Topology-enhanced machine learning model (Top-ML) for anticancer peptide prediction

TL;DR

This paper introduces Top-ML, a topology-enhanced machine learning model that uses novel peptide topological features for improved anticancer peptide prediction, achieving state-of-the-art results with better interpretability.

Contribution

The paper presents a new topology-based feature extraction method for peptides and integrates it into an ML model, enhancing anticancer peptide prediction performance.

Findings

Top-ML achieves state-of-the-art performance on benchmark datasets.

Topology-based features improve peptide classification accuracy.

Model offers greater interpretability than existing deep learning approaches.

Abstract

Recently, therapeutic peptides have demonstrated great promise for cancer treatment. To explore powerful anticancer peptides, artificial intelligence (AI)-based approaches have been developed to systematically screen potential candidates. However, the lack of efficient featurization of peptides has become a bottleneck for these machine-learning models. In this paper, we propose a topology-enhanced machine learning model (Top-ML) for anticancer peptides prediction. Our Top-ML employs peptide topological features derived from its sequence "connection" information characterized by vector and spectral descriptors. Our Top-ML model, employing an Extra-Trees classifier, has been validated on the AntiCP 2.0 and mACPpred 2.0 benchmark datasets, achieving state-of-the-art performance or results comparable to existing deep learning models, while providing greater interpretability. Our results highlight the potential of leveraging novel topology-based featurization to accelerate the identification of anticancer peptides.