--- title: RXNGraphormer canonical_url: "https://www.modelscope.cn/models/SAIS/RXNGraphormer" md_url: "https://www.modelscope.cn/models/SAIS/RXNGraphormer.md" repository: SAIS/RXNGraphormer chinese_name: RXNGraphormer last_updated: 2025-06-14 downloads: 0 stars: 0 --- # RXNGraphormer > RXNGraphormer - SAIS 在 ModelScope 开源的模型。Official implementation of "A unified pre-trained deep learning framework for cross-task reaction performance prediction and synthesis planning" (under review). Here, we provide a wiki document, which was… - **Repository**: SAIS/RXNGraphormer - **Downloads**: 0 - **Stars**: 0 - **Last updated**: 2025-06-14 Source: https://www.modelscope.cn/models/SAIS/RXNGraphormer --- # RXNGraphormer [![Ubuntu](https://img.shields.io/badge/Ubuntu-orange)](https://ubuntu.com/) [![Python 3.8](https://img.shields.io/badge/python-3.8-blue)](https://www.python.org/) [![PyTorch 1.12.1+cu113](https://img.shields.io/badge/PyTorch-1.12.1%2Bcu113-red)](https://pytorch.org/) [![PyG 2.3.1](https://img.shields.io/badge/torch__geometric-2.3.1-green)](https://pytorch-geometric.readthedocs.io/) [![RDKit](https://img.shields.io/badge/Chemoinformatics-RDKit-blueviolet)](https://www.rdkit.org/) [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT) Official implementation of *"A unified pre-trained deep learning framework for cross-task reaction performance prediction and synthesis planning"* (under review). Here, we provide a [wiki document](https://deepwiki.com/licheng-xu-echo/RXNGraphormer), which was auto-generated by DeepWiki based on `f1fe9f4` commit. ## 📌 Key Features - **Unified Architecture**: Integrates graph neural networks with Transformer to capture both intra and inter-molecular information. - **Pre-trained Model**: A pre-trained model that learns chemical bond transformation patterns from 13 million reactions. - **Cross-task Reaction Prediction**: Simultaneous handling of: - Yield, regioselectivity and enantioselectivity estimation (regression) - Forward- and retro-synthesis planning (sequence generation) - **Performance**: RXNGraphormer achieves state-of-the-art performance across eight benchmark datasets for reactivity/selectivity prediction and forward-/retro-synthesis planning, as well as three external realistic datasets for reactivity and selectivity prediction. ## 🚀 Quick Start ### Installation ```bash conda create -n rxngraphormer python=3.8 conda activate rxngraphormer # option 1: install pre-built package (recommended) pip install rxngraphormer -i https://pypi.org/simple -f https://data.pyg.org/whl/torch-1.12.0+cu113.html --extra-index-url https://download.pytorch.org/whl/cu113 # option 2: install from source (development mode) git clone https://github.com/licheng-xu-echo/RXNGraphormer.git cd RXNGraphormer/ pip install -r requirements.txt -f https://data.pyg.org/whl/torch-1.12.0+cu113.html --extra-index-url https://download.pytorch.org/whl/cu113 pip install . ``` **Note:** All codes were tested under Ubuntu 22.04.2 LTS ### Download model weights and dataset All model weights and preprocessed datasets are