Multifunctional Bioactive Peptides Designed via Machine Learning for Food Packaging

Sree Nithish Reddy Gunapati's dataset contains results from a computational framework integrating machine learning and optimization for designing multifunctional bioactive peptides. The data likely includes sequences and properties of peptides predicted to have antioxidant, antifungal, and antibacterial properties for potential application in active food packaging systems. The dataset was last updated on May 12, 2026.

Use Cases

• Training new machine learning models for peptide property prediction based on the described classification models.
• Benchmarking optimization algorithms for combinatorial sequence design using the generated peptide sequences.
• Analyzing amino acid composition patterns for multifunctional bioactive peptides as described in the study.

Strengths

• Includes machine learning models with reported accuracies of 82.37%, 95.49%, and 96.75% for different property classifications.
• Dataset is accompanied by a free-to-use executable tool for designing peptides, as mentioned in the description.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• The dataset is small in scale at 399.4 KB, indicating limited scope.

Provenance

Source

figshare

Collection Method

Generated via a computational framework integrating machine learning models and an optimization algorithm.

Freshness

Last updated 2026-05-12 13:57:20; freshness should be verified.