Next Article in Journal
Assessing Monoclonal and Polyclonal Antibodies in Sepsis and Septic Shock: A Systematic Review of Efficacy and Safety
Previous Article in Journal
The Role of Skin Microbiota in Facial Dermatoses and Related Factors: A Narrative Review
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
IJMS
Volume 26
Issue 18
10.3390/ijms26188858
ijms-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

In Silico Assessment of Potential Geroprotectors: From Separate Endpoints to Complex Pharmacotherapeutic Effects

by
Leonid Stolbov
1,*,
Anastasia Rudik
1,
Alexey Lagunin
1,2,
Dmitry Druzhilovskiy
1,
Dmitry Filimonov
1 and
Vladimir Poroikov
1
1
Institute of Biomedical Chemistry, 10 Bldg. 8, Pogodinskaya Str., 119121 Moscow, Russia
2
Bioinformatics Department, Pirogov Russian National Research Medical University, 1 Bldg. 6 Ostrovityanova Str., 117513 Moscow, Russia
*
Author to whom correspondence should be addressed.
Int. J. Mol. Sci. 2025, 26(18), 8858; https://doi.org/10.3390/ijms26188858
Submission received: 7 August 2025 / Revised: 8 September 2025 / Accepted: 10 September 2025 / Published: 11 September 2025
(This article belongs to the Special Issue In Silico Approaches to Drug Design and Discovery)
Browse Figures
Review Reports Versions Notes

Abstract

This study presents an approach for the in silico assessment of potential geroprotectors that target the multifaceted mechanisms of aging, implemented in the PASS GERO web application. This work is timely given the societal impact of aging—the primary risk factor for major chronic diseases. The urgent need to extend healthspan—the period of life spent in good health—motivates the search for compounds that modulate fundamental aging mechanisms. The model estimates the probabilities of 117 aging-related biological activities with high predictive accuracy, achieving an average Invariant Accuracy of Prediction (IAP) of 0.967 under cross-validation. Validation using known geroprotectors (rapamycin, metformin, and resveratrol) demonstrated strong concordance between predicted activities and documented molecular mechanisms of action. For instance, the model correctly predicted rapamycin’s inhibition of mTOR and metformin’s activation of AMPK. The PASS GERO web application provides a systematic strategy to prioritize novel compound candidates for experimental evaluation in anti-aging research. We discuss challenges including the chemical diversity of the training data, the need for validated biomarkers, and the limitations of translating computational predictions into clinical outcomes, positioning the tool as robust application for activity profiling in discovery workflows.

1. Introduction

Aging is a complex biological phenomenon characterized by the progressive decline of physiological resilience, leading to increased susceptibility to chronic diseases, functional impairment, and mortality [1,2]. This process involves interrelated molecular and cellular effects such as genomic instability [3], telomere shortening, epigenetic dysregulation, mitochondrial dysfunction, cellular senescence, and impaired nutrient-sensing pathways, being also influenced by lifestyle, genetics, and environmental factors. The above-mentioned mechanisms collectively lead to systemic dysfunction across tissue and organ systems [4,5].
With advancing age, the probability of chronic conditions including cardiovascular diseases, type 2 diabetes, cancer, and neurodegenerative disorders rises significantly [6]. While conventional medical approaches focus on treating these pathologies individually, they often address symptoms rather than targeting the underlying complex biological drivers of aging itself [7]. This reactive strategy fails to address the issues that lead to many age-related diseases simultaneously [8,9].
Geroprotectors—compounds designed to modulate the known aging pathways—are increasingly discussed in scientific literature as a transformative therapeutic strategy. Unlike single-disease therapies, geroprotectors aim to extend healthspan (the chronic disease-free period of life) and compress morbidity by intervening in fundamental aging mechanisms and thus increasing lifespan [10]. The conceptual shift towards geroprotection is based on the recognition of aging as a malleable biological process. Several studies in model organisms, from Caenorhabditis elegans to mice, have demonstrated that such interventions as caloric restriction, genetic modifications, and pharmacological agents (e.g., rapamycin) can extend lifespan. Translating these results to humans, however, remains challenging [11,12]. The described factors of aging complicate the identification of robust biomarkers and therapeutic targets. Moreover, the high costs, ethical constraints, and timelines of clinical trials for aging slow down such investigations. Despite these hurdles, geroprotective candidates like senolytics, mTOR inhibitors, NAD+ boosters, and metformin have emerged as front-runners, showing promise in preclinical studies and early-stage human trials [13,14]. Preclinical and early clinical studies suggest these agents may delay or prevent multiple age-related conditions, offering a systemic and prophylactic approach [10,15].
The statistics of publications dedicated to geroprotectors and presented in PubMed [16] demonstrate the exponential increase in the recent years (Figure 1).
The heterogeneity of aging processes demands interventions embracing multiple pathways at a time. Effective geroprotection requires polypharmacological actions, as aging itself arises from the interplay of diverse biological processes. Both the need to consider the complexity and heterogeneity of aging and the aim to intervene in several biological processes simultaneously make the discovery of potential geroprotectors particularly challenging. So, multiparametric estimations are required to capture the diverse aspects of aging [17,18].
Taking the complexity of aging process into account, the identification and evaluation of geroprotectors strictly rely on computational methods, particularly for virtual screening and multi-target activity estimation [19,20,21,22]. In silico approaches facilitate the prioritization of candidates by analyzing chemical structures, predicting biological activities, and modeling interactions with aging-related molecular targets. These tools address challenges posed by the chemical diversity of geroprotectors and the need for multi-objective optimization [23].
“One of the earliest and most widely used examples of data-mining target elucidation is the continuously curated and expanded Prediction of Activity Spectra for Substances (PASS) software” [24]. The PASS software estimates the probable biological activity profile corresponding to the structural formula of a drug-like organic compound. Classification of a compound’s belonging to the “actives” or “inactives” is based on the analysis of structure-activity relationships for the training set of compounds with the known biological activities using a machine learning approach. Particular compound’s biological activity is defined qualitatively as “active” or “inactive”. Molecular structure is described as Multilevel Neighborhoods of Atoms (MNA) descriptors. The algorithm of biological activity spectrum prediction is based on the improved naïve Bayes classifier. The current version of PASS, PASS 2024, estimates 9047 types of biological activity with an average invariant accuracy of 0.934 based on the training set that includes more than 1,482,930 drugs, drug-candidates, pharmaceutical agents, and chemical probes, as well as the compounds with known specific toxicity profiles. Hundreds of biological activities predicted by PASS 2024 may be considered eligible for the evaluation of potential geroprotectors, while some anti-aging activities should still be added to the existing SAR knowledgebase. Nevertheless, some applications of PASS for the evaluation of potential geroprotectors were already performed earlier [25,26,27].
This study presents the development and validation of a novel computational approach for in silico assessment of potential geroprotectors, implemented in a specialized version of our software, PASS GERO. We expanded the system with a comprehensive set of aging-related biological activities, yielding highly predictive models for 117 anti-aging mechanisms, including senolysis, mitochondrial function, and epigenetic regulation. These models showed strong performance, with a mean Invariant Accuracy of Prediction (IAP) of 0.967 in leave-one-out cross-validation and 0.966 in 20-fold cross-validation.
Importantly, applying PASS GERO to well-established geroprotectors such as rapamycin, metformin, and resveratrol produced results that closely matched their known mechanisms of action. For example, the model correctly predicted that rapamycin inhibits mTOR and enhances autophagy, and that resveratrol activates sirtuins, underscoring its biological relevance and its ability to recapitulate the complex polypharmacology of aging interventions.
The principle behind PASS GERO is straightforward: a researcher inputs a compound’s chemical structure—by drawing it or importing a structure file—and the application generates a comprehensive report predicting its activity across all 117 modeled mechanisms of aging. This delivers a multi-target polypharmacological profile in a single step, enabling the prioritization of candidates for subsequent research, including virtual screening of large libraries, guiding the synthesis of novel analogs, and planning targeted experimental studies.
PASS GERO not only provides a robust framework for predicting anti-aging activities but also delivers experimentally verifiable insights, making it a practical tool for prioritizing candidate compounds for further studies.

2. Results

2.1. Activities

We prepared the list of anti-aging biological activities based on their relevance to impact the fundamental features of aging, addressing both its principal targets and associated pathologies [10,15]. By integrating the above-mentioned strategies, this list reflects a multi-target, systems-level approach to geroprotection. Each activity is chosen by the evidence that it is related to longevity pathways observed in humans, ensuring relevance to both basic aging biology and age-associated diseases. This list, grouped by categories, is given below.

2.1.1. Prevention of Free Radical Oxidation

Category: free radical scavengers, antioxidants; transition metal chelators; and lipid peroxidation-resistant fatty acids.
Relevant biological activities implemented in PASS GERO:
  • Free radical scavenger;
  • Antioxidant;
  • Adenosine deaminase inhibitor;
  • Lipid peroxidase inhibitor;
  • Phosphatidic acid synthesis inhibitor;
  • Chelator (Iron).

2.1.2. Maintenance of Mitochondrial Function

Category: mitohormetics, mitochondrial electron transport chain inhibitors; mitophagy inducers; PPARγ/PGC-1α activators; and NAD+ precursors.
Relevant biological activities implemented in PASS GERO:
  • Mitochondrial electron transport inhibitor;
  • Peroxisome proliferator-activated receptor gamma (PPARγ) agonist.

2.1.3. Proteostasis Support

Category: anti-glycation agents; crosslink inhibitors of advanced glycation end products (AGEs); RAGE receptor antagonists; anti-amyloid compounds; extracellular matrix turnover stimulators; autophagy inducers; proteasome activators; and partial translation inhibitors.
Relevant biological activities implemented in PASS GERO:
  • RAGE receptor antagonist;
  • Antiamyloidogenic;
  • Autophagy inducer.

2.1.4. Senolytics/Senostatics

Category: compounds promoting selective elimination of senescent cells or delaying their emergence.
Relevant biological activities implemented in PASS GERO:
  • Senolytic activity.

2.1.5. Suppression of Genomic Instability

Category: antimutagenic compounds; telomere stabilizers; and retrotransposition inhibitors.
Relevant biological activities implemented in PASS GERO:
  • Antimutagenic;
  • Telomerase stimulant;
  • DNA-directed RNA polymerase inhibitor.

2.1.6. Epigenetic Regulators

Category: HDAC/HAT inhibitors and SIRT activators.
Relevant biological activities implemented in PASS GERO:
  • Histone deacetylase (HDAC) inhibitors (classes I–IV, isoforms 1–11, SIRT1–7);
  • Sir2p inhibitor;
  • Histone acetyltransferase (HAT) inhibitors (KAT2A/B, KAT5–7, p300, PCAF, RTT109);
  • Nuclear receptor coactivator 1/3 inhibitors.

2.1.7. Aging-Related Signaling Pathway Inhibitors

Category: mTORC1, PI3K, Ras, Myc, AT1 inhibitors; and AMPK and Klotho activators.
Relevant biological activities implemented in PASS GERO:
  • mTOR complex 1 inhibitor;
  • Phosphatidylinositol 3-kinase (PI3K) inhibitors (alpha, beta, delta, gamma);
  • Myc inhibitor;
  • Angiotensin AT1 receptor antagonist;
  • AMP-activated protein kinase (AMPK) subunit inhibitors.

2.1.8. Anti-Inflammatory Agents

Category: NF-κB inhibitors and NRF2 activators.
Relevant biological activities implemented in PASS GERO:
  • NF-κB pathway inhibitors (NF-κB1, NF-κB2, RelA, RelB);
  • NRF2 stimulant;
  • COX-2, iNOS inhibitors;
  • IGF-1–6 antagonists;
  • HIF-1α inhibitor.

2.1.9. Antifibrotic Agents

Category: TGF-β → ALK5 → p-Smad2/3 pathway inhibitors.
Relevant biological activities implemented in PASS GERO:
  • TGF-β antagonists (isoforms 1–3);
  • TGF-β receptor type I kinase inhibitor;
  • SMAD3 inhibitor.

2.1.10. Neurotrophic Factors

Category: BDNF mimetics and central/peripheral nervous system support agents.
Relevant biological activities implemented in PASS GERO:
  • Neurotrophic factor enhancer;
  • TRKB agonist.

2.1.11. Barrier Function Preservation

Category: matrix metalloproteinase (MMP) inhibitors and tight junction protein synthesis activators.
Predicted Activities:
  • Metalloproteinase-9 inhibitor.

2.1.12. Immunomodulators

Category: thymus regeneration promoters and naïve T-cell pool preservers.
Relevant biological activities implemented in PASS GERO:
  • Estradiol 17β-dehydrogenase inhibitors (isoforms 1–3);
  • 5-Alpha-reductase inhibitors (isoforms 1–2);
  • Melatonin receptor agonists (subtypes 1–5);
  • JAK/STAT pathway inhibitors (JAK1–3, STAT1–6).

2.1.13. Miscellaneous

Category: age-related degenerations/age-associated memory impairment treatment and some antidiabetic mechanisms.
Relevant biological activities implemented in PASS GERO:
  • 5 Hydroxytryptamine 3 antagonist;
  • 5 Hydroxytryptamine 7 agonist;
  • Age-related macular degeneration treatment;
  • Alpha 2 adrenoreceptor antagonist;
  • Insulin growth factor antagonist;
  • Sodium/glucose cotransporter 2 inhibitor.

2.2. Implementation

Integrating this extensive, mechanistically diverse activity dataset with a large-scale training set of more than 1.48 million compounds yielded a comprehensive SAR knowledge base. As detailed in Table 1, the 117 resulting prediction models exhibit consistently high Invariant Accuracy of Prediction (IAP) scores, with a mean exceeding 0.96. These results indicate that the models are not merely descriptive but highly predictive—an essential criterion for practical use in drug discovery. Their robustness—evidenced by strong performance across categories ranging from Senolytic to Epigenetic Regulator–establishes PASS GERO as a reliable tool for generating mechanistically grounded hypotheses about compound activity, which we subsequently validated.
According to Table 1, the most frequent activities belong to the following categories: Epigenetic regulators (38 activities), Immunomodulators (19 activities), Aging-related signaling pathway inhibitors (15 activities), and Anti-inflammatory agents (14 activities). They comprise about 73% of all activities covered by the current version of PASS GERO.
The average IAP value obtained in the leave-one-out cross-validation, IAPLOO CV, is 0.967. The average IAP value obtained in the 20-Fold cross-validation, IAP20-fold CV, is 0.966.
As shown above, the IAPLOO CV and IAP20-fold CV values are almost equal. Thus, the PASS GERO SAR knowledgebase proves to be robust and provides both high accuracy and predictivity.

2.3. Web Application

We developed the web application PASS GERO https://www.way2drug.com/gero/ (accessed on 6 August 2025) that predicts probable mechanisms of anti-aging action for drug-like compounds and is freely available for academic aging-related studies. It offers predictive models to assess geroprotective activities through SAR analysis, enabling researchers to prioritize compounds with activity against aging targets like sirtuins, senescence-associated pathways, etc. It covers 117 different anti-aging activities providing the opportunity to evaluate the complex geroprotective potential for the analyzed compounds.
One may use a particular structural formula that can be drawn using Marvin JS, or loaded using a SMILES string, or, if the structure belongs to an existing drug, it can be identified by the drug name. To perform a prediction, the user may choose one of the twelve categories of anti-aging biological activities or select the option “All activities”. Another parameter that the user should select is the cutoff on Pa values (see Section 4).
Figure 2 shows the prediction results for Urolithin A (URO A), which elevated antioxidant enzymes (SOD, CAT) and reduced lipid peroxidation (MDA) in liver tissues [28], also, URO A lowered pro-inflammatory cytokines (TNF-α, IL-6, NF-κB) [28]. The results of a clinical study of URO A in humans showed that it enhanced mitochondrial activity in older human participants [29]. In non-polar media, compounds UroA exhibit antiradical capabilities against the Radical Adduct Formation (RAF) processes [30]. As shown in Figure 2, the prediction results correlate well with the experimental data, which demonstrates PASS GERO applicability for geroprotective activity assessment.

3. Discussion

Currently, no pharmaceutical agent is officially approved for medical use as a geroprotector. The literature analysis reveals several drugs corresponding to the criteria for classification as potential geroprotectors, some of which are being investigated in clinical trials for treating age-associated diseases [14,15,31,32,33,34,35]. Information about these drugs is provided in Table 2. To retrospectively validate the results of PASS GERO, we predicted the geroprotective activity spectra for the reference compounds. Some prediction results with the cutoff Pa > Pi are summarized in Table 2.
The full spectrum of predicted activities, along with the probabilities of being active or inactive, is provided in the Supplementary Materials (Table S1). External validation (Table 2) shows strong concordance between the known biological mechanisms of the reference compounds and the activities predicted by PASS GERO. However, this correlation should be interpreted with caution.

3.1. Relationship Between Mechanisms of Action and Predicted Activities

Predicted activities denote molecular mechanisms that likely contribute to the observed, higher-level mechanism of action (MoA). For example, the established senolytic MoA of Dasatinib and Navitoclax is reflected in the high-probability prediction of “Senolytic” activity. Conversely, for Resveratrol, its complex, multifaceted MoA (e.g., “improvement of mitochondrial function”) is resolved into a set of specific predicted activities—such as “Mitochondrial electron transport inhibitor”, “Histone deacetylase SIRT1 activator”, and “Autophagy inducer”—each representing a potential molecular pathway through which its overall geroprotective effect is achieved.

3.2. Polypharmacology and Prioritizing Predictions

Predicting multiple activities for a single compound is not only expected but also a core feature of PASS GERO, reflecting the polypharmacology of many effective geroprotectors. Not all predicted activities carry equal weight. Confidence in each prediction is quantified by the probabilities Pa (Probability of being Active) and Pi (Probability of being Inactive). A higher Pa indicates greater confidence that the compound exhibits the specified activity. Researchers should prioritize activities with Pa > Pi and, more stringently, those with a high Pa (e.g., Pa > 0.5) for further experimental investigation. The profile should be evaluated holistically: a compound with several medium-probability predictions across synergistic pathways (e.g., antioxidant, anti-inflammatory, and autophagy-inducing) may be more promising than one with a single high-probability prediction for an isolated activity.

3.3. Confidence, Validation, and Overprediction

While the high cross-validation accuracy (IAP ≈ 0.97) demonstrates strong internal predictive performance, not every predicted activity will be experimentally confirmed across all compounds. The model identifies potential activities based on structural similarity to known actives. Some predictions may reflect true but as-yet-untested biological effects, offering testable hypotheses for research. Others may be overpredictions—an acknowledged limitation of in silico methods. For example, the model may correctly flag a compound’s potential to interact with a target based on its structure, yet the interaction may not be physiologically relevant due to factors such as bioavailability, metabolism, or cellular context.
Accordingly, PASS GERO’s outputs are not definitive claims of activity but a ranked, probability-weighted set of testable hypotheses. The tool prioritizes the most promising candidates and identifies their most likely mechanisms of action, thereby focusing limited experimental resources. As with any computational assessment, all predicted activities require rigorous validation in vitro and in vivo. This cautious framing is essential for the tool’s effective use in geroprotector discovery.

4. Methods and Materials

4.1. The Training Set

The majority of SAR data for relevant biological activities were obtained based on PASS 2024; the data on senolytic activity was taken from the publication of Wong et al., 2023 [19]. After the training, the following statistics represent the obtained SAR knowledgebase:
  • The number of substances in the training set: 1,483,030;
  • The number of unique MNA descriptors: 141,153;
  • The number of activities selected for the prediction: 117.

4.2. SAR Knowledgebase for Geroprotective Activities

Based on the information from the prepared set of structures with geroprotective activity, we performed the training procedure using the general machine approach realized in PASS. This approach is described in several publications earlier [47,48,49]. In brief, it used the MNA (Multilevel Neighborhoods of Atoms) descriptors reflecting the physico-chemical mechanisms of ligand-target interactions [50], and an improved naïve Bayes classifier as a machine learning approach. During the training, the leave-one-out procedure is used, which increases the predictivity of the obtained SAR models. The accuracy of each model is estimated with Invariant Accuracy of Prediction (IAP) calculated by the application of leave-one-out cross-validation; and predictivity—by the application of 20-fold cross-validation [48,49]. As a result of the training, the PASS GERO SAR knowledgebase is developed. This knowledgebase can be further applied for the prediction of geroprotective activity for new compounds using their structural formulae presented in SDF or MOL formats as an input file. An output file includes the lists of the predicted activities with two probabilities, Pa and Pi, which estimate the probability of belonging to the active and inactive compounds, respectively. The activities in this list are arranged in descending order of ΔP = Pa − Pi values, where higher ΔP values indicate a greater confidence in the prediction. By default, all activities predicted with ΔP > 0 are considered probable; however, depending on the particular task, the user may select another cutoff. Additional recommendations for the interpretation of the prediction results are presented in our publications [47,48,49].

4.3. Web Application

To provide the possibility for a wide utilization of the PASS GERO software by the scientific community, we developed a freely available web application at the Way2Drug portal https://www.way2drug.com/gero/ (accessed on 6 August 2025). The PHP programming languages were used to develop the server side of the web application. HTML, CSS, PHP, and JavaScript were used to create the web interface.

5. Conclusions

Despite recent progress, in silico discovery of geroprotectors faces substantial obstacles. The pronounced chemical heterogeneity of candidate compounds—from small synthetic molecules to complex natural products—complicates the construction of robust training sets. The field also lacks reliable, validated biomarkers to quantify the effects of aging interventions, a gap further compounded by the multifactorial nature of aging itself. These scientific challenges are exacerbated by ethical considerations and the formidable complexity of clinical trial design for aging interventions, collectively hindering clinical translation.
Our study directly addresses the first of these challenges. PASS GERO is specifically designed to navigate the chemical diversity of candidate geroprotectors. By leveraging MNA descriptors and a large, purposefully curated training set spanning a broad range of chemical scaffolds, our model provides a unified framework for evaluating structurally diverse compounds. Validation results indicate strong predictive performance for established geroprotectors with markedly different structures—from the complex macrolide rapamycin to the simple polyphenol resveratrol—underscoring its utility. While it does not solve the biomarker validation challenge, PASS GERO offers a systematic, mechanism-based prioritization strategy to guide preclinical research toward the most promising multi-target candidates, thereby de-risking the earliest stages of drug discovery in geroscience.
Critically, validation of PASS GERO against a panel of established geroprotectors confirms its predictive power and biological relevance. The software accurately recapitulates the known polypharmacological profiles of reference compounds.
This capability to generate a comprehensive mechanistic profile for a single molecule—whether from a hand-drawn structure or a SMILES string—provides researchers with a powerful tool to prioritize candidates for virtual screening, inform the synthesis of novel analogs, and design targeted experimental studies, thereby de-risking the early stages of geroprotector discovery.
Nevertheless, significant challenges remain—chief among them the lack of validated biomarkers of aging and the complexity of designing clinical trials for aging interventions. Although PASS GERO cannot resolve these translational hurdles, it directly addresses the initial bottleneck: compound selection. Future work will expand the model’s scope to encompass emerging mechanisms, including hormetins and gut microbiota modulators. As this integrated knowledge base grows, PASS GERO will gain predictive power, improving the likelihood of identifying clinical candidates that target the hallmarks of aging and prevent age-related diseases.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/ijms26188858/s1.

Author Contributions

The conceptualization of the project was carried out by L.S. and V.P.; A.L. and L.S. were responsible for curating the data and conducting formal analysis. Methodology development was a collaborative effort between L.S., A.R., A.L., D.D. and D.F.; V.P. oversaw project administration, and provided supervision throughout. Validation of the findings was conducted by L.S. and A.L.; Lastly, the original draft of the writing was primarily composed by L.S. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Ministry of Science and Higher Education of the Russian Federation within the framework of Agreement No. 075-15-2024-643.

Data Availability Statement

We provide full academic access to the PASS GERO web resource. All senolytics-related data utilized in this study are derived from publicly accessible sources. Specifically, these datasets are available in the Supplementary Materials of our cited reference https://doi.org/10.1038/s43587-023-00415-z (accessed on 6 August 2025), requiring no additional deposition. Using PASS GERO for prediction is free.

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviations

The following abbreviations are used in this manuscript:
IAPInvariant Accuracy of Prediction
LOO CVLeave-One-Out Cross-Validation
MoAMechanism of Action
PASSPrediction of Activity Spectra for Substances
SARStructure-Activity Relationships

References

  1. Harris, J.R.; Korolchuk, V.I. (Eds.) Biochemistry and Cell Biology of Ageing: Part I Biomedical Science; Subcellular Biochemistry; Springer: Singapore, 2018; Volume 90, ISBN 978-981-13-2834-3. [Google Scholar]
  2. Harris, J.R.; Korolchuk, V.I. (Eds.) Biochemistry and Cell Biology of Ageing: Part II Clinical Science; Subcellular Biochemistry; Springer: Singapore, 2019; Volume 91, ISBN 978-981-13-3680-5. [Google Scholar]
  3. Proshkina, E.; Shaposhnikov, M.; Moskalev, A. Genome-Protecting Compounds as Potential Geroprotectors. Int. J. Mol. Sci. 2020, 21, 4484. [Google Scholar] [CrossRef] [PubMed]
  4. Nie, C.; Li, Y.; Li, R.; Yan, Y.; Zhang, D.; Li, T.; Li, Z.; Sun, Y.; Zhen, H.; Ding, J.; et al. Distinct Biological Ages of Organs and Systems Identified from a Multi-Omics Study. Cell Rep. 2022, 38, 110459. [Google Scholar] [CrossRef] [PubMed]
  5. Tenchov, R.; Sasso, J.M.; Wang, X.; Zhou, Q.A. Aging Hallmarks and Progression and Age-Related Diseases: A Landscape View of Research Advancement. ACS Chem. Neurosci. 2024, 15, 1–30. [Google Scholar] [CrossRef] [PubMed]
  6. Guo, J.; Huang, X.; Dou, L.; Yan, M.; Shen, T.; Tang, W.; Li, J. Aging and Aging-Related Diseases: From Molecular Mechanisms to Interventions and Treatments. Signal Transduct. Target. Ther. 2022, 7, 391. [Google Scholar] [CrossRef] [PubMed]
  7. De Magalhães, J.P. Distinguishing between Driver and Passenger Mechanisms of Aging. Nat. Genet. 2024, 56, 204–211. [Google Scholar] [CrossRef] [PubMed]
  8. Rothermund, K.; Englert, C.; Gerstorf, D. Explaining Variation in Individual Aging, Its Sources, and Consequences: A Comprehensive Conceptual Model of Human Aging. Gerontology 2023, 69, 1437–1447. [Google Scholar] [CrossRef]
  9. Oh, H.S.-H.; Rutledge, J.; Nachun, D.; Pálovics, R.; Abiose, O.; Moran-Losada, P.; Channappa, D.; Urey, D.Y.; Kim, K.; Sung, Y.J.; et al. Organ Aging Signatures in the Plasma Proteome Track Health and Disease. Nature 2023, 624, 164–172. [Google Scholar] [CrossRef]
  10. Moskalev, A.; Guvatova, Z.; Lopes, I.D.A.; Beckett, C.W.; Kennedy, B.K.; De Magalhaes, J.P.; Makarov, A.A. Targeting Aging Mechanisms: Pharmacological Perspectives. Trends Endocrinol. Metab. 2022, 33, 266–280. [Google Scholar] [CrossRef]
  11. Anjaneyulu, J.; Godbole, A. Small Organism Models for Mode of Action Research on Anti-Ageing and Nootropic Herbs, Foods, and Formulations. Nutr. Neurosci. 2024, 28, 744–762. [Google Scholar] [CrossRef]
  12. Lee, M.B.; Kaeberlein, M. Translational Geroscience: From Invertebrate Models to Companion Animal and Human Interventions. Transl. Med. Aging 2018, 2, 15–29. [Google Scholar] [CrossRef]
  13. Burd, C.E.; Gill, M.S.; Niedernhofer, L.J.; Robbins, P.D.; Austad, S.N.; Barzilai, N.; Kirkland, J.L. Barriers to the Preclinical Development of Therapeutics That Target Aging Mechanisms: Table 1. J. Gerontol. A. Biol. Sci. Med. Sci. 2016, 71, 1388–1394. [Google Scholar] [CrossRef]
  14. Zhang, Z.; Yang, R.; Zi, Z.; Liu, B. A New Clinical Age of Aging Research. Trends Endocrinol. Metab. 2025, 36, 440–458. [Google Scholar] [CrossRef]
  15. Moskalev, A.A. Potential Geroprotectors—From Bench to Clinic. Biochem. Mosc. 2023, 88, 1732–1738. [Google Scholar] [CrossRef]
  16. PubMed. 2025. Available online: https://pubmed.ncbi.nlm.nih.gov/?term=geroprotector+OR+antiaging+medicine (accessed on 6 August 2025).
  17. Zhang, Y.; Huo, D.; Liu, Z.; Li, X.; Wang, Z.; Li, W. Review on Ginseng and Its Potential Active Substance G-Rg2 against Age-Related Diseases: Traditional Efficacy and Mechanism. J. Ethnopharmacol. 2025, 337, 118781. [Google Scholar] [CrossRef] [PubMed]
  18. Xing, J.; Luo, X.; Jia, K.; Liu, S.; Chen, S.; Qiao, G.; Zhang, C.; Yi, J. Integrating Network Pharmacology and Experimental Verification to Explore the Pharmacological Mechanisms of Asparagus against Polycystic Ovary Syndrome. J. Ovarian Res. 2023, 16, 128. [Google Scholar] [CrossRef] [PubMed]
  19. Wong, F.; Omori, S.; Donghia, N.M.; Zheng, E.J.; Collins, J.J. Discovering Small-Molecule Senolytics with Deep Neural Networks. Nat. Aging 2023, 3, 734–750. [Google Scholar] [CrossRef]
  20. Smer-Barreto, V.; Quintanilla, A.; Elliott, R.J.R.; Dawson, J.C.; Sun, J.; Campa, V.M.; Lorente-Macías, Á.; Unciti-Broceta, A.; Carragher, N.O.; Acosta, J.C.; et al. Discovery of Senolytics Using Machine Learning. Nat. Commun. 2023, 14, 3445. [Google Scholar] [CrossRef] [PubMed]
  21. Aliper, A.; Jellen, L.; Cortese, F.; Artemov, A.; Karpinsky-Semper, D.; Moskalev, A.; Swick, A.G.; Zhavoronkov, A. Towards Natural Mimetics of Metformin and Rapamycin. Aging 2017, 9, 2245–2268. [Google Scholar] [CrossRef]
  22. Zhavoronkov, A.; Mamoshina, P.; Vanhaelen, Q.; Scheibye-Knudsen, M.; Moskalev, A.; Aliper, A. Artificial Intelligence for Aging and Longevity Research: Recent Advances and Perspectives. Ageing Res. Rev. 2019, 49, 49–66. [Google Scholar] [CrossRef]
  23. Arora, S.; Mittal, A.; Duari, S.; Chauhan, S.; Dixit, N.K.; Mohanty, S.K.; Sharma, A.; Solanki, S.; Sharma, A.K.; Gautam, V.; et al. Discovering Geroprotectors through the Explainable Artificial Intelligence-Based Platform AgeXtend. Nat. Aging 2024, 5, 144–161. [Google Scholar] [CrossRef]
  24. Mervin, L.H.; Afzal, A.M.; Drakakis, G.; Lewis, R.; Engkvist, O.; Bender, A. Target Prediction Utilising Negative Bioactivity Data Covering Large Chemical Space. J. Cheminform. 2015, 7, 51. [Google Scholar] [CrossRef]
  25. Bocharova, O.A.; Ionov, N.S.; Kazeev, I.V.; Shevchenko, V.E.; Bocharov, E.V.; Karpova, R.V.; Sheychenko, O.P.; Aksyonov, A.A.; Chulkova, S.V.; Kucheryanu, V.G.; et al. Computer-aided Evaluation of Polyvalent Medications’ Pharmacological Potential. Multiphytoadaptogen as a Case Study. Mol. Inform. 2023, 42, 2200176. [Google Scholar] [CrossRef] [PubMed]
  26. Corominas-Faja, B.; Santangelo, E.; Cuyàs, E.; Micol, V.; Joven, J.; Ariza, X.; Segura-Carretero, A.; García, J.; Menendez, J.A. Computer-Aided Discovery of Biological Activity Spectra for Anti-Aging and Anti-Cancer Olive Oil Oleuropeins. Aging 2014, 6, 731–741. [Google Scholar] [CrossRef] [PubMed]
  27. Gillbro, J.M.; Lundahl, M.; Westman, M.; Baral, R.; Al-Bader, T.; Mavon, A. Structural Activity Relationship Analysis (SAR) and in Vitro Testing Reveal the Anti-ageing Potential Activity of Acetyl Aspartic Acid. Int. J. Cosmet. Sci. 2015, 37, 15–20. [Google Scholar] [CrossRef] [PubMed]
