The aging process involves multiple complex molecular pathways, among which ROS, mTOR and AMPK signaling pathways play a key role in regulating cellular functions and the aging process. Reactive oxygen species (ROS) are important mediators in aging and their abnormal changes directly affect cell fate. When ROS are overproduced, they can cause oxidative stress, leading to damage to DNA, proteins and lipids, thereby accelerating cellular aging and tissue functional degradation⁴⁰. However, ROS also has a dual role; moderate levels of ROS can act as signaling molecules, activating cellular protective mechanisms, such as stimulating the expression of antioxidant genes. This complex mechanism makes the precise regulation of ROS an important focus in anti-aging research.

Another important pathway is mTOR (mammalian target of rapamycin), which, as a key kinase regulating cell growth, protein synthesis and energy metabolism, plays a significant role in the aging process. Studies have shown that excessive activation of mTOR is closely related to aging and various related diseases. Drugs that inhibit mTOR (such as Rapamycin) have shown anti-aging effects in various model organisms, becoming an important direction for anti-aging intervention. At the same time, AMPK (AMP-activated protein kinase), as a sensor of cellular energy status, also plays a central role in regulating the aging process. The activation of AMPK can not only inhibit the mTOR signaling pathway but also enhance autophagy and improve mitochondrial function, thereby delaying cellular and tissue aging⁴².

In addition to these signaling pathways, epigenetic modifications also play a crucial role in the aging process. The temporal changes in DNA methylation are considered a “biological clock” that can reflect an individual’s biological age and provide a basis for assessing the degree of aging. In addition, histone deacetylases (such as SIRT1) play an important role in regulating gene expression and maintaining genomic stability³⁵. The dysfunction of SIRT1 is considered one of the key drivers of aging. In summary, the regulation of signaling pathways and epigenetics is involved in the occurrence and development of aging, providing a rich set of molecular targets for studying aging mechanisms and developing anti-aging intervention measures.
3.2  Screening of Key Targets

Intervention measures against aging depend on the precise screening and validation of molecular targets, which is key to developing anti-aging drugs and intervention strategies. Current research indicates that multiple molecular targets play a core role in delaying aging and related pathological changes, among which SIRT1, NRF2 and FOXO are particularly important. SIRT1 is an NAD⁺-dependent deacetylase that, by regulating key proteins such as FOXO and p53, plays an important role in improving cellular stress tolerance, enhancing DNA repair and maintaining energy metabolism balance. SIRT1 activates the FOXO transcription factor, promoting the expression of antioxidant genes while inhibiting pro-apoptotic signals, thus protecting cells from oxidative stress and damage³¹.

In addition, SIRT1 shows potential for delaying aging in regulating mitochondrial function and lipid metabolism and its agonists have been verified in various model organisms to extend lifespan. NRF2 (nuclear factor erythroid 2-related factor 2) is the main regulator of cellular antioxidant defense, activating the expression of antioxidant enzyme genes to reduce the damage of reactive oxygen species (ROS) to cells. Under normal conditions, NRF2 is regulated by the inhibitory protein Keap1, but under oxidative stress, NRF2 is released and transferred to the nucleus, initiating the expression of antioxidant enzymes such as HO-1 and NQO1, thereby enhancing cellular antioxidant capacity and reducing inflammation and mitochondrial dysfunction⁴³.

