Authors
Thi Lan Nguyen, Nguyen Quoc Khanh Le
Published in
Bioinformatics (Oxford, England). Aug 22, 2026. Epub Aug 22, 2026.
Abstract
RNA-small molecule binding site prediction is crucial for targeted drug discovery. Sequence-based methods are efficient but often fail to capture structural dependencies between nucleotides, whereas structure-aware graph models can better represent spatial interactions but typically rely on complex structural annotations and multi-stage preprocessing pipelines. We therefore developed GRASSP, a streamlined hybrid deep learning framework that integrates pretrained RNA language model (LM) representations with adaptive graph refinement.
GRASSP leverages nucleotide embeddings and predicted secondary-structure features from a pretrained RNA LM to construct spatial RNA graphs, followed by a lightweight two-step graph attention refinement module with adaptive gating to capture local and contextual nucleotide dependencies. Across four benchmark datasets (TE18, HARIBOSS, TL12, and JL10), GRASSP generally outperformed state-of-the-art baselines, with improvements of up to 24.1% in AUC and 44.5% in MCC. Ablation analyses showed that pretrained RNA representations provided the dominant predictive contribution, while spatial graph refinement offered complementary but dataset-dependent benefits. These results demonstrate that GRASSP provides a competitive framework for integrating pretrained RNA representations with spatial structural context while reducing reliance on additional handcrafted structural annotations.
Code and datasets are publicly available at https://github.com/langiocn/GRASSP, with an archival snapshot available on Zenodo at https://doi.org/10.5281/zenodo.21888291.
Supplementary data are available at Bioinformatics online.
PMID:
42633564
Bibliographic data and abstract were imported from PubMed on 23 Aug 2026.
Read full publication at:
Please sign in
to see all details.
Advertisement
Stats
- Recommendations n/a n/a positive of 0 vote(s)
- Views 2
- Comments 0