Machine Learning for Anticancer Peptides

A machine learning model developed for determining anticancer activity of peptides

Patent-Pending Research Project

This project involves the development of a sophisticated machine learning approach to predict anticancer activity in antimicrobial peptides (AMPs). Using a random forest model based on amino acid and dipeptide features, I was able to screen 10,000 AMPs for potential anticancer properties.

Key Accomplishments

  • Developed a random forest model that achieved 79.5% accuracy on the validation dataset
  • Identified 1,648 peptides with promising anticancer activity
  • Performed additional analyses including clustering, physicochemical property predictions, and 3D homology modeling
  • Results suggest potential dual therapeutic effects of AMPs in cancer treatment
  • Patent application filed for the prediction methodology

Technologies Used

  • Machine learning (Random Forest algorithm)
  • Bioinformatics tools for peptide sequence analysis
  • Molecular modeling
  • Statistical analysis

This research demonstrates the power of computational approaches in drug discovery, particularly for identifying dual-purpose peptides that may serve as both antimicrobial and anticancer agents.

References