  28. Karim, S.; Madani, B.; Burzangi, A.S.; Alsieni, M.; Bazuhair, M.A.; Jamal, M.; Daghistani, H.; Barasheed, M.O.; Alkreathy, H.; Khan, M.A.; et al. Urolithin A’s Antioxidative, Anti-Inflammatory, and Antiapoptotic Activities Mitigate Doxorubicin-Induced Liver Injury in Wistar Rats. Biomedicines 2023, 11, 1125. [Google Scholar] [CrossRef]
  29. URO A Clinical Study. Available online: https://www.ncbi.nlm.nih.gov/pubmed/32694802 (accessed on 6 August 2025).
  30. Milanović, Ž. Comprehensive Kinetic Investigation of Antiradical Activity of Urolithins and Their Potential Inhibitory Effect on Xanthine Dehydrogenase. J. Mol. Liq. 2024, 408, 125330. [Google Scholar] [CrossRef]
  31. Rad, A.N.; Grillari, J. Current Senolytics: Mode of Action, Efficacy and Limitations, and Their Future. Mech. Ageing Dev. 2024, 217, 111888. [Google Scholar] [CrossRef]
  32. Espinoza, S.E.; Khosla, S.; Baur, J.A.; De Cabo, R.; Musi, N. Drugs Targeting Mechanisms of Aging to Delay Age-Related Disease and Promote Healthspan: Proceedings of a National Institute on Aging Workshop. J. Gerontol. Ser. A 2023, 78, 53–60. [Google Scholar] [CrossRef]
  33. Ilyushchenko, A.K.; Matchekhina, L.V.; Tkacheva, O.N.; Balashova, A.V.; Melnitskaia, A.A.; Churov, A.V.; Strazhesko, I.D. Senolytic Drugs: Implications for Clinical Practice. Probl. Gerosci. 2023, 1, 7–14. [Google Scholar] [CrossRef]
  34. Morsli, S.; Bellantuono, I. The Use of Geroprotectors to Prevent Multimorbidity: Opportunities and Challenges. Mech. Ageing Dev. 2021, 193, 111391. [Google Scholar] [CrossRef]
  35. Yusri, K.; Kumar, S.; Fong, S.; Gruber, J.; Sorrentino, V. Towards Healthy Longevity: Comprehensive Insights from Molecular Targets and Biomarkers to Biological Clocks. Int. J. Mol. Sci. 2024, 25, 6793. [Google Scholar] [CrossRef] [PubMed]
  36. Bramwell, L.R.; Frankum, R.; Harries, L.W. Repurposing Drugs for Senotherapeutic Effect: Potential Senomorphic Effects of Female Synthetic Hormones. Cells 2024, 13, 517. [Google Scholar] [CrossRef] [PubMed]
  37. Kang, S.M.; Park, J.H. Pleiotropic Benefits of DPP-4 Inhibitors Beyond Glycemic Control. Clin. Med. Insights Endocrinol. Diabetes 2021, 14, 11795514211051698. [Google Scholar] [CrossRef] [PubMed]
  38. Khajebishak, Y.; Alivand, M.; Faghfouri, A.H.; Moludi, J.; Payahoo, L. The Effects of Vitamins and Dietary Pattern on Epigenetic Modification of Non-Communicable Diseases: A Review. Int. J. Vitam. Nutr. Res. 2023, 93, 362–377. [Google Scholar] [CrossRef]
  39. Blagosklonny, M.V. Anti-Aging: Senolytics or Gerostatics (Unconventional View). Oncotarget 2021, 12, 1821–1835. [Google Scholar] [CrossRef]
  40. Song, Q.; Zhou, X.; Xu, K.; Liu, S.; Zhu, X.; Yang, J. The Safety and Antiaging Effects of Nicotinamide Mononucleotide in Human Clinical Trials: An Update. Adv. Nutr. 2023, 14, 1416–1435. [Google Scholar] [CrossRef]
  41. Zhou, D.-D.; Luo, M.; Huang, S.-Y.; Saimaiti, A.; Shang, A.; Gan, R.-Y.; Li, H.-B. Effects and Mechanisms of Resveratrol on Aging and Age-Related Diseases. Oxidative Med. Cell. Longev. 2021, 2021, 9932218. [Google Scholar] [CrossRef]
  42. Wirth, A.; Wolf, B.; Huang, C.-K.; Glage, S.; Hofer, S.J.; Bankstahl, M.; Bär, C.; Thum, T.; Kahl, K.G.; Sigrist, S.J.; et al. Novel Aspects of Age-Protection by Spermidine Supplementation Are Associated with Preserved Telomere Length. GeroScience 2021, 43, 673–690. [Google Scholar] [CrossRef]
  43. Santín-Márquez, R.; Alarcón-Aguilar, A.; López-Diazguerrero, N.E.; Chondrogianni, N.; Königsberg, M. Sulforaphane—Role in Aging and Neurodegeneration. GeroScience 2019, 41, 655–670. [Google Scholar] [CrossRef]
  44. Lujan, C.; Tyler, E.J.; Ecker, S.; Webster, A.P.; Stead, E.R.; Martinez-Miguel, V.E.; Milligan, D.; Garbe, J.C.; Stampfer, M.R.; Beck, S.; et al. An Expedited Screening Platform for the Discovery of Anti-Ageing Compounds in Vitro and in Vivo. Genome Med. 2024, 16, 85. [Google Scholar] [CrossRef]
  45. Kuerec, A.H.; Lim, X.K.; Khoo, A.L.; Sandalova, E.; Guan, L.; Feng, L.; Maier, A.B. Targeting Aging with Urolithin A in Humans: A Systematic Review. Ageing Res. Rev. 2024, 100, 102406. [Google Scholar] [CrossRef]
  46. Tavenier, J.; Nehlin, J.O.; Houlind, M.B.; Rasmussen, L.J.; Tchkonia, T.; Kirkland, J.L.; Andersen, O.; Rasmussen, L.J.H. Fisetin as a Senotherapeutic Agent: Evidence and Perspectives for Age-Related Diseases. Mech. Ageing Dev. 2024, 222, 111995. [Google Scholar] [CrossRef]
  47. Filimonov, D.A.; Druzhilovskiy, D.S.; Lagunin, A.A.; Gloriozova, T.A.; Rudik, A.V.; Dmitriev, A.V.; Pogodin, P.V.; Poroikov, V.V. Computer-Aided Prediction of Biological Activity Spectra for Chemical Compounds: Opportunities and Limitation. Biomed. Chem. Res. Methods 2018, 1, e00004. [Google Scholar] [CrossRef]
  48. Filimonov, D.; Poroikov, V. Probabilistic Approaches in Activity Prediction. In Chemoinformatics Approaches to Virtual Screening; Varnek, A., Tropsha, A., Eds.; The Royal Society of Chemistry: London, UK, 2008; pp. 182–216. ISBN 978-0-85404-144-2. [Google Scholar]
  49. Filimonov, D.A.; Lagunin, A.A.; Gloriozova, T.A.; Rudik, A.V.; Druzhilovskii, D.S.; Pogodin, P.V.; Poroikov, V.V. Prediction of the Biological Activity Spectra of Organic Compounds Using the Pass Online Web Resource. Chem. Heterocycl. Compd. 2014, 50, 444–457. [Google Scholar] [CrossRef]
  50. Filimonov, D.; Poroikov, V.; Borodina, Y.; Gloriozova, T. Chemical Similarity Assessment through Multilevel Neighborhoods of Atoms: Definition and Comparison with the Other Descriptors. J. Chem. Inf. Comput. Sci. 1999, 39, 666–670. [Google Scholar] [CrossRef]
Ijms 26 08858 g001
Figure 1. The number of publications in the field of geroprotection included in the PubMed database (PubMed query: “geroprotector OR anti-aging medicine”).
Figure 1. The number of publications in the field of geroprotection included in the PubMed database (PubMed query: “geroprotector OR anti-aging medicine”).
Ijms 26 08858 g001
Ijms 26 08858 g002
Figure 2. The prediction results for Urolithin A with PASS GERO web application.
Figure 2. The prediction results for Urolithin A with PASS GERO web application.
Ijms 26 08858 g002
Table 1. Characteristics of classification models for assessing geroprotective activities.
Table 1. Characteristics of classification models for assessing geroprotective activities.
N *IAP **Geroprotective Activity
CategoryPrevention of Free Radical Oxidation
