Benchmarking Sequence-Based Compound-Protein Interaction Prediction through Constructing a Debiased Data Set CDPN.

ABSTRACT: Accurate prediction of compound-protein interactions (CPIs) is critical for drug discovery, but existing data sets often suffer from biases that hinder model generalization. Here, we first highlighted that over-represented molecular scaffolds and imbalanced label distributions can lead to machine learning shortcuts. While existing debiasing approaches often compromise data set diversity, we present Clustering-based Down-sampling and Putative Negatives (CDPN), a novel protocol for constructing a debiased CPI benchmark. CDPN mitigates biases through compound Cluster-level Down-sampling and generates Putative Negatives from unexplored chemical spaces, ensuring balanced label distributions. Using CDPN, we systematically benchmark deep learning-based CPI models, with a particular focus on prote

SUBMITTER: Hao Y 

PROVIDER: S-EPMC12690583 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature