HomRank: Homogeneous RNA Ranking for 3D Structure Evaluation

Abstract

Leveraging rigorously curated RNA sequences and a novel ranking-based evaluation paradigm, we propose an improved pipeline for RNA 3D structure assessment. To enhance generalization, we construct a dataset comprising non-redundant single-chain RNA sequences from the PDB and apply unsupervised clustering to minimize data leakage. Based on this, we retrain the previous state-of-the-art method, ARES, to develop ARES+, which improves the top-1 success rate by 21% on the RNA Puzzles benchmark. To further boost near-native identification, we introduce HomRank, a homogeneous RNA ranking method that directly optimizes the selection of near-native conformations from candidate sets, resembling expert evaluation strategies. HomRank achieves 95% and 100% top-1 and top-5 success rates, respectively, significantly outperforming ARES+. These results demonstrate that carefully designed datasets and the expert-like selection paradigm can substantially improve the accuracy and robustness of RNA 3D structure assessment, offering a promising direction for deep learning-based RNA evaluation and near-native conformation selection.