An Integrated Computational Workflow Combining NeuroScore, Network Pharmacology, and Molecular Dynamics Identifies a Benzothiadiazole‐Based SERT Inhibitor for Alzheimer's Disease
TL;DR
A benzothiadiazole derivative is presented as a promising SERT‐modulating lead for AD, establishing a robust computational foundation for experimental validation and establishing a robust computational foundation for experimental validation.
Abstract
Alzheimer's disease (AD) lacks effective disease‐modifying therapies, highlighting the need for novel CNS‐targeted drug candidates. This study employs an integrated computational framework, combining a custom NeuroScore‐based CNS screening, network pharmacology, molecular docking, and molecular dynamics (MD) simulations, to identify 2,1,3‐benzothiadiazole derivatives as potential AD therapeutics. Screening 52,931 PubChem compounds yielded a top candidate (ID: 143693958; NeuroScore: 25.86; MW: 313.4 Da, LogP: 3.12, TPSA: 41.8 Å 2 ). Network pharmacology identified 87 AD‐overlapping targets, pinpointing serotonin transporter (SERT) as the key therapeutic target via serotonergic pathway enrichment. Molecular docking demonstrated strong SERT binding affinity (MolDock score: −132.72 kcal/mol), surpassing reference paroxetine (−130.51 kcal/mol). A 200 ns MD simulation confirmed structural stability (RMSD: 0.22 ± 0.03 nm; R g : 2.38 nm), and MM‐PBSA analysis revealed favorable binding free energy (−18.39 kJ/mol), driven predominantly by van der Waals interactions (−184.38 kJ/mol). ADMET profiling predicted excellent intestinal absorption (94.34%) with no AMES toxicity. This study presents a benzothiadiazole derivative as a promising SERT‐modulating lead for AD, establishing a robust computational foundation for experimental validation.