ArticleCurrent drug discovery technologies2026
Exploring the Neurotherapeutic Potential of Bacillus-derived Macrolactins as NMDAR Antagonists for Alzheimer's Disease: Network Pharmacology and Molecular Modeling Insights.
Article in Current drug discovery technologies, 2026. 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
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionThe N-methyl-D-aspartate receptor (NMDAR) plays a critical role in regulating excitatory glutamatergic neurotransmission and synaptic plasticity. However, excessive NMDAR activation can lead to increased calcium ion influx, resulting in excitotoxicity- a key contributor to neurodegenerative diseases. Although current NMDAR inhibitors exist, their clinical use is limited due to adverse effects.
methodsThis study employed computational screening of Bacillus-derived macrolactins to identify potential NMDAR antagonists. Molecular docking simulations were performed using AMDock v1.5.2 with the AutoDock Vina engine to assess binding affinities to NMDAR (PDB:7SAD). Docked complexes were analyzed for chemical interactions, including polar contacts, using PyMol v2 and Discovery Studio Visualizer v4.5. Pharmacokinetic properties of macrolactins were predicted using Deep-PK. Molecular dynamics simulations via GROMACS assessed complex stability through RMSD, RMSF, radius of gyration (Rg), hydrogen bond count, and solvent-accessible surface area (SASA). Network pharmacology analysis of macrolactins in Alzheimer's disease (AD) involved mapping target interactions in STRING, importing into Cytoscape, and identifying hub genes using CytoHubba for KEGG pathway enrichment.
resultsMacrolactin F emerged as a promising candidate, exhibiting strong binding affinity (-6.8 kcal/mol) and an estimated Ki of 10.37 μM, outperforming commercial memantine and other macrolactins. Molecular dynamics simulations confirmed the stability and conformational integrity of the Macrolactin F-NMDAR complex. KEGG pathway enrichment analysis highlighted key hub pathways associated with AD, including hsa05010, hsa04725, hsa04722, hsa04071, hsa04068, hsa04150, and hsa04910. DISCUSSION: The findings suggest that Macrolactin F possesses superior antagonistic activity against NMDAR compared to memantine, supported by molecular docking and dynamic simulations. Network pharmacology analyses indicate that Macrolactin F can modulate critical signaling pathways implicated in AD, including PI3K/Akt/mTOR and MAPK cascades.
conclusionComputational analyses identify Macrolactin F as a promising preclinical candidate for developing allosteric NMDAR inhibitors. This aligns with SDG 3 by contributing to potential therapeutics for neurodegenerative diseases such as Alzheimer's disease and supports SDG 10 by promoting accessible interventions to reduce global health disparities.
Indexed as
Identifiers
41572710What OpenQuestion 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 OpenQuestion graph.