available via our [figshare repository](https://figshare.com/s/decc64a868ab64a93099). Please put these files in `model_path` and `dataset` folders, like the following structure: ``` RXNGraphormer |── rxngraphormer ├── model_path │ ├── buchwald_hartwig/ | ├── C_H_func/ | ├── pretrained_classification_model/ | ├── suzuki_miyaura/ │ ├── thiol_addition/ | ├── USPTO_50k/ | ├── USPTO_480k/ | └── ... |── dataset │ ├── 50k_with_rxn_type/ | ├── external_validation_dataset/ │ ├── OOS/ │ ├── pretrain/ │ ├── USPTO_50k/ │ ├── USPTO_480k/ │ ├── USPTO_full/ │ └── ... |── ... ``` ### Basic Usage **Reaction performance prediction** ```python3 from rxngraphormer.eval import reaction_prediction # reactivity prediction bh_model_path = "./model_path/buchwald_hartwig/seed0" rxn_smiles_lst = ["CC(C)c1cc(C(C)C)c(-c2ccccc2P(C2CCCCC2)C2CCCCC2)c(C(C)C)c1.CN(C)C(=NC(C)(C)C)N(C)C.Cc1ccc(N)cc1.FC(F)(F)c1ccc(I)cc1.c1ccc(-c2ccon2)cc1>>Cc1ccc(Nc2ccc(C(F)(F)F)cc2)cc1", "CC(C)c1cc(C(C)C)c(-c2ccccc2P(C2CCCCC2)C2CCCCC2)c(C(C)C)c1.CCOC(=O)c1cc(C)on1.CN(C)C(=NC(C)(C)C)N(C)C.Cc1ccc(N)cc1.FC(F)(F)c1ccc(Cl)cc1>>Cc1ccc(Nc2ccc(C(F)(F)F)cc2)cc1"] bh_react_preds = reaction_prediction(bh_model_path,rxn_smiles_lst,task_type="reactivity") # selectivity prediction thiol_add_model_path = "./model_path/thiol_addition/seed0" rxn_smiles_lst = ["COCc1cccc(-c2cc3c(c4c2OP(=O)(O)Oc2c(-c5cccc(COC)c5)cc5c(c2-4)CCCC5)CCCC3)c1.O=C(/N=C/c1ccccc1)c1ccccc1.Sc1ccccc1>>O=C(NC(Sc1ccccc1)c1ccccc1)c1ccccc1", "COCc1cccc(-c2cc3ccccc3c3c2OP(=O)(O)Oc2c(-c4cccc(COC)c4)cc4ccccc4c2-3)c1.O=C(/N=C/c1cccc2ccccc12)c1ccccc1.SC1CCCCC1>>O=C(NC(SC1CCCCC1)c1cccc2ccccc12)c1ccccc1"] thiol_add_sel_preds = reaction_prediction(thiol_add_model_path,rxn_smiles_lst,task_type="selectivity") # It takes about 25 seconds to predict 100 reactions on a single AMD 7735HS CPU without GPU. ``` **Reaction synthesis planning prediction** ```python3 from rxngraphormer.eval import reaction_prediction # retro-synthesis planning uspto_50k_model_path = "./model_path/USPTO_50k" pdt_smiles_lst = ['COC(=O)[C@H](CCCCN)NC(=O)Nc1cc(OC)cc(C(C)(C)C)c1O', 'O=C(Nc1cccc2cnccc12)c1cc([N+](=O)[O-])c(Sc2c(Cl)cncc2Cl)s1', 'CCN(CC)Cc1ccc(-c2nc(C)c(COc3ccc([C@H](CC(=O)N4C(=O)OC[C@@H]4Cc4ccccc4)c4ccon4)cc3)s2)cc1'] rct_preds = reaction_prediction(uspto_50k_model_path, pdt_smiles_lst, task_type="retro-synthesis") # forward-synthesis planning uspto_480k_model_path = "./model_path/USPTO_480k" rct_smiles_lst = ['C1CCOC1.CC(C)C[Mg+].CON(C)C(=O)c1ccc(O)nc1.[Cl-]', 'CN.O.O=C(O)c1ccc(Cl)c([N+](=O)[O-])c1', 'CCn1cc(C(=O)O)c(=O)c2cc(F)c(-c3ccc(N)cc3)cc21.O=CO'] pdt_preds = reaction_prediction(uspto_480k_model_path, rct_smiles_lst, task_type="forward-synthesis") # It takes about 40 seconds to predict 100 reactions on a single AMD 7735HS CPU without GPU. ``` **Reaction embeddings generation** ```python3 from rxngraphormer.rxn_emb import RXNEMB # base on pretrained model pretrain_model_path = "./model_path/pretrained_classification_model" rxnemb_calc_pretrained = RXNEMB(pretrained_model_path=pretrain_model_path,model_type="classifier") rxn_emb_pretrained = rxnemb_calc_pretrained.gen_rxn_emb(["C1CCCCC1.CCO.CS(=O)(=O)N1CCN(Cc2ccccc2)CC1.