MOTIVATION
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.
RESULTS
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.
AVAILABILITY
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 INFORMATION
Supplementary data are available at Bioinformatics online.
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