FOXO (forkhead transcription factor), as a downstream effector molecule of multiple signaling pathways (such as the insulin/IGF-1 pathway), plays an important role in delaying aging and maintaining cellular homeostasis by regulating antioxidant responses, autophagy, DNA repair and cell cycle processes. FOXO can induce the expression of antioxidant enzymes and DNA repair enzymes, reduce the accumulation of ROS and improve cellular function by promoting autophagy to clear damaged organelles¹⁸.
4.   Application of Virtual Screening Technology in Aging Research
Virtual screening (VS) has become a transformative tool in aging research, addressing the complexities of drug discovery for age-associated diseases and interventions aimed at slowing the aging process. Leveraging computational methods, VS facilitates the rapid identification of bioactive compounds targeting key pathways, such as mTOR, SIRT1 and AMPK. Below, the diverse applications of VS in aging research are explored in depth.
4.1  Discovery of Small Molecule Anti-Aging Compounds
Virtual screening has proven highly effective in identifying small molecules with anti-aging properties. For example, computational efforts have pinpointed SIRT1 activators, such as resveratrol analogs, that modulate the activity of this key deacetylase involved in stress resistance and metabolic regulation. Studies like those by Sun et al. (2016) utilized ligand-based virtual screening to identify SIRT1 inhibitors within natural product databases, providing promising leads for pharmaceutical development⁴⁶. Similarly, BACE1 inhibitors have been identified for combating Alzheimer’s disease, a neurodegenerative condition closely linked to aging. Gheidari et al. (2024) demonstrated how structure-based virtual screening combined with molecular docking and ADMET predictions could uncover potential inhibitors with optimized pharmacokinetic profiles⁴⁹
4.2  Targeting Specific Pathways in Aging
The application of VS in aging research often focuses on specific molecular pathways. For instance, the mTOR signaling pathway, a regulator of cellular growth and metabolism, is a well-established target for anti-aging interventions. Rapamycin, an mTOR inhibitor, has been widely studied for its lifespan- extending effects⁵⁰. Virtual screening has played a pivotal role in designing rapamycin derivatives, such as everolimus, which offer enhanced pharmacokinetics and reduced side effects. Similarly, AMPK activators identified through VS hold promise for promoting autophagy and mitigating cellular aging. Zhang et al. (2016) highlighted the use of VS in screening AICAR analogs, which demonstrated significant effects on cellular energy homeostasis and lifespan extension in model organisms⁵¹
4.3  Database Resources Supporting VS in Aging Research

The efficiency and success of VS in aging research are underpinned by comprehensive databases that provide rich repositories of chemical compounds and target structures: Compound Libraries include: ZINC: A freely available database hosting millions of drug-like molecules, enabling high-throughput screening for potential therapeutic candidates⁵².PubChem: Maintained by NCBI, this extensive database includes tens of millions of chemical entities, providing a wealth of information for ligand-based virtual screening⁵³. Protein Structure Databases include: PDB: Offers experimentally resolved protein structures that are instrumental for structure-based docking studies. AlphaFold: By predicting protein structures with high accuracy using deep learning, AlphaFold expands the scope of VS, particularly for previously uncharacterized targets involved in aging²⁴.

4.4  Multi-Scale Modeling Integration in Aging Research

Virtual screening has transcended its initial role as a molecular filtering tool, integrating with multi-scale modeling to connect molecular findings to systemic biological effects. Molecular dynamics simulations validate the stability of compound-target interactions identified through VS, elucidating their potential mechanisms of action within cellular environments. Such approaches have proven invaluable in confirming the efficacy of mTOR³.Systems biology models incorporate VS data into broader frameworks, enabling predictions of how compounds modulate entire biological networks. For example, integrating mTOR inhibitors into metabolic models has provided insights into their effects on energy balance and organismal aging.

4.5. Applications in Multi-Target Drug Discovery

Aging is characterized by interconnected molecular pathways, necessitating multi-target approaches in drug design. Virtual screening has been instrumental in identifying compounds that act on multiple targets simultaneously¹¹. For example, dual-action molecules targeting both mTOR inhibition and NRF2 activation have shown promise in preclinical aging studies. These compounds leverage the interplay between metabolic regulation and antioxidant defenses to address aging more comprehensively.

4.6. Integration of VS with Emerging Technologies

The synergy between VS and emerging technologies like deep learning and multi-omics is transforming aging research. Machine learning algorithms enhance the predictive accuracy of VS, enabling the identification of novel bioactive compounds with greater efficiency. Additionally, multi-omics data integration provides a more holistic view of aging processes, guiding the selection of drug targets and informing compound optimization⁴.

