Briefings in BioinformaticsBioinformaticsPeer-reviewed journal article
CoBRA: Compound Binding Site Prediction using RNA Language Model
Abstract
Ribonucleic acid (RNA) molecules perform a variety of functions within cells and thus implicated in various human diseases such as cancer. The fact that the proteins constitute a small portion of mRNAs and the ability of RNA to form highly specific three-dimensional binding pockets for small molecules have generated considerable interest in developing therapeutic agents that target RNAs as an alternative target for small-molecule drugs. Thus, like proteins, precise prediction of small-molecule binding sites across different classes of RNA targets could act as an starting point for drug discovery. In this study, we present a lightweight deep learning framework called Compound Binding Site Prediction for Ribonucleic Acid (CoBRA). This framework can predict ligand-binding nucleotides without requiring any explicit structural information. Our approach uses residue-level embeddings obtained from a pre-trained RNA language model. These embeddings encapsulate the contextual and statistical properties of each nucleotide and are used in a frozen state as the input for a multi-layer perceptron classifier performing binary classification of binding versus non-binding residues. The model was trained using combinations of ten distinct RNA language models and six different loss functions, using the TR60 and HARIBOSS datasets, and tested on four independent benchmark sets (RB9, JL10, TL12, and TE18). The performance of CoBRA demonstrates a relative improvement of 22.1% in Matthew correlation coefficient and an increase in sensitivity of 45.6% compared to existing state-of-the-art RNA- ligand binding site prediction methods with structural information. These results show that sequence-based language model embeddings alone, which do not require any explicit coordinate or distance information, can match or outperform structure-based methods. This makes it a flexible tool for predicting binding sites across diverse RNA targets. CoBRA is available at https://github.com/WonkyeongJang/CoBRA.
- Citations
- 3
- FWCI
- 1.54
- Field percentile
- top 15%
Reproducibility ledger
Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.
Sign in to file a reproducibility report.
Related papers
- LigRNA: High-accuracy prediction of RNA-ligand binding sites via dynamic chemical complementarity and language model encoding
Xin Wang, Heqin Zhu, … Peng Xiong·bioRxiv·Oct 17, 2026
- Adaptive 2.5D base-pairing subgraph search detects RNA small-molecule binding sites
David Nitchi, Jérôme Waldispühl, Carlos Oliver·bioRxiv·Jul 10, 2026
- Small Molecule Approach to RNA Targeting Binder Discovery (SMARTBind) Using Deep Learning Without Structural Input
Shiyu Jason Jiang, Amirhossein Taghavi, … Yanjun Li·bioRxiv·Sep 24, 2025
- GerNA-Bind: Geometric-enhanced RNA-ligand Binding Specificity Prediction with Deep Learning
Yunpeng Xia, Jiayi Li, … Shuangjia Zheng·bioRxiv·Feb 15, 2025
- Augmented base pairing networks encode RNA-small molecule binding preferences.
Carlos Oliver, Vincent Mallet, … Jérôme Waldispühl·Nucleic Acids ResearchPeer-reviewed·Jul 8, 2020
- DUET: a sequence-only ensemble for RNA-small molecule binding-site prediction
Kaifeng Xiong, Jianhua Jia·bioRxiv·Oct 30, 2026