Artificial intelligence predicts the immunogenic landscape of SARS-CoV-2 leading to universal blueprints for vaccine designs

NeoAgDT: optimization of personal neoantigen vaccine composition by digital twin simulation of a cancer cell population

MOTIVATION

Neoantigen vaccines make use of tumor-specific mutations to enable the patient's immune system to recognize and eliminate cancer. Selecting vaccine elements, however, is a complex task which needs to take into account not only the underlying antigen presentation pathway but also tumor heterogeneity.

RESULTS

Here, we present NeoAgDT, a two-step approach consisting of: (i) simulating individual cancer cells to create a digital twin of the patient's tumor cell population and (ii) optimizing the vaccine composition by integer linear programming based on this digital twin. NeoAgDT shows improved selection of experimentally validated neoantigens over ranking-based approaches in a study of seven patients.

AVAILABILITY AND IMPLEMENTATION

The NeoAgDT code is published on Github: https://github.com/nec-research/neoagdt.

How could our genetics impact COVID-19 vaccine response?

INTRODUCTION

The COVID-19 pandemic, caused by the SARS-CoV-2 virus, has posed unprecedented global health challenges since its emergence in December 2019. The rapid availability of vaccines has been estimated to save millions of lives, but there is variation in how individuals respond to vaccines, influencing their effectiveness at an individual, and population level.

AREAS COVERED

This review focuses on human genetic factors influencing the immune response and effectiveness of vaccines, highlighting the importance of associations across the HLA locus. Genome-Wide Association Studies (GWAS) and other genetic association analyses have identified statistically significant associations between specific HLA alleles including HLA-DRB1*13, DBQ1*06, and A*03 impacting antibody responses and the risk of breakthrough infections post-vaccination. Relationships between these associations and potential mechanisms and links with risks of natural infection or disease are explored, and this review concludes by emphasizing how understanding the mechanisms of these genetic determinants may inform the development of tailored vaccination strategies.

EXPERT OPINION

Although complex, we believe these findings from the SARS-CoV2 pandemic offer a unique opportunity to understand the relationships between HLA and infection and vaccine response, with a goal of optimizing individual protection against COVID-19 in the ongoing pandemic, and possibly influencing wider vaccine development in the future.

Development and use of machine learning algorithms in vaccine target selection

Computer-aided discovery of vaccine targets has become a cornerstone of rational vaccine design. In this article, I discuss how Machine Learning (ML) can inform and guide key computational steps in rational vaccine design concerned with the identification of B and T cell epitopes and correlates of protection. I provide examples of ML models, as well as types of data and predictions for which they are built. I argue that interpretable ML has the potential to improve the identification of immunogens also as a tool for scientific discovery, by helping elucidate the molecular processes underlying vaccine-induced immune responses. I outline the limitations and challenges in terms of data availability and method development that need to be addressed to bridge the gap between advances in ML predictions and their translational application to vaccine design.

People who use drugs show no increase in pre-existing T-cell cross-reactivity toward SARS-CoV-2 but develop a normal polyfunctional T-cell response after standard mRNA vaccination

People who use drugs (PWUD) are at a high risk of contracting and developing severe coronavirus disease 2019 (COVID-19) and other infectious diseases due to their lifestyle, comorbidities, and the detrimental effects of opioids on cellular immunity. However, there is limited research on vaccine responses in PWUD, particularly regarding the role that T cells play in the immune response to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Here, we show that before vaccination, PWUD did not exhibit an increased frequency of preexisting cross-reactive T cells to SARS-CoV-2 and that, despite the inhibitory effects that opioids have on T-cell immunity, standard vaccination can elicit robust polyfunctional CD4+ and CD8+ T-cell responses that were similar to those found in controls. Our findings indicate that vaccination stimulates an effective immune response in PWUD and highlight targeted vaccination as an essential public health instrument for the control of COVID-19 and other infectious diseases in this group of high-risk patients.

Artificial Intelligence in battling infectious diseases: A transformative role

It is widely acknowledged that infectious diseases have wrought immense havoc on human society, being regarded as adversaries from which humanity cannot elude. In recent years, the advancement of Artificial Intelligence (AI) technology has ushered in a revolutionary era in the realm of infectious disease prevention and control. This evolution encompasses early warning of outbreaks, contact tracing, infection diagnosis, drug discovery, and the facilitation of drug design, alongside other facets of epidemic management. This article presents an overview of the utilization of AI systems in the field of infectious diseases, with a specific focus on their role during the COVID-19 pandemic. The article also highlights the contemporary challenges that AI confronts within this domain and posits strategies for their mitigation. There exists an imperative to further harness the potential applications of AI across multiple domains to augment its capacity in effectively addressing future disease outbreaks.

Experimental validation of immunogenic SARS-CoV-2 T cell epitopes identified by artificial intelligence

During the COVID-19 pandemic we utilized an AI-driven T cell epitope prediction tool, the NEC Immune Profiler (NIP) to scrutinize and predict regions of T cell immunogenicity (hotspots) from the entire SARS-CoV-2 viral proteome. These immunogenic regions offer potential for the development of universally protective T cell vaccine candidates. Here, we validated and characterized T cell responses to a set of minimal epitopes from these AI-identified universal hotspots. Utilizing a flow cytometry-based T cell activation-induced marker (AIM) assay, we identified 59 validated screening hits, of which 56% (33 peptides) have not been previously reported. Notably, we found that most of these novel epitopes were derived from the non-spike regions of SARS-CoV-2 (Orf1ab, Orf3a, and E). In addition, ex vivo stimulation with NIP-predicted peptides from the spike protein elicited CD8+ T cell response in PBMC isolated from most vaccinated donors. Our data confirm the predictive accuracy of AI platforms modelling bona fide immunogenicity and provide a novel framework for the evaluation of vaccine-induced T cell responses.

Humoral and cellular immune responses in fully vaccinated individuals with or without SARS-CoV-2 breakthrough infection: Results from the CoV-ADAPT cohort

Despite recent advances in prophylactic vaccination, SARS-CoV-2 infections continue to cause significant morbidity. A better understanding of immune response differences between vaccinated individuals with and without later SARS-CoV-2 breakthrough infection is urgently needed. CoV-ADAPT is a prospective long-term study comparing humoral (anti-spike-RBD-IgG, neutralization capacity, avidity) and cellular (spike-induced T-cell interferon-γ [IFN-γ] release) immune responses in individuals vaccinated against SARS-CoV-2 at four different time points (three before and one after third vaccination). In this cohort study, 62 fully vaccinated individuals presented with SARS-CoV-2 breakthrough infections vs 151 without infection 3-7 months following third vaccination. Breakthrough infections significantly increased anti-spike-RBD-IgG (p < 0.01), but not spike-directed T-cell IFN-γ release (TC) or antibody avidity. Despite comparable surrogate neutralization indices, the functional neutralization capacity against SARS-CoV-2-assessed via a tissue culture-based assay-was significantly higher following breakthrough vs no breakthrough infection. Anti-spike-RBD-IgG and antibody avidity decreased with age (p < 0.01) and females showed higher anti-spike-RBD-IgG (p < 0.01), and a tendency towards higher antibody avidity (p = 0.051). The association between humoral and cellular immune responses previously reported at various time points was lost in subjects after breakthrough infections (p = 0.807). Finally, a machine-learning approach based on our large immunological dataset (a total of 49 variables) from different time points was unable to predict breakthrough infections (area under the curve: 0.55). In conclusion, distinct differences in humoral vs cellular immune responses in fully vaccinated individuals with or without breakthrough infection could be demonstrated. Breakthrough infections predominantly drive the humoral response without boosting the cellular component. Breakthrough infections could not be predicted based on immunological data, which indicates a superior role of environmental factors (e.g., virus exposure) in individualized risk assessment.

Carbohydrates as putative pattern recognition receptor agonists in vaccine development

Adjuvants are essential components of modern vaccines. One general mechanism underlying their immunostimulatory functions is the activation of pattern recognition receptors (PRRs) of innate immune cells. Carbohydrates - as essential signaling molecules on microbial surfaces - are potent PRR agonists and candidate materials for adjuvant design. Here, we summarize the latest trends in developing carbohydrate-containing adjuvants, with fresh opinions on how the physicochemical characteristics of the glycans (e.g., molecular size, assembly status, monosaccharide components, and functional group patterns) affect their adjuvant activities in aiding antigen transport, regulating antigen processing, and enhancing adaptive immune responses. From a translational perspective, we also discuss potential technologies for solving long-lasting challenges in carbohydrate adjuvant design.

The interplay between neoantigens and immune cells in sarcomas treated with checkpoint inhibition

Introduction

Sarcomas are comprised of diverse bone and connective tissue tumors with few effective therapeutic options for locally advanced unresectable and/or metastatic disease. Recent advances in immunotherapy, in particular immune checkpoint inhibition (ICI), have shown promising outcomes in several cancer indications. Unfortunately, ICI therapy has provided only modest clinical responses and seems moderately effective in a subset of the diverse subtypes.

Methods

To explore the immune parameters governing ICI therapy resistance or immune escape, we performed whole exome sequencing (WES) on tumors and their matched normal blood, in addition to RNA-seq from tumors of 31 sarcoma patients treated with pembrolizumab. We used advanced computational methods to investigate key immune properties, such as neoantigens and immune cell composition in the tumor microenvironment (TME).

Results

A multifactorial analysis suggested that expression of high quality neoantigens in the context of specific immune cells in the TME are key prognostic markers of progression-free survival (PFS). The presence of several types of immune cells, including T cells, B cells and macrophages, in the TME were associated with improved PFS. Importantly, we also found the presence of both CD8+ T cells and neoantigens together was associated with improved survival compared to the presence of CD8+ T cells or neoantigens alone. Interestingly, this trend was not identified with the combined presence of CD8+ T cells and TMB; suggesting that a combined CD8+ T cell and neoantigen effect on PFS was important.

Discussion

The outcome of this study may inform future trials that may lead to improved outcomes for sarcoma patients treated with ICI.

Robust spike-specific CD4+ and CD8+ T cell responses in SARS-CoV-2 vaccinated hematopoietic cell transplantation recipients: a prospective, cohort study

Poor overall survival of hematopoietic stem cell transplantation (HSCT) recipients who developed COVID-19 underlies the importance of SARS-CoV-2 vaccination. Previous studies of vaccine efficacy have reported weak humoral responses but conflicting results on T cell immunity. Here, we have examined the relationship between humoral and T cell response in 48 HSCT recipients who received two doses of Moderna's mRNA-1273 or Pfizer/BioNTech's BNT162b2 vaccines. Nearly all HSCT patients had robust T cell immunity regardless of protective humoral responses, with 18/48 (37%, IQR 8.679-5601 BAU/mL) displaying protective IgG anti-receptor binding domain (RBD) levels (>2000 BAU/mL). Flow cytometry analysis of activation induced markers (AIMs) revealed that 90% and 74% of HSCT patients showed reactivity towards immunodominant spike peptides in CD8⁺ and CD4⁺ T cells, respectively. The response rate increased to 90% for CD4⁺ T cells as well when we challenged the cells with a complete set of overlapping peptides spanning the entire spike protein. T cell response was detectable as early as 3 months after transplant, but only CD4⁺ T cell reactivity correlated with IgG anti-RBD level and time after transplantation. Boosting increased seroconversion rate, while only one patient developed COVID-19 requiring hospitalization. Our data suggest that HSCT recipients with poor serological responses were protected from severe COVID-19 by vaccine-induced T cell responses.

Towards systems immunology of critical illness at scale: from single cell 'omics to digital twins

Single-cell 'omics methodology has yielded unprecedented insights based largely on data-centric informatics for reducing, and thus interpreting, massive datasets. In parallel, parsimonious mathematical modeling based on abstractions of pathobiology has also yielded major insights into inflammation and immunity, with these models being extended to describe multi-organ disease pathophysiology as the basis of 'digital twins' and in silico clinical trials. The integration of these distinct methods at scale can drive both basic and translational advances, especially in the context of critical illness, including diseases such as COVID-19. Here, I explore achievements and argue the challenges that are inherent to the integration of data-driven and mechanistic modeling approaches, highlighting the potential of modeling-based strategies for rational immune system reprogramming.

SARS-CoV-2 Vaccines, Vaccine Development Technologies, and Significant Efforts in Vaccine Development during the Pandemic: The Lessons Learned Might Help to Fight against the Next Pandemic

We are currently approaching three years since the beginning of the coronavirus disease 2019 (COVID-19) pandemic. SARS-CoV-2 has caused extensive disruptions in everyday life, public health, and the global economy. Thus far, the vaccine has worked better than expected against the virus. During the pandemic, we experienced several things, such as the virus and its pathogenesis, clinical manifestations, and treatments; emerging variants; different vaccines; and the vaccine development processes. This review describes how each vaccine has been developed and approved with the help of modern technology. We also discuss critical milestones during the vaccine development process. Several lessons were learned from different countries during the two years of vaccine research, development, clinical trials, and vaccination. The lessons learned during the vaccine development process will help to fight the next pandemic.

The race to understand immunopathology in COVID-19: Perspectives on the impact of quantitative approaches to understand within-host interactions

The COVID-19 pandemic has revealed the need for the increased integration of modelling and data analysis to public health, experimental, and clinical studies. Throughout the first two years of the pandemic, there has been a concerted effort to improve our understanding of the within-host immune response to the SARS-CoV-2 virus to provide better predictions of COVID-19 severity, treatment and vaccine development questions, and insights into viral evolution and the impacts of variants on immunopathology. Here we provide perspectives on what has been accomplished using quantitative methods, including predictive modelling, population genetics, machine learning, and dimensionality reduction techniques, in the first 26 months of the COVID-19 pandemic approaches, and where we go from here to improve our responses to this and future pandemics.

Attentive Variational Information Bottleneck for TCR-peptide interaction prediction

MOTIVATION

We present a multi-sequence generalization of Variational Information Bottleneck and call the resulting model Attentive Variational Information Bottleneck (AVIB). Our AVIB model leverages multi-head self-attention to implicitly approximate a posterior distribution over latent encodings conditioned on multiple input sequences. We apply AVIB to a fundamental immuno-oncology problem: predicting the interactions between T-cell receptors (TCRs) and peptides.

RESULTS

Experimental results on various datasets show that AVIB significantly outperforms state-of-the-art methods for TCR-peptide interaction prediction. Additionally, we show that the latent posterior distribution learned by AVIB is particularly effective for the unsupervised detection of out-of-distribution amino acid sequences.

AVAILABILITY AND IMPLEMENTATION

The code and the data used for this study are publicly available at: https://github.com/nec-research/vibtcr.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Prediction of Recurrent Mutations in SARS-CoV-2 Using Artificial Neural Networks

Predicting SARS-CoV-2 mutations is difficult, but predicting recurrent mutations driven by the host, such as those caused by host deaminases, is feasible. We used machine learning to predict which positions from the SARS-CoV-2 genome will hold a recurrent mutation and which mutations will be the most recurrent. We used data from April 2021 that we separated into three sets: a training set, a validation set, and an independent test set. For the test set, we obtained a specificity value of 0.69, a sensitivity value of 0.79, and an Area Under the Curve (AUC) of 0.8, showing that the prediction of recurrent SARS-CoV-2 mutations is feasible. Subsequently, we compared our predictions with updated data from January 2022, showing that some of the false positives in our prediction model become true positives later on. The most important variables detected by the model's Shapley Additive exPlanation (SHAP) are the nucleotide that mutates and RNA reactivity. This is consistent with the SARS-CoV-2 mutational bias pattern and the preference of some host deaminases for specific sequences and RNA secondary structures. We extend our investigation by analyzing the mutations from the variants of concern Alpha, Beta, Delta, Gamma, and Omicron. Finally, we analyzed amino acid changes by looking at the predicted recurrent mutations in the M-pro and spike proteins.

Characterization of the T cell receptor repertoire and melanoma tumor microenvironment upon combined treatment with ipilimumab and hTERT vaccination

Background

This clinical trial evaluated a novel telomerase-targeting therapeutic cancer vaccine, UV1, in combination with ipilimumab, in patients with metastatic melanoma. Translational research was conducted on patient-derived blood and tissue samples with the goal of elucidating the effects of treatment on the T cell receptor repertoire and tumor microenvironment.

Methods

The trial was an open-label, single-center phase I/IIa study. Eligible patients had unresectable metastatic melanoma. Patients received up to 9 UV1 vaccinations and four ipilimumab infusions. Clinical responses were assessed according to RECIST 1.1. Patients were followed up for progression-free survival (PFS) and overall survival (OS). Whole-exome and RNA sequencing, and multiplex immunofluorescence were performed on the biopsies. T cell receptor (TCR) sequencing was performed on the peripheral blood and tumor tissues.

Results

Twelve patients were enrolled in the study. Vaccine-specific immune responses were detected in 91% of evaluable patients. Clinical responses were observed in four patients. The mPFS was 6.7 months, and the mOS was 66.3 months. There was no association between baseline tumor mutational burden, neoantigen load, IFN-γ gene signature, tumor-infiltrating lymphocytes, and response to therapy. Tumor telomerase expression was confirmed in all available biopsies. Vaccine-enriched TCR clones were detected in blood and biopsy, and an increase in the tumor IFN-γ gene signature was detected in clinically responding patients.

Conclusion

Clinical responses were observed irrespective of established predictive biomarkers for checkpoint inhibitor efficacy, indicating an added benefit of the vaccine-induced T cells. The clinical and immunological read-out warrants further investigation of UV1 in combination with checkpoint inhibitors.

Trial registration Clinicaltrials.gov identifier: NCT02275416. Registered October 27, 2014. https://clinicaltrials.gov/ct2/show/NCT02275416?term=uv1&draw=2&rank=6

Supplementary Information

The online version contains supplementary material available at 10.1186/s12967-022-03624-z.

Malaria vaccine approaches leveraging technologies optimized in the COVID-19 era

Africa bears the greatest burden of malaria with more than 200 million clinical cases and more than 600,000 deaths in 2020 alone. While malaria-associated deaths dropped steadily until 2015, the decline started to falter after 2016, highlighting the need for novel potent tools in the fight against malaria. Currently available tools, such as antimalarial drugs and insecticides are threatened by development of resistance by the parasite and the mosquito. The WHO has recently approved RTS,S as the first malaria vaccine for public health use. However, because the RTS,S vaccine has an efficacy of only 36% in young children, there is need for more efficacious vaccines. Indeed, based on the global goal of licensing a malaria vaccine with at least 75% efficacy by 2030, RTS,S is unlikely to be sufficient alone. However, recent years have seen tremendous progress in vaccine development. Although the COVID-19 pandemic impacted malaria control, the rapid progress in research towards the development of COVID-19 vaccines indicate that harnessing funds and technological advances can remarkably expedite vaccine development. In this review, we highlight and discuss current and prospective trends in global efforts to discover and develop malaria vaccines through leveraging mRNA vaccine platforms and other systems optimized during COVID-19 vaccine studies.

Immunology to Immunotherapeutics of SARS-CoV-2: Identification of Immunogenic Epitopes for Vaccine Development

The emergence of COVID19 pandemic caused by SARS-CoV-2 virus has created a global public health and socio-economic crisis. Immunoinformatics-based approaches to investigate the potential antigens is the fastest way to move towards a multiepitope-based vaccine development. This review encompasses the underlying mechanisms of pathogenesis, innate and adaptive immune signaling along with evasion pathways of SARS-CoV-2. Furthermore, it compiles the promiscuous peptides from in silico studies which are subjected to prediction of cytokine milieu using web-based servers. Out of the 434 peptides retrieved from all studies, we have identified 33 most promising T cell vaccine candidates. This review presents a list of the most potential epitopes from several proteins of the virus based on their immunogenicity, homology, conservancy and population coverage studies. These epitopes can form a basis of second generation of vaccine development as the first generation vaccines in various stages of trials mostly focus only on Spike protein. We therefore, propose them as most potential candidates which can be taken up immediately for confirmation by experimental studies.

The T Cell Epitope Landscape of SARS-CoV-2 Variants of Concern

During the COVID-19 pandemic, several SARS-CoV-2 variants of concern (VOC) emerged, bringing with them varying degrees of health and socioeconomic burdens. In particular, the Omicron VOC displayed distinct features of increased transmissibility accompanied by antigenic drift in the spike protein that partially circumvented the ability of pre-existing antibody responses in the global population to neutralize the virus. However, T cell immunity has remained robust throughout all the different VOC transmission waves and has emerged as a critically important correlate of protection against SARS-CoV-2 and its VOCs, in both vaccinated and infected individuals. Therefore, as SARS-CoV-2 VOCs continue to evolve, it is crucial that we characterize the correlates of protection and the potential for immune escape for both B cell and T cell human immunity in the population. Generating the insights necessary to understand T cell immunity, experimentally, for the global human population is at present a critical but a time consuming, expensive, and laborious process. Further, it is not feasible to generate global or universal insights into T cell immunity in an actionable time frame for potential future emerging VOCs. However, using computational means we can expedite and provide early insights into the correlates of T cell protection. In this study, we generated and revealed insights on the T cell epitope landscape for the five main SARS-CoV-2 VOCs observed to date. We demonstrated using a unique AI prediction platform, a significant conservation of presentable T cell epitopes across all mutated peptides for each VOC. This was modeled using the most frequent HLA alleles in the human population and covers the most common HLA haplotypes in the human population. The AI resource generated through this computational study and associated insights may guide the development of T cell vaccines and diagnostics that are even more robust against current and future VOCs, and their emerging subvariants.

Ecological Landscape Design and Evaluation in Public Charging Facilities for Electric Vehicles

The deterioration of the environment in the 21st century has made environmental issues one of the most severe tests for modern society. With this comes a change in energy structure from high-carbon to low-carbon direction, and electric vehicles are gradually developing into the darling of a city with low-carbon transportation and safe travel. This paper carries out a systematic analysis of landscape design and environmental protection in the development of new energy electric vehicle charging facilities in urban habitat. By categorizing the content and provisions of published domestic and international standards, new requirements for standardization are obtained, including barrier-free design, electromagnetic radiation, child safety protection, and urban landscape integration. Among them, ecological landscape public charging facilities can enhance the overall quality of urban environment. This paper analyzes the necessity of landscape design in charging facilities, explores the ecological concepts extended by macroscopic landscape design principles and the problems of public charging facilities, and proposes a design and evaluation method of ecologically landscaped public charging facilities based on hierarchical analysis and neural networks. The hierarchical analysis method is introduced to establish a landscape design assessment index system, and then a neural network is introduced to describe the characteristics of electric vehicle charging, and the landscape design assessment learning samples are trained to establish a landscape design assessment model. Finally, a comparison experiment is conducted with other landscape design assessment methods using specific examples, and the results show that the proposed method has more obvious advantages in ecological landscape public charging facility design assessment with high accuracy, faster landscape design assessment, charging efficiency, and environmental protection.

Artificial Intelligence-Based Data-Driven Strategy to Accelerate Research, Development, and Clinical Trials of COVID Vaccine

The global COVID-19 (coronavirus disease 2019) pandemic, which was caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has resulted in a significant loss of human life around the world. The SARS-CoV-2 has caused significant problems to medical systems and healthcare facilities due to its unexpected global expansion. Despite all of the efforts, developing effective treatments, diagnostic techniques, and vaccinations for this unique virus is a top priority and takes a long time. However, the foremost step in vaccine development is to identify possible antigens for a vaccine. The traditional method was time taking, but after the breakthrough technology of reverse vaccinology (RV) was introduced in 2000, it drastically lowers the time needed to detect antigens ranging from 5-15 years to 1-2 years. The different RV tools work based on machine learning (ML) and artificial intelligence (AI). Models based on AI and ML have shown promising solutions in accelerating the discovery and optimization of new antivirals or effective vaccine candidates. In the present scenario, AI has been extensively used for drug and vaccine research against SARS-COV-2 therapy discovery. This is more useful for the identification of potential existing drugs with inhibitory human coronavirus by using different datasets. The AI tools and computational approaches have led to speedy research and the development of a vaccine to fight against the coronavirus. Therefore, this paper suggests the role of artificial intelligence in the field of clinical trials of vaccines and clinical practices using different tools.

Artificial intelligence and clinical data suggest the T cell-mediated SARS-CoV-2 nonstructural protein intranasal vaccines for global COVID-19 immunity

Advanced computational methodologies suggested SARS-CoV-2, nonstructural proteins ORF1AB, ORF3a, as the source of immunodominant peptides for T cell presentation. T cell immunity is long-lasting and compatible with COVID-19 pathology. Based on the supporting clinical data, nonstructural SARS-CoV-2 protein vaccines could provide global immunity against COVID-19.

Genetic and Structural Analysis of SARS-CoV-2 Spike Protein for Universal Epitope Selection

Evaluation of immunogenic epitopes for universal vaccine development in the face of ongoing SARS-CoV-2 evolution remains a challenge. Herein, we investigate the genetic and structural conservation of an immunogenically relevant epitope (C662-C671) of spike (S) protein across SARS-CoV-2 variants to determine its potential utility as a broad-spectrum vaccine candidate against coronavirus diseases. Comparative sequence analysis, structural assessment, and molecular dynamics simulations of C662-C671 epitope were performed. Mathematical tools were employed to determine its mutational cost. We found that the amino acid sequence of C662-C671 epitope is entirely conserved across the observed major variants of SARS-CoV-2 in addition to SARS-CoV. Its conformation and accessibility are predicted to be conserved, even in the highly mutated Omicron variant. Costly mutational rate in the context of energy expenditure in genome replication and translation can explain this strict conservation. These observations may herald an approach to developing vaccine candidates for universal protection against emergent variants of coronavirus.

Rapid evolution of SARS-CoV-2 challenges human defenses

The race between pathogens and their hosts is a major evolutionary driver, where both reshuffle their genomes to overcome and reorganize the defenses for infection, respectively. Evolutionary theory helps formulate predictions on the future evolutionary dynamics of SARS-CoV-2, which can be monitored through unprecedented real-time tracking of SARS-CoV-2 population genomics at the global scale. Here we quantify the accelerating evolution of SARS-CoV-2 by tracking the SARS-CoV-2 mutation globally, with a focus on the Receptor Binding Domain (RBD) of the spike protein determining infection success. We estimate that the > 820 million people that had been infected by October 5, 2021, produced up to 10²¹ copies of the virus, with 12 new effective RBD variants appearing, on average, daily. Doubling of the number of RBD variants every 89 days, followed by selection of the most infective variants challenges our defenses and calls for a shift to anticipatory, rather than reactive tactics involving collaborative global sequencing and vaccination.

Application of artificial intelligence in COVID-19 medical area: a systematic review

Background

Coronavirus disease 2019 (COVID-19) has caused a large-scale global epidemic, impacting international politics and the economy. At present, there is no particularly effective medicine and treatment plan. Therefore, it is urgent and significant to find new technologies to diagnose early, isolate early, and treat early. Multimodal data drove artificial intelligence (AI) can potentially be the option. During the COVID-19 Pandemic, AI provided cutting-edge applications in disease, medicine, treatment, and target recognition. This paper reviewed the literature on the intersection of AI and medicine to analyze and compare different AI model applications in the COVID-19 Pandemic, evaluate their effectiveness, show their advantages and differences, and introduce the main models and their characteristics.

Methods

We searched PubMed, arXiv, medRxiv, and Google Scholar through February 2020 to identify studies on AI applications in the medical areas for the COVID-19 Pandemic.

Results

We summarize the main AI applications in six areas: (I) epidemiology, (II) diagnosis, (III) progression, (IV) treatment, (V) psychological health impact, and (VI) data security. The ongoing development in AI has significantly improved prediction, contact tracing, screening, diagnosis, treatment, medication, and vaccine development for the COVID-19 Pandemic and reducing human intervention in medical practice.

Discussion

This paper provides strong advice for using AI-based auxiliary tools for related applications of human diseases. We also discuss the clinicians’ role in the further development of AI. They and AI researchers can integrate AI technology with current clinical processes and information systems into applications. In the future, AI personnel and medical workers will further cooperate closely.

Artificial Intelligence in Vaccine and Drug Design

Knowledge in the fields of biochemistry, structural biology, immunological principles, microbiology, and genomics has all increased dramatically in recent years. There has also been tremendous growth in the fields of data science, informatics, and artificial intelligence needed to handle this immense data flow. At the intersection of wet lab and data science is the field of bioinformatics, which seeks to apply computational tools to better understanding of the biological sciences. Like so many other areas of biology, bioinformatics has transformed immunology research leading to the discipline of immunoinformatics. Within this field, many new databases and computational tools have been created that increasingly drive immunology research, in many cases drawing upon artificial intelligence and machine learning to predict complex immune system behaviors, for example, prediction of B cell and T cell epitopes. In this book chapter, we provide an overview of computational tools and artificial intelligence being used for protein modeling, drug screening, vaccine design, and highlight how these tools are being used to transform approaches to pandemic countermeasure development, by reference to the current COVID-19 pandemic.

Role of artificial intelligence in fast-track drug discovery and vaccine development for COVID-19

COVID-19 is a global pandemic spread across more than 200 countries and several measures are being taken to control it. Researchers in pharmaceutical academia/industry are incessantly targeting this disease through vaccine and drug development protocols. Artificial intelligence is being extensively explored for surveillance, diagnostics, contact tracing, and for clinical management of COVID-19. The most common application has been for repurposing of existing drugs through various AI tools. Successful training of artificial neural networks based on identification of specific patterns in binding of known antiviral drugs with protein sequences from diverse virus species have generated models giving good predictions for molecules against SARS-CoV-2 virus, in sync with clinical studies. ML tools have also been used to investigate immunogenic components of the virus to be exploited as vaccine candidates. In this chapter, the utilization of artificial intelligence to accelerate drug-design and vaccine design research for COVID-19 has been reviewed.

Severe acute respiratory syndrome coronavirus-2: An era of struggle and discovery leading to the emergency use authorization of treatment and prevention measures based on computational analysis

Severe acute respiratory syndrome-Coronavirus-2 (SARS-CoV-2), a novel betacoronavirus, has surprised the world with its disease spread and mortality rate. SARS-CoV-2 is a positive-sense, enveloped RNA virus that can infect various organs of the body, potentially leading to multiple organ dysfunction and eventual death. While various medications have received emergency use authorizations (EUAs) for the treatment of Coronavirus disease-2019 (COVID-19), as of April 30, 2021, only one drug has been Food and Drug Administration (FDA)-approved: remdesivir. Currently, three vaccines have received EUAs in the United States, but none are FDA-approved. This shortage of treatments and prevention measures is extremely problematic. Thus computational approaches would provide important data about drug resistance and variants. Such data will be useful for the development of drugs and vaccines. This chapter is a synopsis of SARS-CoV-2 clinical presentation, COVID-19 symptomology, treatment, prevention mechanisms, and SARS-CoV-2 variants using computational analysis.

Pandemic strategies with computational and structural biology against COVID-19: A retrospective

The emergence of the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), which is the etiologic agent of the coronavirus disease 2019 (COVID-19) pandemic, has dominated all aspects of life since of 2020. Research studies on the virus and exploration of therapeutic and preventive strategies has been moving at rapid rates to control the pandemic. In the field of bioinformatics or computational and structural biology, recent research strategies have used multiple disciplines to compile large datasets to uncover statistical correlations and significance, visualize and model proteins, perform molecular dynamics simulations, and employ the help of artificial intelligence and machine learning to harness computational processing power to further the research on COVID-19, including drug screening, drug design, vaccine development, prognosis prediction, and outbreak prediction. These recent developments should help us better understand the viral disease and develop the much-needed therapies and strategies for the management of COVID-19.

