SynVerse: a modular framework for building and evaluating deep learning-based drug synergy prediction models.

ABSTRACT: Synergistic drug combinations are often used to treat cancer. Experimental exploration of all possibilities is expensive. Deep learning (DL) offers a potential alternative for predicting drug pair synergy in specific cell lines. However, current methods often suffer from data leakage and lack systematic ablation studies. We propose SynVerse, a comprehensive evaluation framework featuring four data-splitting strategies to assess DL model generalizability and three ablation studies: module-based, feature shuffling, and a novel network-based approach to disentangle factors influencing performance. We evaluated sixteen models incorporating eight drug- and cell line-specific features, five preprocessing techniques, and two encoders. Our analysis revealed that no model outperformed a baseline us

SUBMITTER: Tasnina N 

PROVIDER: S-EPMC12753315 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature