AI-Designed Anticancer Peptide Properties and Bioactivity Scores

Fifteen novel anticancer peptide sequences were generated by artificial intelligence. The dataset provides calculated physicochemical properties and bioactivity scores for each sequence. Binyu Li authored this resource, which was last updated in March 2026.

Use Cases

• Analyze correlations between specific physicochemical property columns and bioactivity scores to guide peptide design.
• Benchmark AI peptide generation models by comparing predicted properties in this dataset against new candidate sequences.
• Train regression models to predict bioactivity scores from physicochemical property features for virtual screening.
• Validate structure-activity relationship hypotheses by examining the property profiles of these 15 AI-designed sequences.

Strengths

• Contains 15 unique AI-designed peptide sequences.
• Provides both physicochemical properties and bioactivity scores for each entry.
• Data is openly shared under a CC BY 4.0 license.

Limitations

• The dataset is very small, with only 15 peptide sequences, limiting statistical power.
• Bioactivity scores are likely computational predictions rather than experimentally validated measurements.
• No raw experimental data or clinical trial results are included to confirm the scores.

Provenance

Source

Binyu Li via figshare.

Collection Method

Peptide sequences were designed by AI; properties and scores were subsequently calculated.

Freshness

Last updated in March 2026.