Through these diverse applications, virtual screening continues to revolutionize aging research by accelerating drug discovery and expanding our understanding of the molecular mechanisms underpinning aging. As the technology evolves, its role in developing effective anti-aging therapies will undoubtedly grow, offering new hope for addressing the challenges posed by an aging global population.
5. Challenges and Future Directions in Virtual Screening for Aging Research
5.1  Complexity and Diversity of the Aging Process

Aging is an intricate biological phenomenon resulting from the interplay of multiple molecular pathways, diverse cell types and complex biological networks. The heterogeneity of aging processes presents significant challenges for drug discovery and target identification. For instance, the rate and characteristics of aging differ markedly across tissues organs and individuals, introducing variability that complicates the design of universal therapeutic strategies. While certain tissues, such as the brain and cardiovascular system, exhibit pronounced vulnerability to age-related decline, others may demonstrate more resilience. These disparities arise from differences in metabolic activity, regenerative capacity and exposure to environmental stressors, among other factors.

Adding to the complexity, key signaling pathways implicated in aging, such as reactive oxygen species (ROS), AMPK and mTOR, may exhibit context-dependent or even contradictory functions. ROS, for example, can act as damaging agents at high levels, promoting oxidative stress and cellular damage, but they also serve as signaling molecules at physiological levels, triggering protective responses. Similarly, mTOR, which drives growth and protein synthesis, can be detrimental when hyperactivated in aging contexts but essential for tissue repair and immune responses in others. These dual and sometimes conflicting roles pose substantial challenges for precise targeting, requiring nuanced therapeutic approaches that balance activation and inhibition depending on the biological context.

Furthermore, the interdependence of aging-related pathways complicates drug design. Interventions targeting a single pathway may inadvertently affect others, potentially leading to unintended side effects or diminishing therapeutic efficacy. For example, inhibiting mTOR might promote autophagy and longevity but could simultaneously impair anabolic processes essential for tissue maintenance. This intricate network of interactions necessitates the development of multitarget strategies or pathway-specific modulation to achieve effective and context-appropriate outcomes.

5.2  Incompleteness and Bias in Biological Data

A significant obstacle in virtual screening for aging research is the incompleteness and bias inherent in existing biological data. High-quality, comprehensive datasets are essential for the accuracy and reliability of virtual screening models, yet substantial gaps remain in available information. For example, the coverage of three-dimensional protein structures in databases such as the Protein Data Bank (PDB) is far from exhaustive. Many aging-relevant proteins, particularly those with transient or intrinsically disordered regions, remain unresolved or poorly characterized, limiting the applicability of structure-based virtual screening (SBVS).

Moreover, the datasets used for ligand-based virtual screening (LBVS) are often derived from experimentally validated active compounds, which may not capture the full chemical or biological diversity of potential ligands. This reliance on historical data introduces biases that can skew screening results toward well-studied targets while neglecting less-explored but potentially critical pathways. Additionally, data quality issues, such as inconsistencies in experimental conditions, sample heterogeneity and reporting standards, further exacerbate inaccuracies and reduce reproducibility. For instance, compounds that show promising in silico results may fail during in vitro or in vivo validation due to discrepancies in binding affinity, bioavailability or toxicity.

Efforts to address these limitations include the expansion of databases to incorporate more comprehensive protein structures and diverse chemical libraries. Advances in experimental techniques, such as cryo-electron microscopy and AlphaFold, are beginning to close the structural gap by resolving previously inaccessible protein conformations. Concurrently, integrating multi-omics datasets, including proteomics, transcriptomics and metabolomics, can provide richer and more nuanced biological context for target selection and validation.

5.3 Model Interpretability and Practical Verification Difficulties

While virtual screening offers substantial efficiency advantages, the interpretability of its predictive models remains a critical limitation. The algorithms underlying virtual screening, including molecular docking and scoring functions, often fail to fully capture the complexities of molecular interactions. For example, current scoring functions primarily focus on estimating binding energy through simplified representations of van der Waals forces, hydrogen bonding and electrostatic interactions. These approximations, while computationally efficient, may overlook critical factors such as conformational dynamics, water-mediated interactions and allosteric effects, leading to false positives or false negatives in screening results.

