Supplementary File 1: Computational Models for Dual TrkA/TrkB Agonists

A. Vignesh Pandi authored a supplementary document detailing an in silico drug discovery pipeline for Alzheimer's disease. The study, last updated on 2026-06-01, identified six novel quinoline analogues as computationally predicted dual TrkA/TrkB agonists. The document likely contains results from pharmacophore modelling, molecular docking, ADMET analysis, DFT calculations, and molecular dynamics simulations.

Use Cases

• Validate computational drug discovery pipelines based on the described homology and pharmacophore modelling methods.
• Benchmark molecular docking and MM/GBSA scoring protocols based on the reported binding energies for quinoline analogues.
• Assess ligand stability and binding behavior for novel compounds based on the described 100-300 ns molecular dynamics simulations.
• Evaluate predicted pharmacokinetic properties for drug candidates based on the described ADMET analysis framework.

Strengths

• The study reports specific computational results, including docking scores ranging from -8.90 to -5.07 kcal/mol and MM/GBSA scores from -40.47 to -30.71 kcal/mol.
• The document details a multi-stage in silico pipeline, including homology modelling, virtual screening, and molecular dynamics simulations.
• Six novel optimized quinoline analogues (OP-1 to OP-6) are identified as primary outputs of the computational study.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• The data is contained within a 2.2 MB DOCX file, which may require parsing to extract structured information.

Provenance

Source

A. Vignesh Pandi via figshare

Collection Method

In silico drug discovery pipeline involving homology modelling, virtual screening, molecular docking, ADMET, MM/GBSA, DFT, and molecular dynamics simulations.

Freshness

Last updated 2026-06-01 05:36:26; freshness should be verified.