luminespib/NVP-AUY922, L-161982) were found to reduce viral replication without causing cell toxicity. Overall, our time-series transcriptome-based methods offer a novel and feasible strategy for antiviral drug discovery. Our strategies, which combine conventional and data-driven analysis, can be extended for other pathogens causing pandemics in the future.

The benefits and pitfalls of machine learning for biomarker discovery

Prospects for the discovery of robust and reproducible biomarkers have improved considerably with the development of sensitive omics platforms that can enable measurement of biological molecules at an unprecedented scale. With technical barriers to success lowering, the challenge is now moving into the analytical domain. Genome-wide discovery presents a problem of scale and multiple testing as standard statistical methods struggle to distinguish signal from noise in increasingly complex biological systems. Machine learning and AI methods are good at finding answers in large datasets, but they have a tendency to overfit solutions. It may be possible to find a local answer or mechanism in a specific patient sample or small group of samples, but this may not generalise to wider patient populations due to the high likelihood of false discovery. The rise of explainable AI offers to improve the opportunity for true discovery by providing explanations for predictions that can be explored mechanistically before proceeding to costly and time-consuming validation studies. This review aims to introduce some of the basic concepts of machine learning and AI for biomarker discovery with a focus on post hoc explanation of predictions. To illustrate this, we consider how explainable AI has already been used successfully, and we explore a case study that applies AI to biomarker discovery in rheumatoid arthritis, demonstrating the accessibility of tools for AI and machine learning. We use this to illustrate and discuss some of the potential challenges and solutions that may enable AI to critically interrogate disease and response mechanisms.

Transfer Learning with Kernel Methods

Transfer learning refers to the process of adapting a model trained on a source task to a target task. While kernel methods are conceptually and computationally simple models that are competitive on a variety of tasks, it has been unclear how to develop scalable kernel-based transfer learning methods across general source and target tasks with possibly differing label dimensions. In this work, we propose a transfer learning framework for kernel methods by projecting and translating the source model to the target task. We demonstrate the effectiveness of our framework in applications to image classification and virtual drug screening. For both applications, we identify simple scaling laws that characterize the performance of transfer-learned kernels as a function of the number of target examples. We explain this phenomenon in a simplified linear setting, where we are able to derive the exact scaling laws.

Sex blind: bridging the gap between drug exposure and sex-related gene expression in Danio rerio using next-generation sequencing (NGS) data and a literature review to find the missing links in pharmaceutical and environmental toxicology studies

The sex of both humans and Danio rerio has previously been shown to affect the way individuals respond to drug exposure. Genes which allow identification of sex in juvenile zebrafish show potential to reveal these confounding variables between sex in toxicological and preclinical trials but the link between these is so far missing. These sex-specific, early expressed genes where expression is not altered by drug exposure must be carefully selected for this purpose. We aimed to discover genes which can be used in pharmaceutical trials and environmental toxicology studies to uncover sex-related variations in gene expression with drug application using the model organism Danio rerio. Previously published early sex determining genes from King et al. were evaluated as well as additional genes selected from our zebrafish Next-generation sequencing (NGS) data which are known from previously published works not to be susceptible to changes in expression with drug exposure. NGS revealed a further ten female-specific genes (vtg1, cyp17a1, cyp19a1a, igf3, ftz-f1, gdf9, foxl2a, Nr0b1, ipo4, lhcgr) and five male related candidate genes (FKBP5, apobb1, hbaa1, dmrt1, spata6) which are also expressed in juvenile zebrafish, 28 days post fertilisation (dpf). Following this, a literature review was performed to classify which of these early-expressed sex specific genes are already known to be affected by drug exposure in order to determine candidate genes to be used in pharmaceutical trials or environmental toxicology testing studies. Discovery of these early sex-determining genes in Danio rerio will allow identification of sex-related responses to drug testing to improve sex-specific healthcare and the medical treatment of human patients.

A Review on Artificial Intelligence Approaches and Rational Approaches in Drug Discovery

Artificial intelligence (AI) speeds up the drug development process and reduces its time, as well as the cost which is of enormous importance in outbreaks such as COVID-19. It uses a set of machine learning algorithms that collects the available data from resources, categorises, processes and develops novel learning methodologies. Virtual screening is a successful application of AI, which is used in screening huge drug-like databases and filtering to a small number of compounds. The brain's thinking of AI is its neural networking which uses techniques such as Convoluted Neural Network (CNN), Recursive Neural Network (RNN) or Generative Adversial Neural Network (GANN). The application ranges from small molecule drug discovery to the development of vaccines. In the present review article, we discussed various techniques of drug design, structure and ligand-based, pharmacokinetics and toxicity prediction using AI. The rapid phase of discovery is the need of the hour and AI is a targeted approach to achieve this.

Revisiting the COVID-19 Pandemic: An Insight into Long-Term Post-COVID Complications and Repurposing of Drugs

SARS-CoV-2 is a highly contagious and dangerous coronavirus that has been spreading around the world since late December 2019. Severe COVID-19 has been observed to induce severe damage to the alveoli, and the slow loss of lung function led to the deaths of many patients. Scientists from all over the world are now saying that SARS-CoV-2 can spread through the air, which is a very frightening prospect for humans. Many scientists thought that this virus would evolve during the first wave of the pandemic and that the second wave of reinfection with the coronavirus would also be very dangerous. In late 2020 and early 2021, researchers found different genetic versions of the SARS-CoV-2 virus in many places around the world. Patients with different types of viruses had different symptoms. It is now evident from numerous case studies that many COVID-19 patients who are released from nursing homes or hospitals are more prone to developing multi-organ dysfunction than the general population. Understanding the pathophysiology of COVID-19 and its impact on various organ systems is crucial for developing effective treatment strategies and managing long-term health consequences. The case studies highlighted in this review provide valuable insights into the ongoing health concerns of individuals affected by COVID-19.

Celastrol: A lead compound that inhibits SARS-CoV-2 replication, the activity of viral and human cysteine proteases, and virus-induced IL-6 secretion

The global emergence of coronavirus disease 2019 (COVID-19) has caused substantial human casualties. Clinical manifestations of this disease vary from asymptomatic to lethal, and the symptomatic form can be associated with cytokine storm and hyperinflammation. In face of the urgent demand for effective drugs to treat COVID-19, we have searched for candidate compounds using in silico approach followed by experimental validation. Here we identified celastrol, a pentacyclic triterpene isolated from Tripterygium wilfordii Hook F, as one of the best compounds out of 39 drug candidates. Celastrol reverted the gene expression signature from severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-infected cells and irreversibly inhibited the recombinant forms of the viral and human cysteine proteases involved in virus invasion, such as Mpro (main protease), PLpro (papain-like protease), and recombinant human cathepsin L. Celastrol suppressed SARS-CoV-2 replication in human and monkey cell lines and decreased interleukin-6 (IL-6) secretion in the SARS-CoV-2-infected human cell line. Celastrol acted in a concentration-dependent manner, with undetectable signs of cytotoxicity, and inhibited in vitro replication of the parental and SARS-CoV-2 variant. Therefore, celastrol is a promising lead compound to develop new drug candidates to face COVID-19 due to its ability to suppress SARS-CoV-2 replication and IL-6 production in infected cells.

Rational drug repositioning for coronavirus-associated diseases using directional mapping and side-effect inference

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the pathogen of coronavirus disease 2019 (COVID-19), has infected hundreds of millions of people and caused millions of deaths. Looking for valid druggable targets with minimal side effects for the treatment of COVID-19 remains critical. After discovering host genes from multiscale omics data, we developed an end-to-end network method to investigate drug-host gene(s)-coronavirus (CoV) paths and the mechanism of action between the drug and the host factor in a directional network. We also inspected the potential side effect of the candidate drug on several common comorbidities. We established a catalog of host genes associated with three CoVs. Rule-based prioritization yielded 29 Food and Drug Administration (FDA)-approved drugs via accounting for the effects of drugs on CoVs, comorbidities, and drug-target confidence information. Seven drugs are currently undergoing clinical trials as COVID-19 treatment. This catalog of druggable host genes associated with CoVs and the prioritized repurposed drugs will provide a new sight in therapeutics discovery for severe COVID-19 patients.