Moreover, the transition from virtual predictions to experimental validation is fraught with challenges. Candidate compounds identified through virtual screening often encounter obstacles such as poor bioavailability, off-target effects and unexpected toxicity during in vitro or in vivo testing. These practical issues highlight the need for more robust and interpretable models that can better predict pharmacokinetic and pharmacodynamic properties, as well as potential side effects²⁹.

To address these challenges, the integration of molecular dynamics simulations and advanced machine learning algorithms is being explored. Molecular dynamics can provide a more detailed understanding of ligand-protein interactions by simulating their behavior over time, offering insights into binding stability and conformational changes. Meanwhile, machine learning models trained on large datasets of experimental results can improve the predictive accuracy of scoring functions, particularly when applied to novel or poorly characterized targets.

The integration of deep learning into virtual screening represents a transformative advancement, enhancing both accuracy and efficiency. Deep learning models, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), excel at identifying complex patterns and relationships within large datasets. In the context of virtual screening, these models can be applied to tasks such as protein- ligand docking, activity prediction and de novo molecule generation.

One notable application is the use of AlphaFold-predicted protein structures to optimize structure-based virtual screening campaigns. By providing high-accuracy models for previously unresolved targets, AlphaFold enables the screening of ligands against a broader range of proteins, including those with significant implications for aging. Additionally, generative adversarial networks (GANs) and variational autoencoders (VAEs) are being employed to design novel compounds with desired properties, expanding the chemical space available for anti-aging drug discovery.

Despite these advances, challenges remain in ensuring the interpretability and generalizability of deep learning models. Training these models requires extensive, high-quality datasets and their predictions must be validated through experimental studies to ensure real-world applicability. Future developments in explainable AI and transfer learning may help address these issues, enabling more reliable and actionable insights⁶.

5.5  Aging Drug Discovery Driven by Multi-Omics Data

The advent of multi-omics technologies, encompassing genomics, transcriptomics, proteomics and metabolomics, has revolutionized aging research by providing a systems-level understanding of molecular processes. Integrating these datasets with virtual screening offers unprecedented opportunities to identify novel drug targets and intervention strategies.

For example, transcriptomic analyses can reveal age-related changes in gene expression, highlighting pathways that may be amenable to therapeutic modulation. Proteomic studies can further elucidate post-translational modifications and protein- protein interactions that drive aging processes. Metabolomics, meanwhile, can provide insights into metabolic shifts associated with aging and identify small molecules that may restore homeostasis.

By combining these data streams, researchers can construct comprehensive models of aging networks, enabling the identification of key nodes and hubs for targeted intervention. Virtual screening can then be applied to identify compounds that modulate these targets, accelerating the translation of omics- based insights into therapeutic candidates.

Emerging technologies such as microfluidics and organ- on-a-chip systems offer additional opportunities for efficient validation. These platforms enable the testing of candidate compounds in physiologically relevant environments, bridging the gap between in vitro studies and in vivo applications. By incorporating these innovations, the virtual screening pipeline can be further optimized, reducing the time and cost associated with drug discovery.

6.  Summary and Outlook

Virtual screening (VS) has emerged as a cornerstone in aging research, providing an indispensable computational approach for exploring the molecular mechanisms of aging and accelerating the development of anti-aging therapies. By targeting pivotal signaling pathways such as mTOR, SIRT1, AMPK and FOXO, VS has facilitated the discovery of numerous active compounds that delay aging-related cellular and systemic dysfunctions. These advancements not only pave the way for novel drug development but also contribute to a deeper understanding of aging as a dynamic and modifiable process. VS has transformed the traditional pipeline of drug discovery by integrating compound libraries such as ZINC and PubChem, along with structural databases like PDB and AlphaFold, to enable high- throughput and precise identification of promising targets. This capability has reduced the timeline and cost associated with drug development, positioning VS as a key driver of innovation in the anti-aging field.

Despite its successes, VS in aging research faces significant challenges stemming from the inherent complexity of aging. Aging is governed by interconnected molecular networks involving multiple signaling pathways, epigenetic modifications and diverse cell types, all of which vary significantly across tissues and individuals. For instance, the roles of pathways such as ROS, AMPK and mTOR are context-dependent, often exhibiting dual or contradictory effects in different biological systems. This complexity complicates the identification of universal targets and necessitates a more nuanced approach to drug design. Additionally, the incomplete and biased nature of existing biological data poses a major hurdle. While databases like PDB and AlphaFold have expanded the accessibility of protein structure information, many critical targets remain unresolved or poorly characterized. Experimental datasets often suffer from variability in experimental conditions or sample sources, further exacerbating issues of reproducibility and predictive accuracy.

The predictive models underpinning VS also present limitations. Scoring functions, while central to molecular docking, frequently oversimplify the interactions between ligands and their targets. This can lead to false positives or negatives, resulting in wasted resources during experimental validation. Furthermore, current models often struggle to incorporate the dynamic and flexible nature of protein-ligand interactions, as well as the influence of cellular and systemic environments. The reliance on computationally intensive processes also limits the scalability of VS in scenarios requiring the integration of large compound libraries or high-resolution simulations.

To address these challenges, future developments in VS will need to prioritize several key areas. The integration of deep learning technologies holds immense promise for improving the accuracy and scalability of virtual screening. By leveraging neural networks, VS can predict compound-target interactions with higher precision, even in cases involving highly dynamic or poorly characterized targets. Deep learning models can also assist in generating novel molecular structures and exploring vast chemical spaces, enabling the discovery of innovative compounds with anti-aging potential. For example, the use of AlphaFold’s predicted protein structures in conjunction with advanced deep learning algorithms could revolutionize structure- based VS by enhancing the quality of docking predictions.

The incorporation of multi-omics data-spanning genomics, transcriptomics, proteomics and metabolomics-represents another critical frontier. Multi-omics approaches can uncover the dynamic changes in molecular networks during aging, providing a comprehensive framework for identifying and validating therapeutic targets. For instance, transcriptomic data highlighting age-related changes in gene expression can guide the prioritization of targets for intervention, while metabolomic analyses can reveal potential biomarkers for evaluating drug efficacy. Integrating these datasets with VS workflows will allow researchers to tailor drug discovery efforts to the complex, multifactorial nature of aging.

Moreover, the efficient integration of VS with experimental validation will be crucial for translating computational predictions into actionable therapies. Advances in molecular dynamics simulations can refine docking results by accounting for protein flexibility and solvent effects, improving the reliability of hit compounds. High-throughput experimental techniques, such as high-content screening and mass spectrometry, will further accelerate the validation of candidate molecules. These experimental platforms can also provide feedback to refine VS models, creating a synergistic cycle that enhances both computational predictions and empirical outcomes.

Another promising avenue is the development of multitarget drugs that address the multifaceted nature of aging. Aging involves the simultaneous dysregulation of multiple pathways and therapies targeting a single mechanism are often insufficient. By designing compounds that modulate multiple pathways-such as mTOR inhibition coupled with AMPK activation-researchers can develop interventions that provide more robust and holistic benefits. Advances in computational methods, including network pharmacology and systems biology approaches, will enable the rational design of these multitarget agents^4,24-28.

In addition to methodological advancements, the expansion and refinement of supporting databases will play a pivotal role in the future of VS. Databases that integrate comprehensive information on aging biomarkers, experimental validation results and clinical trial outcomes will enhance the reliability and relevance of VS predictions^3.

Cloud-based computing platforms, offering scalable and high-performance computational resources, will further support large-scale VS campaigns, enabling the efficient screening of ultra-large virtual libraries containing billions of compounds.

As these technologies and methodologies converge, VS is poised to remain at the forefront of aging research, driving innovations that not only elucidate the molecular basis of aging but also accelerate the development of precise, effective interventions. By addressing the challenges of complexity, data quality and translational validation, VS can help bridge the gap between basic research and clinical application. This will ultimately enable the development of therapies that extend health span, mitigate age-related diseases and improve the quality of life for an aging global population. The future of VS in aging research is one of immense potential, marked by the promise of transformative breakthroughs in our understanding and management of the aging process¹.

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