26410.934Free radical scavenger
49910.925Antioxidant
3150.988Adenosine deaminase inhibitor
11530.959Lipid peroxidase inhibitor
7990.967Chelator
450.986Chelator, Iron
CategoryMaintenance of Mitochondrial Function
40.931Mitochondrial electron transport inhibitor
34440.986Peroxisome proliferator-activated receptor gamma agonist
CategoryProteostasis Support
560.919RAGE receptor antagonist
32860.931Antiamyloidogenic
5000.836Autophagy inducer
CategorySenolytics/senostatics
440.897Senolytic
CategorySuppression of Genomic Instability
400.974Antimutagenic
110.999Telomerase stimulant
32320.967DNA-directed RNA polymerase inhibitor
CategoryEpigenetic Regulators
49970.993Histone deacetylase 1 inhibitor
21380.992Histone deacetylase 2 inhibitor
17440.996Histone deacetylase 3 inhibitor
11100.994Histone deacetylase 4 inhibitor
18790.993Histone deacetylase 5 inhibitor
34550.994Histone deacetylase 6 inhibitor
5330.992Histone deacetylase 7 inhibitor
16370.992Histone deacetylase 8 inhibitor
5010.990Histone deacetylase 9 inhibitor
5810.995Histone deacetylase 10 inhibitor
6560.994Histone deacetylase 11 inhibitor
81.000Histone deacetylase 1A inhibitor
421.000Histone deacetylase 1B inhibitor
3370.956Histone deacetylase SIRT1 inhibitor
3110.981Histone deacetylase SIRT1 stimulant
5860.986Histone deacetylase SIRT2 inhibitor
50.999Histone deacetylase SIRT2 stimulant
1610.982Histone deacetylase SIRT3 inhibitor
680.995Histone deacetylase SIRT5 inhibitor
140.885Histone deacetylase SIRT6 inhibitor
62190.992Histone deacetylase class I inhibitor
50080.993Histone deacetylase class II inhibitor
25660.993Histone deacetylase class IIa inhibitor
34850.994Histone deacetylase class IIb inhibitor
8030.968Histone deacetylase class III inhibitor
6560.994Histone deacetylase class IV inhibitor
10,9850.984Histone deacetylase inhibitor
350.955Histone acetyltransferase KAT2A inhibitor
970.990Histone acetyltransferase KAT2B inhibitor
210.965Histone acetyltransferase KAT5 inhibitor
2430.999Histone acetyltransferase KAT6A inhibitor
110.980Histone acetyltransferase KAT7 inhibitor
410.973Histone acetyltransferase PCAF inhibitor
840.989Histone acetyltransferase RTT109 inhibitor
11000.935Histone acetyltransferase inhibitor
3990.974Histone acetyltransferase p300 inhibitor
1150.891Nuclear receptor coactivator 1 inhibitor
1710.840Nuclear receptor coactivator 3 inhibitor
CategoryAging-related Signaling Pathway Inhibitors
3100.964mTOR complex 1 inhibitor
11,7360.989Phosphatidylinositol 3-kinase alpha inhibitor
29220.988Phosphatidylinositol 3-kinase beta inhibitor
49940.989Phosphatidylinositol 3-kinase delta inhibitor
68950.986Phosphatidylinositol 3-kinase gamma inhibitor
18,0930.984Phosphatidylinositol 3-kinase inhibitor
5070.986Myc inhibitor
61760.994Angiotensin AT1 receptor antagonist
16250.998Angiotensin AT1A receptor antagonist
14130.999Angiotensin AT1B receptor antagonist
3940.934AMP-activated protein kinase inhibitor
3110.930AMP-activated protein kinase, alpha-1 subunit inhibitor
430.955AMP-activated protein kinase, alpha-2 subunit inhibitor
180.892AMP-activated protein kinase, beta-1 subunit inhibitor
150.945AMP-activated protein kinase, gamma-1 subunit inhibitor
CategoryAnti-Inflammatory Agents
17870.906Transcription factor NF kappa B inhibitor
4830.900Transcription factor NF kappa B1 inhibitor
740.995Transcription factor NF kappa B2 inhibitor
990.977Transcription factor RelA inhibitor
100.964NF-E2-related factor 2 stimulant
48850.992Poly(ADP-ribose) polymerase 1 inhibitor
16990.980Inducible nitric oxide synthase inhibitor
70790.967Cyclooxygenase 2 inhibitor
23270.960Ribosomal protein S6 kinase inhibitor
38980.979Insulin growth factor antagonist
38540.979Insulin like growth factor 1 antagonist
30.995Insulin like growth factor 2 antagonist
441.000Insulin like growth factor 3 antagonist
21250.913Hypoxia-inducible factor 1-alpha inhibitor
CategoryAntifibrotic Agents
40880.929Fibrosis treatment
21150.988Transforming growth factor beta 1 antagonist
1820.991Transforming growth factor beta 2 antagonist
750.965Transforming growth factor beta 3 antagonist
23670.980Transforming growth factor beta antagonist
2100.996TGF beta receptor type I kinase inhibitor
290.857SMAD3 inhibitor
CategoryNeurotrophic Factors
5240.963Neurotrophic factor
770.934Neurotrophic factor enhancer
590.974TRKB agonist
CategoryBarrier Function Preservation
29750.980Metalloproteinase-9 inhibitor
CategoryImmunomodulators
7040.997Estradiol 17 beta-dehydrogenase 1 inhibitor
5790.994Estradiol 17 beta-dehydrogenase 2 inhibitor
3020.996Estradiol 17 beta-dehydrogenase 3 inhibitor
12320.994Estradiol 17 beta-dehydrogenase inhibitor
6870.9965-Alpha-reductase 1 inhibitor
6420.9945-Alpha-reductase 2 inhibitor
17380.9945-Alpha-reductase inhibitor
3720.995Melatonin receptor 1 agonist
1880.997Melatonin receptor 2 agonist
6150.995Melatonin receptor agonist
63460.992Janus tyrosine kinase 1 inhibitor
10,8710.980Janus tyrosine kinase 2 inhibitor
80030.983Janus tyrosine kinase 3 inhibitor
15,1020.978Janus tyrosine kinase inhibitor
11740.918Transcription factor STAT inhibitor
260.831Transcription factor STAT1 inhibitor
8810.925Transcription factor STAT3 inhibitor
470.874Transcription factor STAT5 inhibitor
2470.957Transcription factor STAT6 inhibitor
CategoryMiscellaneous
24350.9885 Hydroxytryptamine 3 antagonist
910.9865 Hydroxytryptamine 7 agonist
1160.916Age-related macular degeneration treatment
27720.975Alpha 2 adrenoreceptor antagonist
22260.998Sodium/glucose cotransporter 2 inhibitor
* N is the number of active compounds in the training set; ** IAP is the Invariant Accuracy of Prediction for each activity estimated by the leave-one-out cross-validation.
Table 2. Potential geroprotectors under investigation and their predicted activities.
Table 2. Potential geroprotectors under investigation and their predicted activities.
Name and StructureMoA *Predicted Activities
Atorvastatin
Ijms 26 08858 i001		Targeting senescent cells, inhibition of CDKN2A expression [36]Mitochondrial electron transport inhibitor
Senolytic
Acetylsalicylic acid
Ijms 26 08858 i002		Targeting senescent cells, inhibition of CDKN2A expression
[36]		Transcription factor STAT1 inhibitor
Transcription factor NF kappa B inhibitor
SMAD3 inhibitor
Autophagy inducer
Transcription factor NF kappa B1 inhibitor
Free radical scavenger
Transcription factor STAT inhibitor
