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. 2026 Sep 23;37(4):103111. doi: 10.1016/j.omtn.2026.103111

AptViralDB: A repository of experimentally validated antiviral aptamers

Nisha Bajiya 1, Shivansh Singh 2, Pushpendra Singh Gahlot 1, Gajendra PS Raghava 1,∗
PMCID: PMC13670952  PMID: 42889724

Abstract

In an era of increasing drug resistance, exploring alternative molecules is crucial for the efficient management and treatment of viral diseases. Nucleic acid aptamers have emerged as highly promising candidates due to their exceptional target specificity, low immunogenicity, and versatile mechanisms for viral blocking. This manuscript describes a manually curated database providing comprehensive information on experimentally validated antiviral aptamers. It contains 1,768 entries of antiviral aptamers, with detailed annotations for 40 viral species and 104 molecular targets, compiled from the literature and existing databases. We also provide predicted secondary structures and their corresponding minimum free energy (MFE) values. Additionally, a knowledge graph created using ArcadeDB/OpenCypher enables users to seamlessly explore connections among aptamers, viruses, molecular targets, and biological activities. Finally, the platform offers advanced search and browsing tools, BLAST-based sequence similarity searches, GC-content analysis, downloadable datasets, and REST API access to support computational applications.

Keywords: MT: Special Issue: Innovations in Aptamer Technology, antiviral aptamers, viral diagnostics, antiviral therapeutics, viral targets, aptamer database

Graphical abstract

graphic file with name ga1.webp

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Raghava and colleagues present AptViralDB, a manually curated repository of 1,768 experimentally verified antiviral aptamers covering 40 viral species. By integrating sequence, functional, and structural data with search, browsing, and knowledge-graph tools, the database facilitates the identification and comparison of nucleic acid aptamers for antiviral treatments, diagnostics, and biosensors.

Introduction

Viruses remain among the most formidable infectious agents because of their rapid evolution, high mutation rates, and remarkable ability to evade host immune responses. Recurrent outbreaks caused by pathogens such as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), human immunodeficiency virus (HIV), influenza A virus, Zika virus, Ebola virus, and more recently emerging zoonotic viruses continue to pose major threats to global public health and socioeconomic stability.1,2,3 Although antiviral drugs and vaccines have substantially reduced disease burden, their long-term effectiveness is frequently compromised by viral genetic variability, antigenic drift and shift, drug resistance, pharmacokinetic limitations, adverse drug reactions, and drug-drug interactions.4,5 In addition, many antiviral drugs are associated with adverse effects, including dermatological reactions, central nervous system disturbances, flu-like symptoms, blood cell-based irregularities, and organ toxicity, and may exhibit reduced efficacy due to drug-drug interactions.6,7,8 These drawbacks emphasize the necessity for rapidly adjustable recognition systems capable of targeting similar viral elements with preserved selectivity.

Among emerging antiviral strategies, nucleic acid aptamers have emerged as promising candidates for antiviral therapeutics and diagnostics. Since the introduction of the systematic evolution of ligands by exponential enrichment (SELEX) technology in 1990,9,10,11 a variety of aptamers targeted to purified viral proteins, intact virions, virus-infected cells, and even host proteins of the virus were developed.12 Depending on the intended application, specialized SELEX strategies, including Protein-SELEX, have enabled the isolation of aptamers recognizing viral envelope glycoproteins, spike proteins, reverse transcriptases, RNA-dependent RNA polymerases, proteases, and nucleocapsid proteins.13 Whole-virus SELEX preserves native conformational epitopes, while Cell-SELEX enables recognition of virus-infected cells. The integration of next-generation sequencing technologies into SELEX workflows has enabled rapid aptamer discovery, as they are able to analyze the functionality of sequenced aptamers and identify the active sites of the aptamers with antiviral properties using enriched aptamer libraries.14

