Graph-based Reaction Templates/Rules Extraction
This repository is dedicated to the systematic extraction of reaction rules from reaction databases. Our primary focus is the computational analysis and transformation of molecular reactions into a structured set of rules. This work facilitates a deeper understanding of reaction mechanisms and pathways. The SynTemp framework is organized into four main phases:
- AAMs Inference: Based on ensemble AAMs for accurate atom mapping.
- Imaginary Transition State (ITS) Completion: Enhances ITS by incorporating hydrogen inference to fully capture the reaction mechanism.
- Reaction Center Detection and Extension: Focuses on identifying and extending the core active sites of reactions.
- Hierarchical Clustering: Groups extended reaction centers or partial ITS to analyze reaction patterns.
The general framework and its components are depicted in Figures A, B, and C below.
- Templates Analysis: We have developed topological descriptors for ITS graphs to encapsulate the essential information of templates.
- Rules Application: Observes the trade-off between radii and coverage/novelty metrics. Increased coverage tends to reduce the number of output solutions due to the complexities of subgraph matching within the DPO framework. However, it also decreases novelty. This trade-off serves as a precursor to our forthcoming research, which will focus on developing a constrained framework for synthesis planning.
To install and set up the SynTemp framework, follow these steps. Please ensure you have Python 3.9 or later installed on your system.
- Python 3.11
- rdkit>=2023.9.5
- networkx>=3.3
- fgutils==0.0.17
- seaborn==0.13.2
- joblib==1.3.2
If you want to run ensemble AAMs
- dgl==2.1.0
- dgllife==0.3.2
- localmapper==0.1.3
- rxn-chem-utils==1.5.0
- rxn-utils==2.0.0
- rxnmapper==0.3.0
- chython==1.75
- chytorch==1.60
- chytorch-rxnmap==1.4
- torchdata==0.7.1
-
Python Installation: Ensure that Python 3.11 or later is installed on your system. You can download it from python.org.
-
Creating a Virtual Environment (Optional but Recommended): It's recommended to use a virtual environment to avoid conflicts with other projects or system-wide packages. Use the following commands to create and activate a virtual environment:
python -m venv syntemp-env
source syntemp-env/bin/activate # On Windows use `syntemp-env\Scripts\activate`Or Conda
conda create --name syntemp-env python=3.11
conda activate syntemp-env- Install from PyPi: The easiest way to use SynTemp is by installing the PyPI package syntemp.
pip install syntemp
Optional if you want to install full version
pip install syntemp[all]
- Verify Installation: After installation, you can verify that Syn Temp is correctly installed by running a simple test
echo -e "R-id,reaction\n0,COC(=O)[C@H](CCCCNC(=O)OCc1ccccc1)NC(=O)Nc1cc(OC)cc(C(C)(C)C)c1O>>COC(=O)[C@H](CCCCN)NC(=O)Nc1cc(OC)cc(C(C)(C)C)c1O" > test.csv
python -m syntemp --data_path test.csv --rebalancing --id 'R-id' --rsmi 'reaction' --rerun_aam --fix_hydrogen --log_file ./log.txt --save_dir ./from SynTemp.auto_template import AutoTemp
smiles = (
"COC(=O)[C@H](CCCCNC(=O)OCc1ccccc1)NC(=O)Nc1cc(OC)cc(C(C)(C)C)c1O>>"
+ "COC(=O)[C@H](CCCCN)NC(=O)Nc1cc(OC)cc(C(C)(C)C)c1O"
)
data = [{'R-id': '1', 'reactions': smiles}]
auto = AutoTemp(
rebalancing=True,
mapper_types=["rxn_mapper", "graphormer", "local_mapper"],
id="R-id",
rsmi="reactions",
n_jobs=1,
verbose=2,
batch_size=1,
job_timeout=None,
safe_mode=False,
save_dir=None,
fix_hydrogen=True,
)
(gml_rules, reaction_dicts, templates, hier_templates,
its_correct, uncertain_hydrogen,) = auto.temp_extract(data, lib_path=None)
print(gml_rules[0][0])
>> '''rule [
ruleID "0"
left [
edge [ source 1 target 2 label "-" ]
edge [ source 3 target 4 label "-" ]
]
context [
node [ id 1 label "N" ]
node [ id 2 label "C" ]
node [ id 3 label "O" ]
node [ id 4 label "H" ]
]
right [
edge [ source 1 target 4 label "-" ]
edge [ source 2 target 3 label "-" ]
]
]'''echo -e "R-id,reaction\n0,COC(=O)[C@H](CCCCNC(=O)OCc1ccccc1)NC(=O)Nc1cc(OC)cc(C(C)(C)C)c1O>>COC(=O)[C@H](CCCCN)NC(=O)Nc1cc(OC)cc(C(C)(C)C)c1O" > test.csv
python -m syntemp --data_path test.csv --rebalancing --id 'R-id' --rsmi 'reaction' --rerun_aam --fix_hydrogen --log_file ./log.txt --save_dir ./Run these commands from the root of the cloned repository.
python -m syntemp --data_path Data/USPTO_50K_original.csv --log_file Data/Test/log.txt --save_dir Data/Test/ --rebalancing --fix_hydrogen --rerun_aam --n_jobs 3 --batch_size 1000 --rsmi reactions --id IDSynTemp: Efficient Extraction of Graph-Based Reaction Rules from Large-Scale Reaction Databases
@Article{Phan2024,
author={Phan T-L, Weinbauer K, Gonzalez Laffitte ME, Pan Y, Merkle D, Andersen JL, et al},
title={SynTemp: Efficient Extraction of Graph-Based Reaction Rules from Large-Scale Reaction Databases},
journal={ChemRxiv},
year={2024},
doi={10.26434/chemrxiv-2024-tkm36},
url={https://chemrxiv.org/engage/chemrxiv/article-details/66f677b751558a15ef4cf5f7}
}
This project is licensed under MIT License - see the License file for details.
This project has received funding from the European Unions Horizon Europe Doctoral Network programme under the Marie-Skłodowska-Curie grant agreement No 101072930 (TACsy -- Training Alliance for Computational)