syntemp 0.0.3

Graph-based reaction templates extraction

SynTemp

Graph-based Reaction Templates/Rules Extraction

Overview

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:

1. AAMs Inference: Based on ensemble AAMs for accurate atom mapping.
2. Imaginary Transition State (ITS) Completion: Enhances ITS by incorporating hydrogen inference to fully capture the reaction mechanism.
3. Reaction Center Detection and Extension: Focuses on identifying and extending the core active sites of reactions.
4. 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.

Downstream Applications

• 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.

Table of Contents

• Installation
• Usage
• Contributing
• Publication
• License
• Acknowledgments

Installation

To install and set up the SynTemp framework, follow these steps. Please ensure you have Python 3.9 or later installed on your system.

Prerequisites

• 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

Step-by-Step Installation Guide

1. Python Installation: Ensure that Python 3.11 or later is installed on your system. You can download it from python.org.

2. 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

3. Install from PyPi: The easiest way to use SynTemp is by installing the PyPI package syntemp.

pip install syntemp

4. 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 ./

Usage

Use in script

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,
    refinement_its=False,
)

(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 "-" ]
 ]
]'''

Use in command line

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 ./

Reproduce templates extraction

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 ID

Publication

SynTemp: Efficient Extraction of Graph-Based Reaction Rules from Large-Scale Reaction Databases

Citation

@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}
}

Contributing

• Tieu-Long Phan

License

This project is licensed under MIT License - see the License file for details.

Acknowledgments

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)