Unlike conventional antiviral molecules, aptamers can interfere at different levels in the viral life cycle. They can block viral attachment by hindering receptor recognition (e.g., blocking of the CCR5 required for HIV-1 entry,15 blocking the hemagglutinin receptor-binding site via DNA aptamer of influenza viruses,16,17 and comparative characterization of various DNA aptamers targeting spike proteins across numerous SARS-CoV-2 variants18); inhibit membrane fusion with the cell and viral entry (e.g., a DNA aptamer that blocks the post-attachment fusion of the spike protein of SARS-CoV-219 and a fusion aptamer that targets the S2 subunit, which will inhibit spike-ACE2-mediated entry20); suppress genome replication by targeting viral polymerases or reverse transcriptases (such as an RNA aptamers that inhibit the RNA-dependent RNA polymerase activity of SARS-CoV-221); interrupt viral protein maturation such as RNA aptamers against HIV-1 protease, which is responsible for cleavage of Gag/Gag-Pol polyprotein during viral maturation22; recognize the virus-infected host cells (including aptamers against GNNV (grouper nervous necrosis virus)-infected grouper brain [GB] cells to identify infected-cell-surface biomarker for targeted delivery23 and DNA aptamers against vaccinia virus-infected and -uninfected lung cancer A549 cells24); and serve as molecular probes for highly sensitive diagnostics and biosensing platforms.25,26,27,28 Their ease of chemical synthesis, low immunogenicity, high reproducibility, and specific chemical modification further expand their utility for targeted drug delivery and therapeutic development.29,30 The various mechanisms through which antiviral aptamers interfere with viral infection and replication are outlined in Figure 1. This opens a vast array of possibilities for aptamers and their use in developing next-generation anti-infective agents.

Figure 1.

Figure 1

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Diagrammatic representation of the antiviral mechanism by antiviral aptamers

Antiviral aptamers display excellent target specificity due to the folding of aptamers into intricate canonical structures (e.g., stem-loops, hairpins, and bulges) and non-canonical structures (e.g., pseudoknots and G-quadruplexes) that result in significant conformational changes and unique conformational rearrangements complementary to their targets or the viral structure upon binding.31 Structural elucidation using X-ray crystallography (such as determining the crystal structure of a DNA aptamer bound to the SARS-CoV-2 nucleocapsid N-terminal domain32), NMR spectroscopy (including the NMR evidence shows that an anti-HIV-1 reverse-transcriptase DNA aptamer induces a conformational change in the enzyme33 and the DNA aptamer bound to SARS-CoV-2 nucleocapsid N-terminal domain for NMR-derived complex structure34), and computational modeling has revealed that aptamer-target interactions are governed by molecular shape complementarity along with various noncovalent interactions such as hydrogen bonding, van der Waals forces, hydrophobic effects, and electrostatic forces.35 Following selection, techniques such as surface plasmon resonance (SPR), biolayer interferometry (BLI), microscale thermophoresis (MST), fluorescence-based assays, and enzyme-linked binding assays are commonly performed to quantify binding affinity and estimate dissociation constants (KD) that are in a pico- to nanomolar range comparable to those of antibodies,36 inhibitory concentration values (IC50), and effective concentration values (EC50).37,38 Then, the antiviral efficacy of these identified aptamers is usually evaluated using viral neutralization assays, plaque reduction assays, antiviral cell-based assays, and experiments on inhibition of viral entry.

With increased emphasis on antiviral aptamer research, a variety of computational methods such as the prediction of aptamer secondary and tertiary structure, molecular docking, molecular dynamics simulation, and artificial intelligence (AI)-based approaches have been introduced for more efficient discovery and optimization of aptamers, which predict aptamer folding, aptamer-target interaction, and the conserved motif of the aptamers before experimental validation.38,39,40,41,42 However, the success of these methods relies heavily on high-quality, well-curated, experimentally validated antiviral aptamers.

The necessity of well-annotated datasets has already been demonstrated in the field of antiviral peptides, where dedicated databases and prediction tools43,44,45,46,47,48,49,50 have accelerated sequence analysis, activity prediction, and the design of new antiviral peptides. However, computational tools dedicated to antiviral aptamers are still limited. Although several general-purpose aptamer databases have been developed,51,52,53,54,55 they are not exclusively focused on antiviral applications and generally lack comprehensive viral classification, standardized binding data, and detailed functional annotations. Therefore, comprehensive comparisons, data-driven discovery, and the development of robust AI-based predictive algorithms for antiviral aptamers remain challenging.

To date, only four clinical trials involving antiviral or virus-associated aptamers have been registered on ClinicalTrials.gov, reflecting the clinical translational gap. These include two trials for COVID-19, one for the treatment evaluation using ApTOLL (ClinicalTrials.gov: NCT05293236), which is a DNA aptamer targeting TLR4 and another is a saliva-based DNA aptamer diagnostic platform for its detection (ClinicalTrials.gov: NCT04974203); third, a tenofovir aptamer-based biosensor used to monitor adherence to HIV drugs (ClinicalTrials.gov: NCT04870671); and fourth, BC 007 (ClinicalTrials.gov: NCT05911009), a 15-mer single-stranded DNA (ssDNA) aptamer drug that is being evaluated as a therapy for long COVID. This limited progression can be attributed to challenges such as nuclease instability, delivery barriers for intracellular viral targets, pharmacokinetic optimization, and evolving regulatory pathways for nucleic acid therapeutics. Moreover, relevant antiviral aptamer data are dispersed across heterogeneous studies, with inconsistent data reporting, which hampers systematic comparison across viral species and the development of robust computational prediction tools, underscoring the need for a centralized and structured database.

To bridge this gap, we developed AptViralDB, a comprehensive repository of experimentally validated antiviral aptamers that provides a structured, searchable platform for the global scientific community. The platform provides advanced search and browsing, downloadable datasets, knowledge graph (KG)-based relationship exploration, REST API access, and integrated analysis tools, thereby establishing a computational resource that supports large-scale bioinformatics analyses, machine learning model development, and the rational discovery of next-generation antiviral aptamers.

Results

Data analysis

AptViralDB currently contains 1,768 experimentally validated antiviral aptamers targeting 40 different viral species, with 121 entries in the UTexas databases, 106 in AptaDB, and 26 in Aptabase. The exponential growth in cumulative entries from 1992 to 2025 is depicted in Figure 2. After 2011, a significant increase in the number of published studies can be observed. Various oligonucleotide types have been used to construct antiviral aptamers (Table 1) and can be categorized into ssDNA, RNA, and xeno nucleic acid (XNA)-based aptamers. The majority (69.1%) are DNA aptamers, while a limited proportion (2.0%) are XNA-based aptamers (i.e., FANA (2′-Fluoro-arabino nucleic acid), HNA (Hexitol nucleic acid), and TNA (Threose nucleic acid) aptamers).

Figure 2.

Figure 2

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Line plot showing the cumulative year-wise growth trend of aptamers in antiviral studies

Table 1.

The distribution of aptamers based on the type of nucleic acids in AptViralDB

Aptamer type Count Percentage (%)
ssDNA 1,221 69.1
ssRNA 510 28.86
ssXNA 36 2.04
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Figure 3A demonstrates that analyzing the virus species reveals that coronaviruses, HIV, influenza, and hepatitis viruses account for the majority of entries. Similarly, in Figure 3B, most aptamers in the database target whole viral particles with nonspecific target information (12.21%) and surface glycoproteins such as hemagglutinin, spike proteins, and receptor-binding domains, as well as intracellular enzymes such as reverse transcriptase.

Figure 3.

Figure 3

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Most frequenctly reported viruses and molecular targets of antiviral aptamers in AptViralDB

Lengthwise distribution indicates that 80% of antiviral aptamers fall within the 41–100 nt range, particularly for therapeutic and biosensor applications (see Figure 4A). Meanwhile, regarding affinity validation methods, as depicted in Figure 4B, SPR is the most frequently used method, with a total of 236 entries reported in the database.

Figure 4.

Figure 4

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Distribution of aptamer activity roles by sequence length and the most commonly used affinity determination methods

Thermodynamic stability and secondary structure analysis of antiviral aptamers

As depicted in Figure 5A, the strong negative correlation coefficient between sequence length and minimum free energy (MFE; R = −0.579) indicates that longer aptamer sequences exhibit more thermodynamically stable secondary structures (i.e., more negative MFE values). The unimodal distribution of MFE with an average value of −10.44 kcal/mol (Figure 5B) indicates that most aptamers have moderate secondary-structure stability, while a few aptamers possess highly negative MFE values, which signifies stronger folding stability. Additionally, it has been shown that ssRNA aptamers generally have more negative MFE values than ssDNA aptamers. Furthermore, the evaluation of dot-bracket notation showed a considerably large percentage of paired bases.

Figure 5.

Figure 5

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Thermodynamic stability and secondary structure characteristics of antiviral aptamers

Discussion

AptViralDB, altogether, represents the broader reasons that will shape aptamer research in viral therapies in the near future. After the expiration of the most significant patent (US5567588A) in aptamer research on the SELEX methodology, the number of published studies has steadily increased since 2011. Also, triggered by major global outbreaks such as the 2009 influenza pandemic and the 2021 COVID-19 pandemic, there has been increased scientific attention to the antiviral applications of aptamers. This stimulated the rapid development of novel molecular recognition systems as a potential basis for diagnostics or therapeutics.1 This growth was accompanied by a clear predominance of DNA aptamers in the database, which is likely explained by their higher stability compared to RNA aptamers due to enhanced resistance to hydrolysis and easier handling and use.29 Meanwhile, the smaller proportion of XNA-based aptamers reflects their more specialized role, as these are a group of chemically modified nucleic acid analogs that were developed with the aim of increasing resistance to nucleases and improving stability for antiviral applications while maintaining high affinity binding to their targets.56