NF-E2-related factor 2 stimulant
Antioxidant
Antimutagenic
Histone deacetylase SIRT6 inhibitor
Neurotrophic factor enhancer
Hypoxia-inducible factor 1 alpha inhibitor
Chelator
Vildagliptin
Ijms 26 08858 i003		Reduction in stress-induced cellular senescence rate
[37]		Senolytic
Dasatinib
Ijms 26 08858 i004		Targeting senescent cells, inhibition of PI3K (Phosphoinositide 3-kinase)
[14]		Senolytic
Quercetin
Ijms 26 08858 i005		Targeting senescent cells, inhibition of PI3K (Phosphoinositide 3-kinase)
[14]		Senolytic
Curcumin
Ijms 26 08858 i006		Inhibitor of DNMT1, DNMT3B, HATs, HDAC2, and HDAC8
[38]		Free radical scavenger
Antioxidant
Transcription factor NF kappa B inhibitor
Antimutagenic
Antiamyloidogenic
Transcription factor NF kappa B1 inhibitor
Autophagy inducer
SMAD3 inhibitor
Transcription factor STAT inhibitor
Hypoxia-inducible factor 1 alpha inhibitor
Histone acetyltransferase inhibitor
Nuclear receptor coactivator 3 inhibitor
Lipid peroxidase inhibitor
Chelator
NF-E2-related factor 2 stimulant
Mitochondrial electron transport inhibitor
Insulin like growth factor 2 antagonist
Nuclear receptor coactivator 1 inhibitor
Metformin
Ijms 26 08858 i007		Autophagy, mitochondrial biogenesis
[14]		Histone deacetylase SIRT6 inhibitor
Transcription factor NF kappa B1 inhibitor
NF-E2-related factor 2 stimulant
Navitoclax
Ijms 26 08858 i008		Targeting senescent cells, inhibition of Bcl-2 and Bcl-xl
[39]		Senolytic
Transcription factor STAT5 inhibitor
Nicotinamide riboside
Ijms 26 08858 i009		Replenishment of NAD+ deficiency
[40]		Autophagy inducer
Nuclear receptor coactivator 3 inhibitor
Transcription factor NF kappa inhibitor
Histone deacetylase SIRT1 stimulant
Transcription factor STAT inhibitor
Mitochondrial electron transport inhibitor
Rapamycin (Sirolimus)
Ijms 26 08858 i010		Targeting senescent cells, mTOR inhibitor
[14]		Senolytic
mTOR complex 1 inhibitor
Transcription factor NF kappa B inhibitor
Neurotrophic factor
Fibrosis treatment
Hypoxia-inducible factor 1 alpha inhibitor
Resveratrol
Ijms 26 08858 i011		Reduction in oxidative stress, anti-inflammatory effect, improvement of mitochondrial function, regulation of apoptosis
[41]		Autophagy inducer
Antimutagenic
Free radical scavenger
Antioxidant
SMAD3 inhibitor
Nuclear receptor coactivator 3 inhibitor
Antiamyloidogenic
Transcription factor NF kappa B inhibitor
Histone deacetylase SIRT2 stimulant
Transcription factor NF kappa B1 inhibitor
NF-E2-related factor 2 stimulant
Chelator
Lipid peroxidase inhibitor
Histone deacetylase SIRT1 stimulant
Transcription factor STAT inhibitor
Hypoxia-inducible factor 1 alpha inhibitor
Nuclear receptor coactivator 1 inhibitor
Neurotrophic factor enhancer
Insulin like growth factor 2 antagonist
Mitochondrial electron transport inhibitor
Histone acetyltransferase inhibitor
Transcription factor RelA inhibitor
Senolytic
Spermidine
Ijms 26 08858 i012		Autophagy inducer
[42]		Autophagy inducer
Antimutagenic
Histone deacetylase SIRT6 inhibitor
Chelator
Transcription factor NF kappa B1 inhibitor
NF-E2-related factor 2 stimulant
Antiamyloidogenic
Transcription factor STAT1 inhibitor
Histone deacetylase SIRT1 inhibitor
Inducible nitric-oxide synthase inhibitor
SMAD3 inhibitor
Nuclear receptor coactivator 1 inhibitor
Transcription factor STAT inhibitor
Nuclear receptor coactivator 3 inhibitor
Sulforophane
Ijms 26 08858 i013		Antioxidant and anti-inflammatory effects, proteasome activation [43]NF-E2-related factor 2 stimulant
Transcription factor STAT1 inhibitor
Inducible nitric-oxide synthase inhibitor
Antioxidant
Antiamyloidogenic
Torin 2
Ijms 26 08858 i014		Inhibitor of TORC1 and TORC2
[44]		Transcription factor STAT1 inhibitor
Fibrosis treatment
AMP-activated protein kinase inhibitor
mTOR complex 1 inhibitor
Urolithin A
Ijms 26 08858 i015		Mitophagy
[45]		Antimutagenic
SMAD3 inhibitor
RAGE receptor antagonist
Nuclear receptor coactivator 3 inhibitor
Free radical scavenger
Antioxidant
Transcription factor NF kappa B inhibitor
NF-E2-related factor 2 stimulant
Antiamyloidogenic
Autophagy inducer
Neurotrophic factor enhancer
Hypoxia-inducible factor 1 alpha inhibitor
Nuclear receptor coactivator 1 inhibitor
Transcription factor STAT inhibitor
Mitochondrial electron transport inhibitor
17 beta-dehydrogenase 3 inhibitor
Senolytic
Fisetin
Ijms 26 08858 i016		Targeting senescent cells, inhibition of PI3K (Phosphoinositide 3-kinase)
[46]		Antimutagenic
Free radical scavenger
Antioxidant
SMAD3 inhibitor
Autophagy inducer
TRKB agonist
Nuclear receptor coactivator 3 inhibitor
Transcription factor NF kappa B inhibitor
Hypoxia-inducible factor 1 alpha inhibitor
NF-E2-related factor 2 stimulant
Transcription factor NF kappa B1 inhibitor
Lipid peroxidase inhibitor
Mitochondrial electron transport inhibitor
Histone deacetylase SIRT2 stimulant
Antiamyloidogenic
Transcription factor STAT inhibitor
Chelator
Histone deacetylase SIRT1 stimulant
RAGE receptor antagonist
Estradiol 17 beta-dehydrogenase inhibitor
Neurotrophic factor enhancer
* MoA—Mechanism of Action.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Stolbov, L.; Rudik, A.; Lagunin, A.; Druzhilovskiy, D.; Filimonov, D.; Poroikov, V. In Silico Assessment of Potential Geroprotectors: From Separate Endpoints to Complex Pharmacotherapeutic Effects. Int. J. Mol. Sci. 2025, 26, 8858. https://doi.org/10.3390/ijms26188858

AMA Style

Stolbov L, Rudik A, Lagunin A, Druzhilovskiy D, Filimonov D, Poroikov V. In Silico Assessment of Potential Geroprotectors: From Separate Endpoints to Complex Pharmacotherapeutic Effects. International Journal of Molecular Sciences. 2025; 26(18):8858. https://doi.org/10.3390/ijms26188858

Chicago/Turabian Style

Stolbov, Leonid, Anastasia Rudik, Alexey Lagunin, Dmitry Druzhilovskiy, Dmitry Filimonov, and Vladimir Poroikov. 2025. "In Silico Assessment of Potential Geroprotectors: From Separate Endpoints to Complex Pharmacotherapeutic Effects" International Journal of Molecular Sciences 26, no. 18: 8858. https://doi.org/10.3390/ijms26188858

APA Style

Stolbov, L., Rudik, A., Lagunin, A., Druzhilovskiy, D., Filimonov, D., & Poroikov, V. (2025). In Silico Assessment of Potential Geroprotectors: From Separate Endpoints to Complex Pharmacotherapeutic Effects. International Journal of Molecular Sciences, 26(18), 8858. https://doi.org/10.3390/ijms26188858

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop