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

Findings highlight APOE, BACE1, and CSF1R as key therapeutic targets as key therapeutic targets and identify compound 2a as a promising multi-target lead candidate for additional experimental validation in AD.

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

Computational support is provided for the potential role of Gomisin N and Schisandrin B as multi-target candidates in PSC-related therapeutic research, although experimental validation is required to confirm their biological activity, pharmacokinetic behavior, and safety.

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.