ChronobioticsDB: The Database of Drugs and Compounds Modulating Circadian Rhythms
Abstract
1. Introduction
2. Results
2.1. Database Web Interface
2.2. Database Statistics and Analysis
2.3. The Classification of Molecules According to the Major Chemical Groups
- Terpenes/Steroids;
- Alkaloids;
- Sulfonamides/Sulfonates;
- Aromatic compounds (Phenols and Flavonoids);
- Organofluorines/Organochlorines;
- Quaternary ammonium compounds;
- Amides/Peptides;
- Esters/Lipids;
- Heterocycles (Pyridine and Thiophene);
- Nitro compounds;
- Phosphates/Nucleotides;
- Organometallics/Salts;
- Macrocyclic compounds;
- Disulfides;
- Alkyne derivatives;
- Barbiturates;
- Metal salts.
2.4. Key Observations on the Proposed Classes in ChronobioticsDB
3. Discussion
3.1. Ethical Considerations
3.2. Future Updates and Community Engagement
3.3. Limitations, International Validation
4. Materials and Methods
4.1. Data Acquisition
4.1.1. Data Sources and Extraction
4.1.2. Data Quality Assessment and Validation
- (a)
- Peer-reviewed articles published in the past five decades.
- (b)
- Research demonstrating both acute and chronic effects on circadian rhythms.
- (c)
- Compounds studied through both in vitro and in vivo methodologies.
4.1.3. Data Integration and Standardization
- (a)
- Compound names were normalized following the IUPAC nomenclature (if available).
- (b)
- Controlled vocabularies were established for biological terms relating to chronobiotic effects to ensure uniformity across entries.
4.1.4. Database Implementation
4.2. Database Organization (Primary and Secondary Data)
4.3. Database Architecture
4.4. Core Components of the Architecture
4.4.1. Central Table: Chronobiotic
4.4.2. Auxiliary Tables
- One-to-Many Relationships: The synonyms table is connected to Chronobiotic via the foreign key originalbiotic.
- Many-to-Many Relationships: The target, mechanism, effects, article, and class tables are linked to Chronobiotic through intermediary tables, which are automatically generated by Django.
4.4.3. Schema of Table Relationships
- Chronobiotic → synonyms: A single compound may have multiple synonyms.
- Chronobiotic → target: A single compound may interact with multiple targets, and a single target may be associated with multiple compounds.
- Chronobiotic → mechanism: A single compound may exhibit multiple mechanisms of action, and a single mechanism may be associated with multiple compounds.
- Chronobiotic → class: A single compound may belong to multiple classes, and a single class may encompass multiple compounds.
- Chronobiotic → effects. A single compound may exhibit multiple effects on circadian rhythms.
- Chronobiotic → article. A single compound may exhibit multiple literature sources where it is described, and one source also may contain many compounds.
4.4.4. Technologies and Tools
- DBMS: PostgreSQL, a robust and reliable relational database, provides high performance and supports complex queries [15].
- ORM: Django ORM (Django 5.1.2) is employed for database interactions at the Python (v3.11) code level. This eliminates the need for manual SQL query writing and facilitates efficient data management [16].
- Indexes: Indexes have been created on frequently queried fields, such as gname, smiles, and targetsname, to optimize search performance.
- Migrations: Django’s built-in migration system allows for seamless modifications to the database structure without data loss.
4.4.5. Ensuring Data Integrity and Security
- Foreign Keys: All inter-table relationships are implemented through foreign keys, ensuring data integrity.
- Unique Constraints: Unique fields (gname, smiles, molecula, and iupacname) prevent record duplication.
- Role-Based Access Control: Database access is restricted at the user and role levels, ensuring data security.
- Encryption: Confidential data is stored in an encrypted format.
4.5. Use of Artificial Intelligence
5. Conclusions and Future Perspectives
5.1. Content Expansion and Data Curation
5.2. Integration of AI-Driven Search and Assisting Tools
5.3. Computational Chronobiotics Discovery
5.4. Gerontological Applications and Predictive Modeling
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Trait of Research | CRITERIA | |
---|---|---|
Research Inclusion | Research Exclusion | |
Date of Publication | Present | Absent |
Age of an article, years | >0 | >50 |
The source/subject is relevant to the research question | Yes | No |
Appropriate academic and technical level | Yes | No |
Source authority and authorship | Peer-reviewed journal article, scientific report, governmental or academic website, description of authors, presence of affiliation, publisher information | Non-peer-reviewed sources, incomplete information about author, affiliations, publisher, journal or book, or online resource |
Accuracy of information presentation in the source | There are no mistakes or unclear statements. Statements are supported by evidence. Information is reliable and is presented in reliable form. | Numerous mistakes, statements unsupported by evidence, unreliable information, and unclear presentation of it. |
Purpose of the source publication | Academic or technical use | Entertainment, opinion, propaganda |
Cited literature in the source | Bibliography, link in the text with a description of the source | Absence of any links and bibliographic records or inappropriate non-scholarly sources cited |
Effect on circadian rhythm described in the article | Present | Absent |
Sample size, objects, or patients treated | >30 | <30 |
Reproducibility | Methods are reproducible (Clearly described source of compound or way of extraction/synthesis, doses, regimen, model organism strain or patients cohort described, the method of circadian rhythm measurements and statistics are represented properly) | Not reproducible, speculation (Not clearly described source of compound or way of extraction/synthesis, nonclear doses, regimen. Model organism strain or patient cohort is not appropriate for academic study, the methods of circadian rhythm measurements and statistics are not described or mentioned in general aspect without citation) |
Interaction with target | Described | Not described |
Model object | Having a circadian molecular clock and circadian rhythms of physiological and molecular processes | The circadian patterns in the object are not described and there is no molecular machinery of the oscillator |
Ethical aspect | Ethically appropriate protocol of study, verified with an ethical committee if needed | Illegal or unethical protocol described |
Presence of chemical compound or living organism (if probiotic) | Yes | No |
Presence of the mechanism of activity | Yes | No |
Presence of a chemical graphic formula | Yes | No |
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Share and Cite
Solovev, I.A.; Golubev, D.A.; Yagovkina, A.I.; Kotelina, N.O. ChronobioticsDB: The Database of Drugs and Compounds Modulating Circadian Rhythms. Clocks & Sleep 2025, 7, 30. https://doi.org/10.3390/clockssleep7030030
Solovev IA, Golubev DA, Yagovkina AI, Kotelina NO. ChronobioticsDB: The Database of Drugs and Compounds Modulating Circadian Rhythms. Clocks & Sleep. 2025; 7(3):30. https://doi.org/10.3390/clockssleep7030030
Chicago/Turabian StyleSolovev, Ilya A., Denis A. Golubev, Arina I. Yagovkina, and Nadezhda O. Kotelina. 2025. "ChronobioticsDB: The Database of Drugs and Compounds Modulating Circadian Rhythms" Clocks & Sleep 7, no. 3: 30. https://doi.org/10.3390/clockssleep7030030
APA StyleSolovev, I. A., Golubev, D. A., Yagovkina, A. I., & Kotelina, N. O. (2025). ChronobioticsDB: The Database of Drugs and Compounds Modulating Circadian Rhythms. Clocks & Sleep, 7(3), 30. https://doi.org/10.3390/clockssleep7030030