ArticleDrug design, development and therapy2025
Identification and Validation of Antidepressant Small Molecules Using Bioinformatics and Mouse Depression Models.
Article in Drug design, development and therapy, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Depression, a prevalent psychiatric disorder with limited effective treatments, can be addressed by repurposing existing small molecules via bioinformatics as a promising approach, though previous studies using tools like CMAP and GEO have successfully identified candidate drugs for neuropsychiatric disorders, few have combined in silico predictions with in vivo validation for it. Objective: The aim of this study was to employ bioinformatics and in vivo experimental validation to mine potential antidepressant small molecule compounds. Methods: This study utilized data from the GEO database, employing bioinformatics analysis methods to analyze the dataset. The CMAP platform was used to deeply explore potential antidepressant small-molecule compounds. In vivo experiments validated the antidepressant effects of the small-molecule compounds on a chronic restraint stress mouse model. Results: This study identified 311 differentially expressed genes (DEGs) from GSE182193-associated with the PI3K-Akt, MAPK, and neurotrophic factor signaling pathways, with key genes identified via Weighted Gene Co-expression Network Analysis (WGCNA) and immune correlation analysis-and screened 5 candidate compounds via CMAP, among which pyrimethamine, pifithrin-mu, and mibefradil significantly improved depressive behaviors in chronic restraint stress (CRS) model mice by regulating key protein expression in the PI3K-Akt and neurotrophic factor signaling pathways, as shown by a 33.44%-60.32% increase in movement distance in the open field test (P < 0.01 to P < 0.001) and a 20.25%-30.19% decrease in immobility time in the forced swim test (P < 0.01 to P < 0.001). Conclusion: This study shows that pyrimethamine, pifithrin-mu, and mibefradil can regulate key proteins in the PI3K-Akt and neurotrophic factor pathways, improving depressive behaviors in mice and indicating their potential in alleviating depression; additionally, bioinformatics-driven repurposing of existing drugs for antidepressant discovery is more efficient than de novo development, and this study provides an exploratory demonstration of this.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.