[OH-].[OH-].[Pd+2]>>CS(=O)(=O)N1CCNCC1", "CCOC(C)=O.Cc1cc([N+](=O)[O-])ccc1NC(=O)c1ccccc1.Cl[Sn]Cl.O.O.O=C([O-])O.[Na+]>>Cc1cc(N)ccc1NC(=O)c1ccccc1", "COc1ccc(-c2coc3ccc(-c4nnc(S)o4)cc23)cc1.COc1ccc(CCl)cc1F>>COc1ccc(-c2coc3ccc(-c4nnc(SCc5ccc(OC)c(F)c5)o4)cc23)cc1"]) # base on finetuned model finetune_model_path = "./model_path/buchwald_hartwig/seed0" rxnemb_calc_finetuneed = RXNEMB(pretrained_model_path=finetune_model_path,model_type="regressor") rxn_emb_finetuned = rxnemb_calc_finetuneed.gen_rxn_emb(["CC(C)c1cc(C(C)C)c(-c2ccccc2P(C2CCCCC2)C2CCCCC2)c(C(C)C)c1.CN(C)C(=NC(C)(C)C)N(C)C.Cc1ccc(N)cc1.FC(F)(F)c1ccc(I)cc1.c1ccc(-c2ccon2)cc1>>Cc1ccc(Nc2ccc(C(F)(F)F)cc2)cc1", "CCN=P(N=P(N(C)C)(N(C)C)N(C)C)(N(C)C)N(C)C.COc1ccc(OC)c(P([C@]23C[C@H]4C[C@H](C[C@H](C4)C2)C3)[C@]23C[C@H]4C[C@H](C[C@H](C4)C2)C3)c1-c1c(C(C)C)cc(C(C)C)cc1C(C)C.Cc1ccc(N)cc1.Ic1cccnc1.c1ccc(-c2ccon2)cc1>>Cc1ccc(Nc2cccnc2)cc1", "CC(C)c1cc(C(C)C)c(-c2ccccc2P(C2CCCCC2)C2CCCCC2)c(C(C)C)c1.CCc1ccc(I)cc1.CN1CCCN2CCCN=C12.Cc1ccc(N)cc1.c1ccc2oncc2c1>>CCc1ccc(Nc2ccc(C)cc2)cc1"]) ``` ## ⚛️ Model Training Although the script automatically preprocesses the data when you start the model training program, we recommend that you perform data preprocessing separately to avoid errors when using multi-gpu training models. Multi-gpu is usually used for model pretraining and fine-tuning for reaction synthesis planning tasks. In the pre-training task, multiple data sub-files are used to create training sets and validation sets. The `file_num_trunck` parameter in the json file can control how many sub-files are used. If it is set to `0`, all sub-files are used. ```bash python data_preprocess.py --config_json ./config/pretrain_parameters.json # preprocess pretraining data CUDA_VISIBLE_DEVICES="0,1" python -m torch.distributed.launch --nproc_per_node 2 train_model.py --config_json ./config/pretrain_parameters.json # pretrain model with multi-gpu python train_model.py --config_json ./config/C_H_func_parameters.json # finetune regression model python train_model.py --config_json ./config/uspto_50k_parameters.json # finetune sequence genration model ``` ## 🔢 Evaluation ```bash python eval_model.py --config_json ./config/buchwald_hartwig_eval.json # evaluate regression model python eval_model.py --config_json ./config/uspto_50k_eval.json # evaluate sequence generation model ``` ## 📑 Some results in paper Here we provide a [notebook](https://github.com/licheng-xu-echo/RXNGraphormer/blob/main/notebook/demo.ipynb) to show how to generate fictitious reactions based on real reactions, how to generate delta-mol SMILES based on reaction SMILES, how to evaluate the regression performance of RXNGraphormer, use the embeddings generated by pre-trained RXNGraphormer to measure the distance between different reaction types in USPTO-50k, and use the embeddings generated by fine-tuned RXNGraphormer to examine the model's understanding of reaction structure-performance relationship. ## 📚 Citation This paper is currently under review.