This is the official implementation of the code related to the paper "Toward a Unified Benchmark and Framework for Deep Learning-Based Prediction of Nuclear Magnetic Resonance Chemical Shifts".
Authors: Fanjie Xu, Wentao Guo, Feng Wang, Lin Yao, Hongshuai Wang, Fujie Tang*, Zhifeng Gao*, Linfeng Zhang, Weinan E, Zhong-Qun Tian, Jun Cheng* (* are corresponding authors).
Our pre-training weights and datasets for all fine-tuning stages can be downloaded on zenodo. The online web app can be used NMR chemical shift prediction.
Four modules of the NMRNet framework:
- Data preparation, providing structure and NMR data.
- Pre-training, using pure structural information for self-supervised tasks, including masked atom prediction and 3D position recovery.
- Fine-tuning, for supervised NMR chemical shift prediction.
- Inference, where the fine-tuned NMRNet model parameters are frozen and applied to various tasks.
The installation steps for Linux systems are as follows:
pip install torch==1.13.1+cu116 torchvision==0.14.1+cu116 torchaudio==0.13.1 --extra-index-url https://download.pytorch.org/whl/cu116
pip install scikit-learn==1.3.2
pip install ase==3.22.1
pip install ./unicore-0.0.1+cu116torch1.12.0-cp38-cp38-linux_x86_64.whl
pip install pandas==2.0.3
Detailed installation tutorials for other versions of unicore package can be found at: Uni-Core.
First, you need to prepare your dataset for pre-training or fine-tuning in lmdb format and put it in data folder (you may refer to the demo as a reference).
Subsequently, please put the pre-trained weights into the weights folder (skip this step if re-training). Our pre-trained weights can be downloaded on zenodo.
Then, you can pre-train or fine-tune on your dataset. Here are demo scripts for pre-training and fine-tuning, please adjust the paths and hyperparameters according to your needs.
Pre-training in form of cutoff radius
sh script/pretrain_rcut.sh
Fine-tuning with 5-fold cross-validation
sh script/finetune_cv.sh
Details of the original Uni-Mol can be found in the paper.
Inference
A demo notebook can be found in the notebook folder.
An online service is available at ai4ec and bohrium.
Xu, F., Guo, W., Wang, F. et al. Toward a unified benchmark and framework for deep learning-based prediction of nuclear magnetic resonance chemical shifts. Nat Comput Sci 5, 292–300 (2025). https://doi.org/10.1038/s43588-025-00783-z
This project is licensed under the terms of the MIT license. See LICENSE for additional details.