SARS-CoV-2 Nsp6 damages Drosophila heart and mouse cardiomyocytes through MGA/MAX complex-mediated increased glycolysis

SARS-CoV-2 infection causes COVID-19, a severe acute respiratory disease associated with cardiovascular complications including long-term outcomes. The presence of virus in cardiac tissue of patients with COVID-19 suggests this is a direct, rather than secondary, effect of infection. Here, by expressing individual SARS-CoV-2 proteins in the Drosophila heart, we demonstrate interaction of virus Nsp6 with host proteins of the MGA/MAX complex (MGA, PCGF6 and TFDP1). Complementing transcriptomic data from the fly heart reveal that this interaction blocks the antagonistic MGA/MAX complex, which shifts the balance towards MYC/MAX and activates glycolysis-with similar findings in mouse cardiomyocytes. Further, the Nsp6-induced glycolysis disrupts cardiac mitochondrial function, known to increase reactive oxygen species (ROS) in heart failure; this could explain COVID-19-associated cardiac pathology. Inhibiting the glycolysis pathway by 2-deoxy-D-glucose (2DG) treatment attenuates the Nsp6-induced cardiac phenotype in flies and mice. These findings point to glycolysis as a potential pharmacological target for treating COVID-19-associated heart failure.

Methodology-Centered Review of Molecular Modeling, Simulation, and Prediction of SARS-CoV-2

Despite tremendous efforts in the past two years, our understanding of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), virus-host interactions, immune response, virulence, transmission, and evolution is still very limited. This limitation calls for further in-depth investigation. Computational studies have become an indispensable component in combating coronavirus disease 2019 (COVID-19) due to their low cost, their efficiency, and the fact that they are free from safety and ethical constraints. Additionally, the mechanism that governs the global evolution and transmission of SARS-CoV-2 cannot be revealed from individual experiments and was discovered by integrating genotyping of massive viral sequences, biophysical modeling of protein-protein interactions, deep mutational data, deep learning, and advanced mathematics. There exists a tsunami of literature on the molecular modeling, simulations, and predictions of SARS-CoV-2 and related developments of drugs, vaccines, antibodies, and diagnostics. To provide readers with a quick update about this literature, we present a comprehensive and systematic methodology-centered review. Aspects such as molecular biophysics, bioinformatics, cheminformatics, machine learning, and mathematics are discussed. This review will be beneficial to researchers who are looking for ways to contribute to SARS-CoV-2 studies and those who are interested in the status of the field.

Network approaches for modeling the effect of drugs and diseases

The network approach is quickly becoming a fundamental building block of computational methods aiming at elucidating the mechanism of action (MoA) and therapeutic effect of drugs. By modeling the effect of drugs and diseases on different biological networks, it is possible to better explain the interplay between disease perturbations and drug targets as well as how drug compounds induce favorable biological responses and/or adverse effects. Omics technologies have been extensively used to generate the data needed to study the mechanisms of action of drugs and diseases. These data are often exploited to define condition-specific networks and to study whether drugs can reverse disease perturbations. In this review, we describe network data mining algorithms that are commonly used to study drug’s MoA and to improve our understanding of the basis of chronic diseases. These methods can support fundamental stages of the drug development process, including the identification of putative drug targets, the in silico screening of drug compounds and drug combinations for the treatment of diseases. We also discuss recent studies using biological and omics-driven networks to search for possible repurposed FDA-approved drug treatments for SARS-CoV-2 infections (COVID-19).

KNIME workflow for retrieving causal drug and protein interactions, building networks, and performing topological enrichment analysis demonstrated by a DILI case study

As an alternative to one drug-one target approaches, systems biology methods can provide a deeper insight into the holistic effects of drugs. Network-based approaches are tools of systems biology, that can represent valuable methods for visualizing and analysing drug-protein and protein-protein interactions. In this study, a KNIME workflow is presented which connects drugs to causal target proteins and target proteins to their causal protein interactors. With the collected data, networks can be constructed for visualizing and interpreting the connections. The last part of the workflow provides a topological enrichment test for identifying relevant pathways and processes connected to the submitted data. The workflow is based on openly available databases and their web services. As a case study, compounds of DILIRank were analysed. DILIRank is the benchmark dataset for Drug-Induced Liver Injury by the FDA, where compounds are categorized by their likeliness of causing DILI. The study includes the drugs that are most likely to cause DILI ("mostDILI") and the ones that are not likely to cause DILI ("noDILI"). After selecting the compounds of interest, down- and upregulated proteins connected to the mostDILI group were identified; furthermore, a liver-specific subset of those was created. The downregulated sub-list had considerably more entries, therefore, network and causal interactome were constructed and topological pathway enrichment analysis was performed with this list. The workflow identified proteins such as Prostaglandin G7H synthase 1 and UDP-glucuronosyltransferase 1A9 as key participants in the potential toxic events disclosing the possible mode of action. The topological network analysis resulted in pathways such as recycling of bile acids and salts and glucuronidation, indicating their involvement in DILI. The KNIME pipeline was built to support target and network-based approaches to analyse any sets of drug data and identify their target proteins, mode of actions and processes they are involved in. The fragments of the pipeline can be used separately or can be combined as required.

Cell deaths: Involvement in the pathogenesis and intervention therapy of COVID-19

The current pandemic of coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection has dramatically influenced various aspects of the world. It is urgent to thoroughly study pathology and underlying mechanisms for developing effective strategies to prevent and treat this threatening disease. It is universally acknowledged that cell death and cell autophagy are essential and crucial to maintaining host homeostasis and participating in disease pathogenesis. At present, more than twenty different types of cell death have been discovered, some parts of which have been fully understood, whereas some of which need more investigation. Increasing studies have indicated that cell death and cell autophagy caused by coronavirus might play an important role in virus infection and pathogenicity. However, the knowledge of the interactions and related mechanisms of SARS-CoV-2 between cell death and cell autophagy lacks systematic elucidation. Therefore, in this review, we comprehensively delineate how SARS-CoV-2 manipulates diverse cell death (including apoptosis, necroptosis, pyroptosis, ferroptosis, and NETosis) and cell autophagy for itself benefits, which is simultaneously involved in the occurrence and progression of COVID-19, aiming to provide a reasonable basis for the existing interventions and further development of novel therapies.

Therapy Targets SARS-CoV-2 Infection-Induced Cell Death

Coronavirus Disease 2019 (COVID-19) caused by SARS-CoV-2 has become a global health issue. The clinical presentation of COVID-19 is highly variable, ranging from asymptomatic and mild disease to severe. However, the mechanisms for the high mortality induced by SARS-CoV-2 infection are still not well understood. Recent studies have indicated that the cytokine storm might play an essential role in the disease progression in patients with COVID-19, which is characterized by the uncontrolled release of cytokines and chemokines leading to acute respiratory distress syndrome (ARDS), multi-organ failure, and even death. Cell death, especially, inflammatory cell death, might be the initiation of a cytokine storm caused by SARS-CoV-2 infection. This review summarizes the forms of cell death caused by SARS-CoV-2 in vivo or in vitro and elaborates on the dedication of apoptosis, necroptosis, NETosis, pyroptosis of syncytia, and even SARS-CoV-2 E proteins forming channel induced cell death, providing insights into targets on the cell death pathway for the treatment of COVID-19.

A multilevel approach for screening natural compounds as an antiviral agent for COVID-19

The COVID-19 has a worldwide spread, which has prompted concerted efforts to find successful drug treatments. Drug design focused on finding antiviral therapeutic agents from plant-derived compounds which may disrupt the attachment of SARS-CoV-2 to host cells is with a pivotal need and role in the last year. Herein, we provide an approach based on drug design methods combined with machine learning approaches to classify and discover inhibitors for COVID-19 from natural products. The spike receptor-binding domain (RBD) was docked with database of 125 ligands. The docking protocol based on several steps was performed within Autodock Vina to identify the high-affinity binding mode and to reveal more insights into interaction between the phytochemicals and the RBD domain. A protein-ligand interaction analyzer has been developed. The drug-likeness properties of explored inhibitors are analyzed in the frame of exploratory data analyses. The developed computational protocol yielded a comprehensive pipeline for predicting the inhibitors to prevent the entry RBD region.

Simple, fast, and flexible framework for matrix completion with infinite width neural networks

Matrix completion problems arise in many applications including recommendation systems, computer vision, and genomics. Increasingly larger neural networks have been successful in many of these applications but at considerable computational costs. Remarkably, taking the width of a neural network to infinity allows for improved computational performance. In this work, we develop an infinite width neural network framework for matrix completion that is simple, fast, and flexible. Simplicity and speed come from the connection between the infinite width limit of neural networks and kernels known as neural tangent kernels (NTK). In particular, we derive the NTK for fully connected and convolutional neural networks for matrix completion. The flexibility stems from a feature prior, which allows encoding relationships between coordinates of the target matrix, akin to semisupervised learning. The effectiveness of our framework is demonstrated through competitive results for virtual drug screening and image inpainting/reconstruction. We also provide an implementation in Python to make our framework accessible on standard hardware to a broad audience.

Machine Learning Applications in Drug Repurposing

The coronavirus disease (COVID-19) has led to an rush to repurpose existing drugs, although the underlying evidence base is of variable quality. Drug repurposing is a technique by taking advantage of existing known drugs or drug combinations to be explored in an unexpected medical scenario. Drug repurposing, hence, plays a vital role in accelerating the pre-clinical process of designing novel drugs by saving time and cost compared to the traditional de novo drug discovery processes. Since drug repurposing depends on massive observed data from existing drugs and diseases, the tremendous growth of publicly available large-scale machine learning methods supplies the state-of-the-art application of data science to signaling disease, medicine, therapeutics, and identifying targets with the least error. In this article, we introduce guidelines on strategies and options of utilizing machine learning approaches for accelerating drug repurposing. We discuss how to employ machine learning methods in studying precision medicine, and as an instance, how machine learning approaches can accelerate COVID-19 drug repurposing by developing Chinese traditional medicine therapy. This article provides a strong reasonableness for employing machine learning methods for drug repurposing, including during fighting for COVID-19 pandemic.

Causal reasoning over knowledge graphs leveraging drug-perturbed and disease-specific transcriptomic signatures for drug discovery

Network-based approaches are becoming increasingly popular for drug discovery as they provide a systems-level overview of the mechanisms underlying disease pathophysiology. They have demonstrated significant early promise over other methods of biological data representation, such as in target discovery, side effect prediction and drug repurposing. In parallel, an explosion of -omics data for the deep characterization of biological systems routinely uncovers molecular signatures of disease for similar applications. Here, we present RPath, a novel algorithm that prioritizes drugs for a given disease by reasoning over causal paths in a knowledge graph (KG), guided by both drug-perturbed as well as disease-specific transcriptomic signatures. First, our approach identifies the causal paths that connect a drug to a particular disease. Next, it reasons over these paths to identify those that correlate with the transcriptional signatures observed in a drug-perturbation experiment, and anti-correlate to signatures observed in the disease of interest. The paths which match this signature profile are then proposed to represent the mechanism of action of the drug. We demonstrate how RPath consistently prioritizes clinically investigated drug-disease pairs on multiple datasets and KGs, achieving better performance over other similar methodologies. Furthermore, we present two case studies showing how one can deconvolute the predictions made by RPath as well as predict novel targets.

Repurposing of Drugs for SARS-CoV-2 Using Inverse Docking Fingerprints

Severe acute respiratory syndrome coronavirus 2 or SARS-CoV-2 is a virus that belongs to the Coronaviridae family. This group of viruses commonly causes colds but possesses a tremendous pathogenic potential. In humans, an outbreak of SARS caused by the SARS-CoV virus was first reported in 2003, followed by 2012 when the Middle East respiratory syndrome coronavirus (MERS-CoV) led to an outbreak of Middle East respiratory syndrome (MERS). Moreover, COVID-19 represents a serious socioeconomic and global health problem that has already claimed more than four million lives. To date, there are only a handful of therapeutic options to combat this disease, and only a single direct-acting antiviral, the conditionally approved remdesivir. Since there is an urgent need for active drugs against SARS-CoV-2, the strategy of drug repurposing represents one of the fastest ways to achieve this goal. An in silico drug repurposing study using two methods was conducted. A structure-based virtual screening of the FDA-approved drug database on SARS-CoV-2 main protease was performed, and the 11 highest-scoring compounds with known 3CLpro activity were identified while the methodology was used to report further 11 potential and completely novel 3CLpro inhibitors. Then, inverse molecular docking was performed on the entire viral protein database as well as on the Coronaviridae family protein subset to examine the hit compounds in detail. Instead of target fishing, inverse docking fingerprints were generated for each hit compound as well as for the five most frequently reported and direct-acting repurposed drugs that served as controls. In this way, the target-hitting space was examined and compared and we can support the further biological evaluation of all 11 newly reported hits on SARS-CoV-2 3CLpro as well as recommend further in-depth studies on antihelminthic class member compounds. The authors acknowledge the general usefulness of this approach for a full-fledged inverse docking fingerprint screening in the future.

Strategies to identify candidate repurposable drugs: COVID-19 treatment as a case example

Drug repurposing is an invaluable strategy to identify new uses for existing drug therapies that overcome many of the time and financial costs associated with novel drug development. The COVID-19 pandemic has driven an unprecedented surge in the development and use of bioinformatic tools to identify candidate repurposable drugs. Using COVID-19 as a case study, we discuss examples of machine-learning and signature-based approaches that have been adapted to rapidly identify candidate drugs. The Library of Integrated Network-based Signatures (LINCS) and Connectivity Map (CMap) are commonly used repositories and have the advantage of being amenable to use by scientists with limited bioinformatic training. Next, we discuss how these recent advances in bioinformatic drug repurposing approaches might be adapted to identify repurposable drugs for CNS disorders. As the development of novel therapies that successfully target the cause of neuropsychiatric and neurological disorders has stalled, there is a pressing need for innovative strategies to treat these complex brain disorders. Bioinformatic approaches to identify repurposable drugs provide an exciting avenue of research that offer promise for improved treatments for CNS disorders.

Application of artificial intelligence and machine learning for COVID-19 drug discovery and vaccine design

The global pandemic of coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2, has led to a dramatic loss of human life worldwide. Despite many efforts, the development of effective drugs and vaccines for this novel virus will take considerable time. Artificial intelligence (AI) and machine learning (ML) offer promising solutions that could accelerate the discovery and optimization of new antivirals. Motivated by this, in this paper, we present an extensive survey on the application of AI and ML for combating COVID-19 based on the rapidly emerging literature. Particularly, we point out the challenges and future directions associated with state-of-the-art solutions to effectively control the COVID-19 pandemic. We hope that this review provides researchers with new insights into the ways AI and ML fight and have fought the COVID-19 outbreak.

SARS-CoV-2 promotes RIPK1 activation to facilitate viral propagation

Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is the ongoing global pandemic that poses substantial challenges to public health worldwide. A subset of COVID-19 patients experience systemic inflammatory response, known as cytokine storm, which may lead to death. Receptor-interacting serine/threonine-protein kinase 1 (RIPK1) is an important mediator of inflammation and cell death. Here, we examined the interaction of RIPK1-mediated innate immunity with SARS-CoV-2 infection. We found evidence of RIPK1 activation in human COVID-19 lung pathological samples, and cultured human lung organoids and ACE2 transgenic mice infected by SARS-CoV-2. Inhibition of RIPK1 using multiple small-molecule inhibitors reduced the viral load of SARS-CoV-2 in human lung organoids. Furthermore, therapeutic dosing of the RIPK1 inhibitor Nec-1s reduced mortality and lung viral load, and blocked the CNS manifestation of SARS-CoV-2 in ACE2 transgenic mice. Mechanistically, we found that the RNA-dependent RNA polymerase of SARS-CoV-2, NSP12, a highly conserved central component of coronaviral replication and transcription machinery, promoted the activation of RIPK1. Furthermore, NSP12 323L variant, encoded by the SARS-CoV-2 C14408T variant first detected in Lombardy, Italy, that carries a Pro323Leu amino acid substitution in NSP12, showed increased ability to activate RIPK1. Inhibition of RIPK1 downregulated the transcriptional induction of proinflammatory cytokines and host factors including ACE2 and EGFR that promote viral entry into cells. Our results suggest that SARS-CoV-2 may have an unexpected and unusual ability to hijack the RIPK1-mediated host defense response to promote its own propagation and that inhibition of RIPK1 may provide a therapeutic option for the treatment of COVID-19.

Application of omics technology to combat the COVID-19 pandemic

As of August 27, 2021, the ongoing pandemic of coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has spread to over 220 countries, areas, and territories. Thus far, 214,468,601 confirmed cases, including 4,470,969 deaths, have been reported to the World Health Organization. To combat the COVID-19 pandemic, multiomics-based strategies, including genomics, transcriptomics, proteomics, and metabolomics, have been used to study the diagnosis methods, pathogenesis, prognosis, and potential drug targets of COVID-19. In order to help researchers and clinicians to keep up with the knowledge of COVID-19, we summarized the most recent progresses reported in omics-based research papers. This review discusses omics-based approaches for studying COVID-19, summarizing newly emerged SARS-CoV-2 variants as well as potential diagnostic methods, risk factors, and pathological features of COVID-19. This review can help researchers and clinicians gain insight into COVID-19 features, providing direction for future drug development and guidance for clinical treatment, so that patients can receive appropriate treatment as soon as possible to reduce the risk of disease progression.

Age-sex population adjusted analysis of disease severity in epidemics as a tool to devise public health policies for COVID-19

Governments continue to update social intervention strategies to contain COVID-19 infections. However, investigation of COVID-19 severity indicators across the population might help to design more precise strategies, balancing the need to keep people safe and to reduce the socio-economic burden of generalized restriction precedures. Here, we propose a method for age-sex population-adjusted analysis of disease severity in epidemics that has the advantage to use simple and repeatable variables, which are daily or weekly available. This allows to monitor the effect of public health policies in short term, and to repeat these calculations over time to surveille epidemic dynamics and impact. Our method can help to define a risk-categorization of likeliness to develop a severe COVID-19 disease which requires intensive care or is indicative of a higher risk of dying. Indeed, analysis of suitable open-access COVID-19 data in three European countries indicates that individuals in the 0-40 age interval and females under 60 are significantly less likely to develop a severe condition and die, whereas males equal or above 60 are more likely at risk of severe disease and death. Hence, a combination of age-adaptive and sex-balanced guidelines for social interventions could represent key public health management tools for policymakers.

Mechanogenomic coupling of lung tissue stiffness, EMT and coronavirus pathogenicity

In this Current Opinion, we highlight the importance of the material properties of tissues and how alterations therein, which influence epithelial-to-mesenchymal transitions, represent an important layer of regulation in a number of diseases and potentially also play a critical role in host-pathogen interactions. In light of the current SARS-CoV-2 pandemic, we here highlight the possible role of lung tissue stiffening with ageing and how this might facilitate increased SARS-CoV-2 replication through matrix-stiffness dependent epithelial-to-mesenchymal transitions of the lung epithelium. This emphasizes the need for integrating material properties of tissues in drug discovery programs.

Lessons from the COVID-19 pandemic for advancing computational drug repurposing strategies

Responding quickly to unknown pathogens is crucial to stop uncontrolled spread of diseases that lead to epidemics, such as the novel coronavirus, and to keep protective measures at a level that causes as little social and economic harm as possible. This can be achieved through computational approaches that significantly speed up drug discovery. A powerful approach is to restrict the search to existing drugs through drug repurposing, which can vastly accelerate the usually long approval process. In this Review, we examine a representative set of currently used computational approaches to identify repurposable drugs for COVID-19, as well as their underlying data resources. Furthermore, we compare drug candidates predicted by computational methods to drugs being assessed by clinical trials. Finally, we discuss lessons learned from the reviewed research efforts, including how to successfully connect computational approaches with experimental studies, and propose a unified drug repurposing strategy for better preparedness in the case of future outbreaks.