Investigation of the impact of SARS-CoV infection on the immunologic status and lung function after 15 years

BACKGROUND

We investigate the long-term effects of SARS-CoV on patients' lung and immune systems 15 years post-infection. SARS-CoV-2 pandemic is ongoing however, another genetically related beta-coronavirus SARS-CoV caused an epidemic in 2003-2004.

METHODS

We enrolled 58 healthcare workers from Peking University People's Hospital who were infected with SARS-CoV in 2003. We evaluated lung damage by mMRC score, pulmonary function tests, and chest CT. Immune function was assessed by their serum levels of globin, complete components, and peripheral T cell subsets. ELISA was used to detect SARS-CoV-specific IgG antibodies in sera.

RESULTS

After 15 years of disease onset, 19 (36.5%), 8 (34.6%), and 19 (36.5%) subjects had impaired DL (CO), RV, and FEF25-75, respectively. 17 (30.4%) subjects had an mMRC score ≥ 2. Fourteen (25.5%) cases had residual CT abnormalities. T regulatory cells were a bit higher in the SARS survivors. IgG antibodies against SARS S-RBD protein and N protein were detected in 11 (18.97%) and 12 (20.69%) subjects, respectively. Subgroup analysis revealed that small airway dysfunction and CT abnormalities were more common in the severe group than in the non-severe group (57.1% vs 22.6%, 54.5% vs 6.1%, respectively, p < 0.05).

CONCLUSIONS

SARS-CoV could cause permanent damage to the lung, which requires early pulmonary rehabilitation. The long-lived immune memory response against coronavirus requires further studies to assess the potential benefit. Trial registration ClinicalTrials.gov, NCT03443102. Registered prospectively on 25 January 2018.

Construction of a multiepitope vaccine candidate against Fasciola hepatica: an in silico design using various immunogenic excretory/secretory antigens

BACKGROUND

Fasciola hepatica is an important pathogen that causes liver fluke disease in definitive hosts such as livestock animals and humans. Various excretory/secretory products have been used in serological diagnosis and vaccination studies targeting fasciolosis. There are no commercial vaccines against fasciolosis yet. Bioinformatic analysis based on computational methods have lower cost and provide faster output compared to conventional vaccine antigen discovery techniques. The aim of this study was to predict B- and T-cell specific epitopes of four excretory/secretory antigens (Kunitz-type serine protease inhibitor, cathepsin L1, helminth defense molecule, and glutathione S-transferase) of Fasciola hepatica and to construct a multiepitope vaccine candidate against fasciolosis.

METHODS AND RESULTS

Initially, nonallergic and the highest antigenic B- and T- cell epitopes were selected and then, physico-chemical parameters, secondary and tertiary structures of designed multiepitope vaccine candidate were predicted. Tertiary structure was refined and validated using online bioinformatic tools. Linear and discontinuous B-cell epitopes and disulfide bonds were determined. Finally, molecular docking analysis for MHC-I and MHC-II receptors was performed.

CONCLUSION

This multi-epitope vaccine candidate antigen, with high immunological properties, can be considered as a promising vaccine candidate for animal experiments and wet lab studies.

Artificial Intelligence for COVID-19: A Systematic Review

Background: Recently, Coronavirus Disease 2019 (COVID-19), caused by severe acute respiratory syndrome virus 2 (SARS-CoV-2), has affected more than 200 countries and lead to enormous losses. This study systematically reviews the application of Artificial Intelligence (AI) techniques in COVID-19, especially for diagnosis, estimation of epidemic trends, prognosis, and exploration of effective and safe drugs and vaccines; and discusses the potential limitations. Methods: We report this systematic review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. We searched PubMed, Embase and the Cochrane Library from inception to 19 September 2020 for published studies of AI applications in COVID-19. We used PROBAST (prediction model risk of bias assessment tool) to assess the quality of literature related to the diagnosis and prognosis of COVID-19. We registered the protocol (PROSPERO CRD42020211555). Results: We included 78 studies: 46 articles discussed AI-assisted diagnosis for COVID-19 with total accuracy of 70.00 to 99.92%, sensitivity of 73.00 to 100.00%, specificity of 25 to 100.00%, and area under the curve of 0.732 to 1.000. Fourteen articles evaluated prognosis based on clinical characteristics at hospital admission, such as clinical, laboratory and radiological characteristics, reaching accuracy of 74.4 to 95.20%, sensitivity of 72.8 to 98.00%, specificity of 55 to 96.87% and AUC of 0.66 to 0.997 in predicting critical COVID-19. Nine articles used AI models to predict the epidemic of the COVID-19, such as epidemic peak, infection rate, number of infected cases, transmission laws, and development trend. Eight articles used AI to explore potential effective drugs, primarily through drug repurposing and drug development. Finally, 1 article predicted vaccine targets that have the potential to develop COVID-19 vaccines. Conclusions: In this review, we have shown that AI achieved high performance in diagnosis, prognosis evaluation, epidemic prediction and drug discovery for COVID-19. AI has the potential to enhance significantly existing medical and healthcare system efficiency during the COVID-19 pandemic.

Apropos of Universal Epitope Discovery for COVID-19 Vaccines: A Framework for Targeted Phage Display-Based Delivery and Integration of New Evaluation Tools.. [PMID: 34676381 PMCID: PMC8529634 DOI: 10.1101/2021.08.30.458222]

Targeted bacteriophage (phage) particles are potentially attractive yet inexpensive platforms for immunization. Herein, we describe targeted phage capsid display of an immunogenically relevant epitope of the SARS-CoV-2 Spike protein that is empirically conserved, likely due to the high mutational cost among all variants identified to date. This observation may herald an approach to developing vaccine candidates for broad-spectrum, towards universal, protection against multiple emergent variants of coronavirus that cause COVID-19.