The choice of nucleic acid chemistry was, in turn, influenced by which viruses and viral components are targeted by researchers. The predominance of coronaviruses, HIV, influenza, and hepatitis viruses among the targeted organisms aligns with global disease burden and historical antiviral research priorities.4,5 Similarly, the frequent targeting of whole viral cells and of surface glycoproteins such as hemagglutinin, spike proteins, and receptor-binding domains, together with intracellular enzymes such as reverse transcriptase, reflects the established strategy of blocking viral attachment, entry, and replication machinery.25,28 The development and validation choices align with a similar reasoning tailored toward a functional, testable aptamer. The concentration of aptamers in the 41–100 nt range is commonly reported as optimal for maintaining structural complexity and high-affinity binding while preserving synthesis efficiency.35,36 Meanwhile, the predominance of SPR among affinity determination methods is consistent with its recognition as a gold-standard, real-time, label-free technique for kinetic characterization of aptamer-target interactions.37

Finally, the structural insights from the curated dataset help explain the specific targeting ability of aptamers. To determine aptamer secondary structure, well-characterized experimental methods, including X-ray crystallography, cryoelectron microscopy (cryo-EM) (e.g., the cryo-EM structure of a bivalently bound to the SARS-CoV-2 spike receptor-binding domain),57 NMR, small-angle X-ray scattering (SAXS) (e.g., SAXS-determined three-dimensional [3D] shape of LC-18t),58 and CD (circular dichroism) spectroscopy (e.g., analyzing anthracycline aptamer DRN-10),59 are prominent, which provide structural and dynamic details but need advanced instrumentation and laborious protocols,38 whereas secondary-structure prediction using computational tools is a quick and scalable method for determining base-pairing patterns and folding stability, thereby guiding subsequent experimental validation.60

From the analysis, the negative correlation observed between sequence length and MFE is consistent with longer nucleotide sequences offering greater potential for base pairing and complex folding in both RNA and DNA molecules.61 Similarly, the tendency of ssRNA aptamers to show lower MFE values than ssDNA aptamers is consistent with their greater structural variability and intramolecular interactions, as recently supported by experimental studies on the comparative energetic stabilities of nucleic acid folding.62 The high proportion of paired bases identified from dot-bracket notation further implies the presence of relatively compact secondary structures, with stem-loops as the most predominant motifs. Overall, these observations suggest that the aptamers in the dataset possess stable, structured configurations that could facilitate efficient, high-affinity binding to their viral targets. It will strengthen the potential of AptViralDB as a structured resource for identifying design principles that generalize across the antiviral aptamer field.

Materials and methods

Data collection

To curate antiviral aptamer entries, we conducted PubMed and Google searches using keywords such as “antiviral aptamer,” “viral aptamer,” “therapeutic aptamer,” “diagnostic aptamer,” etc. Also, we have retrieved data from existing databases, including the UTexas Aptamer Database,53 AptaBase (https://www.iitg.ac.in/proj/aptabase/about.html), and AptaDB.54 Each retrieved article and review was manually screened to ensure relevance. After removing duplicates and unnecessary articles and keeping experimentally validated antiviral aptamers, the search returned articles from 1992 to 2025, totaling 364 PMIDs and 17 DOIs. When multiple variants of an aptamer were reported, each variant was reported as a separate entry. The selected publications were then thoroughly reviewed for the collection of antiviral aptamer data utilized as antivirals, diagnostics, and targeted delivery. The complete repository framework is shown in Figure 6.

Figure 6.

Figure 6

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The architecture of AptViralDB

Data annotation and standardization

For each entry, detailed information about the aptamer was manually extracted, including its sequence, length, type, chemical modifications, target name, target virus, experimental SELEX method, library used, and number of rounds performed to obtain the desired aptamer, affinity determination method, and binding affinity (KD). Additional information, such as the study’s objective, the inhibition outcome (IC50 or EC50), diagnostic applications, and the activity role (therapeutic, detection, etc.), was recorded. In addition, cytotoxicity, stability, half-life, and patent information were curated when available. All reported sequences are in uppercase, and modified nucleotides are consistently annotated along with their type. The repository comprises 40 different virus classes and 104 reported aptamer targets. The entries have been linked to their corresponding aptamer cards, which contain detailed, curated information. Also, optimal secondary structures of the aptamers were predicted using the ViennaRNA Package 2.063 with MFE in kcal/mol, and their representation in standard dot notation and structural elements, including stems, loops, hairpins, etc., was reported.

