SMARTBind

This is the official repository for SMARTBind model. SMARTBind is a sequence-based RNA-ligand interaction prediction method by leveraging contrastive learning and RNA foundation model. It can be used for RNA-ligand interaction virtual screening and binding site prediction for the potential ligand binders.

Installation

git clone https://github.com/AIDD-LiLab/SMARTBind.git
cd SMARTBind

Install Required Packages

To install the required packages, run the following command:

# cuda 12.4 is required to satisfy the torch version
conda env create -f environment.yml
conda activate SMARTBind_env

Inference with SMARTBind

Downloaded pretrained SMARTBind models from Zenodo. An alternative way is to use gdown as follows if you are working in a server without browser access:

mkdir SMARTBind_weight
cd SMARTBind_weight
gdown --id 1z0PD0CRMAs1Q43g836JMzh0VFcAcoG-l
unzip SMARTBind_weight.zip
rm SMARTBind_weight.zip

Please refer to the notebook/README.md for the details of the inference with SMARTBind model using jupyter notebooks.

Acknowledgements

We thank the authors for making the following packages, software, and models open-sourced and easy to implement: RNA-FM, BioPython, ProDy, Open Babel, RDKit, RNA 3D Hub, DeepCoy, RNA3DB, MMseqs2, PyTorch Lightning, AutoDock, rDock.

Cite SMARTBind

@article {Jiang2025.09.24.678312,
	author = {Jiang, Shiyu and Taghavi, Amirhossein and Wang, Tenghui and Meyer, Samantha M. and Childs-Disney, Jessica L. and Li, Chenglong and Disney, Mattew D. and Li, Yanjun},
	title = {Small Molecule Approach to RNA Targeting Binder Discovery (SMARTBind) Using Deep Learning Without Structural Input},
	year = {2025},
	doi = {10.1101/2025.09.24.678312},
	publisher = {Cold Spring Harbor Laboratory},
	journal = {bioRxiv}
}