A Systematic Benchmark for Peptide Property Prediction
Journal:
bioRxiv
Published Date:
Feb 10, 2026
Abstract
Accurate prediction of peptide physicochemical properties and biological activities is critical for rational peptide design and high-throughput screening. However, current research is often constrained by heterogeneous data sources and inconsistent evaluation standards, which hinder fair comparisons and reliable assessments of model generalization. In this work, we present PPB, a peptide property prediction benchmark designed to evaluate model performance with an emphasis on realistic generalization across both classification and regression tasks. By applying unified biological filtering criteria, we systematically curated and standardized 15 datasets comprising 161,571 unique sequences, spanning a wide range of physicochemical properties and functional activities. We benchmarked seven representative architectures--encompassing traditional machine learning, deep learning, and pre-trained language models--alongside diverse feature encoding schemes. Furthermore, we investigated the impact of random versus homology-based (sequence similarity) data splitting strategies on model robustness. To facilitate community access, we developed the PPB web server (http://ppb.molmatrix.com/index.html), which provides centralized resources for standardized dataset downloads, interactive visualization of benchmark results, and detailed evaluation protocols.