Database architecture and implementation

AptViralDB was developed using a Relational Database Management System (RDBMS) architecture implemented in MySQL (v.5.5.62) and Apache HTTP Server (v.2.4.7). The back end was built with PHP, while the front end was designed with TypeScript, HTML5, React, and Tailwind CSS to ensure compatibility across modern web browsers. The database framework was organized into interconnected tables storing aptamer details, virus type, target information, experimental methods, and publication metadata for querying, browsing, and analyzing data. Users can access it for free at https://webs.iiitd.edu.in/raghava/aptviraldb.

Web interface and functional modules

The web interface was designed to provide automatic access to data. Users can perform a simple search using virus name, target name, or aptamer ID. An advanced search module allows filtering based on multiple parameters, including aptamer type, target virus, aptamer targets, and affinity range. The database also provides browsing options categorized by “target organism-wise,” “target-wise,” “length-wise,” “modification-wise,” “affinity-wise,” “activity role,” “affinity determination method,” and “publication year.” Additionally, similarity search using nucleotide BLAST and the GC calculator tool has been incorporated into the analysis. To obtain more information about the aptamers incorporated from other repositories, such as AptaDB and Aptabase, users can access our “cross-referencing” module. For programmatically downloading data, the REST API module is also available. Additionally, the user can download the sequence data in FASTA format and the predicted secondary structures in SVG format to facilitate further analysis.

KG construction and integration

To enable a structured representation of complex relationships among antiviral aptamers and their related data, we adopted a KG based on the ArcadeDB multi-model graph database (https://arcadedb.com/knowledge-graphs.html) within AptViralDB. During data preprocessing, curated records were presented as tables, mapped to structured subject-predicate-object triples (head-relation-tail), and stored as CSV files. Three core triple sets were constructed: (1) Aptamer-TARGET-Viral Protein (capturing aptamers and direct interactions with the viral protein targets), (2) Aptamer-TARGETS_ORGANISM-Virus (connecting aptamers to the viral organisms being targeted), and (3) Aptamer-HAS_ACTIVITY_ROLE-Functional Role (capturing the purpose of the aptamer). The CSVs were then loaded into the ArcadeDB instance via OpenCypher LOAD CSV queries using MERGE operations so as to avoid node deduplication across files, defining 4 vertex types (aptamer, target, organism, ActivityRole) connected via 3 directed edge types. This architecture enables multi-level traversal between relationships, thereby enabling exploration of unexplored findings such as viral families sharing the same target, evaluating the effects of modifications in specific roles, and assessing the repurposing potential of existing aptamers in other viruses. The ArcadeDB platform has enabled the construction of an interoperable data structure that supports advanced analytical workflows via its Studio interface and API, as well as a flexible architecture for integrating further data and analyses, including predictive modeling and machine learning.

Conclusions

In brief, AptViralDB is a comprehensive, manually curated database of experimentally validated antiviral aptamers. By aggregating scattered literature into a structured, searchable platform, this resource addresses a current gap in antiviral research. It provides a valuable resource for researchers engaged in antiviral drug discovery, diagnostic development, and computational modeling. The curated dataset can be used for structure-function analysis, motif identification, binding affinity prediction, and the development of machine-learning-based methods to identify new antiviral aptamers. Furthermore, the repository can support rapid response efforts during emerging viral outbreaks by enabling aptamer repurposing and comparative analysis.

Acknowledgments

We would like to acknowledge the Department of Biotechnology (DBT), government of India, for supporting this work under grant no. BT/PR40158/BTIS/137/24/2021. The authors are thankful for the necessary facilities and infrastructure, as well as fellowships and financial support, provided by the Council of Scientific and Industrial Research (CSIR), the University Grants Commission (UGC), the Department of Biotechnology (DBT), and the Department of Computational Biology at IIITD, New Delhi. Lastly, we also like to thank Draw.io for making the Figures. AptViralDB is freely available at https://webs.iiitd.edu.in/raghava/aptviraldb/ and is compatible with desktops, tablets, and smartphones. The database sequences in FASTA format and their predicted secondary structure can be downloaded at https://webs.iiitd.edu.in/raghava/aptviraldb/downloads. The paper’s bioRxiv DOI is https://doi.org/10.64898/2026.07.08.737144.

Author contributions

N.B. collected, curated, and analyzed the data. S.S. developed the web server’s front end and back end. P.S.G. helped S.S. develop the KG. The manuscript was prepared by N.B., S.S., and G.P.S.R. The project’s idea was conceived and coordinated by G.P.S.R. The final version of the paper was reviewed and approved by all authors.

Declaration of interests

No conflicts of interest were declared by the authors.

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