Integrative Proteome-Wide Virtual Screening for Pan-Flaviviral Antiviral Scaffolds

Anderson Pereira Soares created this dataset in 2026, detailing a computational pipeline for broad-spectrum antiviral discovery. It contains results from homology modeling and high-throughput docking of 160 natural product and repurposed drug scaffolds against structural and nonstructural proteins from five flaviviruses. The analysis identified 40 top-ranked scaffolds, including myricetin and temoporfin, with estimated binding affinities provided.

Use Cases

• Prioritizing lead compounds for experimental validation based on estimated binding energies (Kd) and multitarget profiles.
• Analyzing conserved binding sites across flavivirus proteins (NS5 polymerase, E glycoprotein) for pan-viral therapeutic design.
• Comparing the structural dynamics of viral proteins across Zika, Yellow Fever, and West Nile viruses using RMSD and PCA results.
• Filtering compound libraries for drug-like properties using applied pharmacokinetic rules (Lipinski's, ADMET).

Strengths

• The dataset is the product of a standardized, multi-step computational pipeline combining homology modeling, pocket prediction, and exhaustive docking (2,000 runs per pocket).
• It provides specific estimated binding affinities (Kd) for lead compounds, such as 1.2 nM for temoporfin and 1.9 µM for myricetin.
• The analysis covers five distinct flaviviruses (Zika, Yellow Fever, West Nile, Saint Louis encephalitis, Usutu), enabling comparative studies.

Limitations

• The dataset is stored as a 1.7 MB PDF, which may require extraction to access structured data for computational analysis.
• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment for large-scale machine learning tasks.

Provenance

Source

figshare, authored by Anderson Pereira Soares.

Collection Method

Data generated through integrative computational methods: homology modeling of viral proteins, sequence/structure-based pocket prediction, electrostatic profiling, pharmacokinetic filtering, and high-throughput molecular docking.

Freshness

Last updated 2026-04-30 05:24:54; freshness should be verified.