Integration of network pharmacology, structure-based pharmacophore modeling, DFT, molecular docking, and molecular dynamics simulations to identify multi-target small molecule inhibitors against Alzheimer's disease.

Novel Pyrazoline Derivatives as Therapeutic Candidate for Alzheimer’s Disease: An Integrated Network Pharmacology and 3D-QSAR Modelling

Alzheimer’s disease (AD) is a complex neurodegenerative condition marked by amyloid-β accumulation, tau pathology, and neuroinflammation, while current single-target therapies largely provide symptomatic relief without effectively halting disease progression. Therefore, the rational design and synthesis of novel small...

An Integrated Computational Workflow Combining NeuroScore, Network Pharmacology, and Molecular Dynamics Identifies a Benzothiadiazole‐Based SERT Inhibitor for Alzheimer's Disease

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.

Network Pharmacology & Bioinformatics Insights Elucidate Garcinol as a Promising Natural MAO-B Inhibitor for Alzheimer’s Disease

Fungal Metabolites as Multi-Target Modulators of Alzheimer’s Disease: An Integrated In Silico Docking and Molecular Dynamics Approach

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder with limited therapeutic options offering only symptomatic relief. Natural fungal metabolites have recently gained attention for their neuroprotective potential. In this study, an extensive library of 18,349 fungal compounds was retrieved from the NPA...

Network Pharmacology, Molecular Docking, Molecular Dynamics Simulation and ADMET Analysis Predict the Molecular Mechanisms of Gomisin N and Schisandrin B as Multi-Target Agents in Primary Sclerosing Cholangitis

Primary sclerosing cholangitis (PSC) is a chronic cholestatic liver disease characterized by inflammatory, fibrotic, and immune-mediated mechanisms, with limited therapeutic options. In this study, an integrative computational strategy combining network pharmacology, molecular docking, molecular dynamics simulation, MM...

Computational de novo discovery of novel mTOR inhibitors using recurrent neural networks and integrated in silico approaches including docking and DFT

A Recurrent Neural Network-based de novo drug design approach was employed to discover novel mTOR inhibitors, and S000031 emerged as the most promising candidate due to its superior binding affinity, structural novelty, and electronic characteristics.