Artificial Intelligence in Surveillance, Diagnosis, Drug Discovery and Vaccine Development against COVID-19

As of August 6th, 2021, the World Health Organization has notified 200.8 million laboratory-confirmed infections and 4.26 million deaths from COVID-19, making it the worst pandemic since the 1918 flu. The main challenges in mitigating COVID-19 are effective vaccination, treatment, and agile containment strategies. In this review, we focus on the potential of Artificial Intelligence (AI) in COVID-19 surveillance, diagnosis, outcome prediction, drug discovery and vaccine development. With the help of big data, AI tries to mimic the cognitive capabilities of a human brain, such as problem-solving and learning abilities. Machine Learning (ML), a subset of AI, holds special promise for solving problems based on experiences gained from the curated data. Advances in AI methods have created an unprecedented opportunity for building agile surveillance systems using the deluge of real-time data generated within a short span of time. During the COVID-19 pandemic, many reports have discussed the utility of AI approaches in prioritization, delivery, surveillance, and supply chain of drugs, vaccines, and non-pharmaceutical interventions. This review will discuss the clinical utility of AI-based models and will also discuss limitations and challenges faced by AI systems, such as model generalizability, explainability, and trust as pillars for real-life deployment in healthcare.

Artificial Intelligence in Surveillance, Diagnosis, Drug Discovery and Vaccine Development against COVID-19

As of August 6th, 2021, the World Health Organization has notified 200.8 million laboratory-confirmed infections and 4.26 million deaths from COVID-19, making it the worst pandemic since the 1918 flu. The main challenges in mitigating COVID-19 are effective vaccination, treatment, and agile containment strategies. In this review, we focus on the potential of Artificial Intelligence (AI) in COVID-19 surveillance, diagnosis, outcome prediction, drug discovery and vaccine development. With the help of big data, AI tries to mimic the cognitive capabilities of a human brain, such as problem-solving and learning abilities. Machine Learning (ML), a subset of AI, holds special promise for solving problems based on experiences gained from the curated data. Advances in AI methods have created an unprecedented opportunity for building agile surveillance systems using the deluge of real-time data generated within a short span of time. During the COVID-19 pandemic, many reports have discussed the utility of AI approaches in prioritization, delivery, surveillance, and supply chain of drugs, vaccines, and non-pharmaceutical interventions. This review will discuss the clinical utility of AI-based models and will also discuss limitations and challenges faced by AI systems, such as model generalizability, explainability, and trust as pillars for real-life deployment in healthcare.

Rates of SARS-CoV-2 transmission and vaccination impact the fate of vaccine-resistant strains

Vaccines are thought to be the best available solution for controlling the ongoing SARS-CoV-2 pandemic. However, the emergence of vaccine-resistant strains may come too rapidly for current vaccine developments to alleviate the health, economic and social consequences of the pandemic. To quantify and characterize the risk of such a scenario, we created a SIR-derived model with initial stochastic dynamics of the vaccine-resistant strain to study the probability of its emergence and establishment. Using parameters realistically resembling SARS-CoV-2 transmission, we model a wave-like pattern of the pandemic and consider the impact of the rate of vaccination and the strength of non-pharmaceutical intervention measures on the probability of emergence of a resistant strain. As expected, we found that a fast rate of vaccination decreases the probability of emergence of a resistant strain. Counterintuitively, when a relaxation of non-pharmaceutical interventions happened at a time when most individuals of the population have already been vaccinated the probability of emergence of a resistant strain was greatly increased. Consequently, we show that a period of transmission reduction close to the end of the vaccination campaign can substantially reduce the probability of resistant strain establishment. Our results suggest that policymakers and individuals should consider maintaining non-pharmaceutical interventions and transmission-reducing behaviours throughout the entire vaccination period.

Artificial intelligence-driven drug repurposing and structural biology for SARS-CoV-2

It has been said that COVID-19 is a generational challenge in many ways. But, at the same time, it becomes a catalyst for collective action, innovation, and discovery. Realizing the full potential of artificial intelligence (AI) for structure determination of unknown proteins and drug discovery are some of these innovations. Potential applications of AI include predicting the structure of the infectious proteins, identifying drugs that may be effective in targeting these proteins, and proposing new chemical compounds for further testing as potential drugs. AI and machine learning (ML) allow for rapid drug development including repurposing existing drugs. Algorithms were used to search for novel or approved antiviral drugs capable of inhibiting SARS-CoV-2. This paper presents a survey of AI and ML methods being used in various biochemistry of SARS-CoV-2, from structure to drug development, in the fight against the deadly COVID-19 pandemic. It is envisioned that this study will provide AI/ML researchers and the wider community an overview of the current status of AI applications particularly in structural biology, drug repurposing, and development, and motivate researchers in harnessing AI potentials in the fight against COVID-19.

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

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

Perspectives of Manipulative and High-Performance Nanosystems to Manage Consequences of Emerging New Severe Acute Respiratory Syndrome Coronavirus 2 Variants

The emergence of new SARS-CoV-2 variants made the COVID-19 infection pandemic and/or endemic more severe and life-threatening due to ease of transmission, rapid infection, high mortality, and capacity to neutralize the therapeutic ability of developed vaccines. These consequences raise questions on established COVID-19 infection management strategies based on nano-assisted approaches, including rapid diagnostics, therapeutics, and efficient trapping and virus eradication through stimuli-assisted masks and filters composed of nanosystems. Considering these concerns as motivation, this perspective article highlights the role and aspects of nano-enabled approaches to manage the consequences of the COVID-19 infection pandemic associated with newer SARS-CoV-2 variants of concern and significance generated due to mutations. The controlled high-performance of a nanosystem seems capable of effectively detecting new variables for rapid diagnostics, performing site-specific delivery of a therapeutic agent needed for effective treatment, and developing technologies to purify the air and sanitizing premises. The outcomes of this report project manipulative, multifunctional nanosystems for developing high-performance technologies needed to manage consequences of newer SARS-CoV-2 variants efficiently and effectively through an overall targeted, smart approach.

A Digital Twins Machine Learning Model for Forecasting Disease Progression in Stroke Patients

Background: Machine learning methods have been developed to predict the likelihood of a given event or classify patients into two or more diagnostic categories. Digital twin models, which forecast entire trajectories of patient health data, have potential applications in clinical trials and patient management. Methods: In this study, we apply a digital twin model based on a variational autoencoder to a population of patients who went on to experience an ischemic stroke. The digital twin’s ability to model patient clinical features was assessed with regard to its ability to forecast clinical measurement trajectories leading up to the onset of the acute medical event and beyond using International Classification of Diseases (ICD) codes for ischemic stroke and lab values as inputs. Results: The simulated patient trajectories were virtually indistinguishable from real patient data, with similar feature means, standard deviations, inter-feature correlations, and covariance structures on a withheld test set. A logistic regression adversary model was unable to distinguish between the real and simulated data area under the receiver operating characteristic (ROC) curve (AUCadversary = 0.51). Conclusion: Through accurate projection of patient trajectories, this model may help inform clinical decision making or provide virtual control arms for efficient clinical trials.

Bioinformatic HLA Studies in the Context of SARS-CoV-2 Pandemic and Review on Association of HLA Alleles with Preexisting Medical Conditions

After the announcement of a new coronavirus in China in December 2019, which was then called SARS-CoV-2, this virus changed to a global concern and it was then declared as a pandemic by WHO. Human leukocyte antigen (HLA) alleles, which are one of the most polymorphic genes, play a pivotal role in both resistance and vulnerability of the body against viruses and other infections as well as chronic diseases. The association between HLA alleles and preexisting medical conditions such as cardiovascular diseases and diabetes mellitus is reported in various studies. In this review, we focused on the bioinformatic HLA studies to summarize the HLA alleles which responded to SARS-CoV-2 peptides and have been used to design vaccines. We also reviewed HLA alleles that are associated with comorbidities and might be related to the high mortality rate among COVID-19 patients. Since both genes and patients' medical conditions play a key role in both severity of the disease and the mortality rate in COVID-19 patients, a better understanding of the connection between HLA alleles and SARS-CoV-2 can provide a wider perspective on the behavior of the virus. Such understanding can help scientists, especially in terms of protecting healthcare workers and designing effective vaccines.

The Role of Artificial Intelligence in Fighting the COVID-19 Pandemic

The first few months of 2020 have profoundly changed the way we live our lives and carry out our daily activities. Although the widespread use of futuristic robotaxis and self-driving commercial vehicles has not yet become a reality, the COVID-19 pandemic has dramatically accelerated the adoption of Artificial Intelligence (AI) in different fields. We have witnessed the equivalent of two years of digital transformation compressed into just a few months. Whether it is in tracing epidemiological peaks or in transacting contactless payments, the impact of these developments has been almost immediate, and a window has opened up on what is to come. Here we analyze and discuss how AI can support us in facing the ongoing pandemic. Despite the numerous and undeniable contributions of AI, clinical trials and human skills are still required. Even if different strategies have been developed in different states worldwide, the fight against the pandemic seems to have found everywhere a valuable ally in AI, a global and open-source tool capable of providing assistance in this health emergency. A careful AI application would enable us to operate within this complex scenario involving healthcare, society and research.

Artificial Intelligence-Aided Precision Medicine for COVID-19: Strategic Areas of Research and Development

Artificial intelligence (AI) technologies can play a key role in preventing, detecting, and monitoring epidemics. In this paper, we provide an overview of the recently published literature on the COVID-19 pandemic in four strategic areas: (1) triage, diagnosis, and risk prediction; (2) drug repurposing and development; (3) pharmacogenomics and vaccines; and (4) mining of the medical literature. We highlight how AI-powered health care can enable public health systems to efficiently handle future outbreaks and improve patient outcomes.

Role of artificial intelligence in peptide vaccine design against RNA viruses

RNA viruses have high rate of replication and mutation that help them adapt and change according to their environmental conditions. Many viral mutants are the cause of various severe and lethal diseases. Vaccines, on the other hand have the capacity to protect us from infectious diseases by eliciting antibody or cell-mediated immune responses that are pathogen-specific. While there are a few reviews pertaining to the use of artificial intelligence (AI) for SARS-COV-2 vaccine development, none focus on peptide vaccination for RNA viruses and the important role played by AI in it. Peptide vaccine which is slowly coming to be recognized as a safe and effective vaccination strategy has the capacity to overcome the mutant escape problem which is also being currently faced by SARS-COV-2 vaccines in circulation.Here we review the present scenario of peptide vaccines which are developed using mathematical and computational statistics methods to prevent the spread of disease caused by RNA viruses. We also focus on the importance and current stage of AI and mathematical evolutionary modeling using machine learning tools in the establishment of these new peptide vaccines for the control of viral disease.

How artificial intelligence may help the Covid-19 pandemic: Pitfalls and lessons for the future

The clinical severity, rapid transmission and human losses due to coronavirus disease 2019 (Covid‐19) have led the World Health Organization to declare it a pandemic. Traditional epidemiological tools are being significantly complemented by recent innovations especially using artificial intelligence (AI) and machine learning. AI‐based model systems could improve pattern recognition of disease spread in populations and predictions of outbreaks in different geographical locations. A variable and a minimal amount of data are available for the signs and symptoms of Covid‐19, allowing a composite of maximum likelihood algorithms to be employed to enhance the accuracy of disease diagnosis and to identify potential drugs. AI‐based forecasting and predictions are expected to complement traditional approaches by helping public health officials to select better response and preparedness measures against Covid‐19 cases. AI‐based approaches have helped address the key issues but a significant impact on the global healthcare industry is yet to be achieved. The capability of AI to address the challenges may make it a key player in the operation of healthcare systems in future. Here, we present an overview of the prospective applications of the AI model systems in healthcare settings during the ongoing Covid‐19 pandemic.

Artificial Intelligence for COVID-19 Drug Discovery and Vaccine Development

SARS-COV-2 has roused the scientific community with a call to action to combat the growing pandemic. At the time of this writing, there are as yet no novel antiviral agents or approved vaccines available for deployment as a frontline defense. Understanding the pathobiology of COVID-19 could aid scientists in their discovery of potent antivirals by elucidating unexplored viral pathways. One method for accomplishing this is the leveraging of computational methods to discover new candidate drugs and vaccines in silico. In the last decade, machine learning-based models, trained on specific biomolecules, have offered inexpensive and rapid implementation methods for the discovery of effective viral therapies. Given a target biomolecule, these models are capable of predicting inhibitor candidates in a structural-based manner. If enough data are presented to a model, it can aid the search for a drug or vaccine candidate by identifying patterns within the data. In this review, we focus on the recent advances of COVID-19 drug and vaccine development using artificial intelligence and the potential of intelligent training for the discovery of COVID-19 therapeutics. To facilitate applications of deep learning for SARS-COV-2, we highlight multiple molecular targets of COVID-19, inhibition of which may increase patient survival. Moreover, we present CoronaDB-AI, a dataset of compounds, peptides, and epitopes discovered either in silico or in vitro that can be potentially used for training models in order to extract COVID-19 treatment. The information and datasets provided in this review can be used to train deep learning-based models and accelerate the discovery of effective viral therapies.