Welcome to the chemical reaction compound atom-to-atom mapping project !!!
A chemical reaction can be defined as the transformation of a set of chemical compounds into another. Accompanied by energy change, the atoms of the reactant chemical compounds are rearranged to form the product chemical compounds. Therefore, correctly mapping this rearrangement of chemical compound atoms is essential for capturing the nature of the chemical reaction. This atom-to-atom mapping or atom mapping task has proven challenging as it is a generalization of the well-known subgraph isomorphism problem. Consequently, the main objective of this project is to systematically curate and facilitate access to relevant chemical reaction compound atom-to-atom mapping research resources.
A standalone environment can be created using the git and conda commands as follows:
git clone https://github.com/neo-chem-synth-wave/atom-to-atom-mapping.git
cd atom-to-atom-mapping
conda env create -f environment.yaml
conda activate atom-to-atom-mapping-envThe atom_to_atom_mapping package can be installed using the pip command as follows:
pip install --no-build-isolation -e .The purpose of the scripts directory is to illustrate how to run the atom-to-atom mapping using the following approaches:
- Indigo [1] by EPAM
- RXNMapper [2] by IBM RXN for Chemistry
- Chytorch RxnMap [3] by CIMM KFU
- LocalMapper [4] by MICC SNU
The map_reaction_smiles_using_indigo script can be utilized as follows:
# Example #1: Map a chemical reaction SMILES string using the Indigo approach.
python scripts/map_reaction_smiles_using_indigo.py \
--reaction_smiles "[O-][N+](=O)c1ccc(Br)cn1.CC(=O)Nc1ccc(O)cc1>>CC(=O)Nc1ccc(Oc2ccc(nc2)[N+]([O-])=O)cc1"
# Example #2: Map a chemical reaction SMILES dataset using the Indigo approach.
python scripts/map_reaction_smiles_using_indigo.py \
--input_csv_file_path "data/uspto_50k_by_20171116_coley_c_w_et_al.csv" \
--reaction_smiles_column_name "rxn_smiles" \
--output_csv_file_path "data/uspto_50k_by_20171116_coley_c_w_et_al_indigo.csv"The map_reaction_smiles_using_rxnmapper script can be utilized as follows:
# Example #1: Map a chemical reaction SMILES string using the RXNMapper approach.
python scripts/map_reaction_smiles_using_rxnmapper.py \
--reaction_smiles "COC(Cl)=O.Fc1cccc(COc2ccc3CCNCCc3c2)c1>>COC(=O)N1CCc2ccc(OCc3cccc(F)c3)cc2CC1"
# Example #2: Map a chemical reaction SMILES dataset using the RXNMapper approach.
python scripts/map_reaction_smiles_using_rxnmapper.py \
--input_csv_file_path "data/uspto_50k_by_20171116_coley_c_w_et_al.csv" \
--reaction_smiles_column_name "rxn_smiles" \
--output_csv_file_path "data/uspto_50k_by_20171116_coley_c_w_et_al_rxnmapper.csv"Please keep in mind that the execution device of the model at the center of the approach is determined automatically. Therefore, the visibility of the devices and subsequent batch size should be adjusted before executing the scripts.
The map_reaction_smiles_using_chytorch_rxnmap script can be utilized as follows:
# Example #1: Map a chemical reaction SMILES string using the Chytorch RxnMap approach.
python scripts/map_reaction_smiles_using_chytorch_rxnmap.py \
--reaction_smiles "OCN1C(=O)Cc2ccccc12.c1nc2ccccc2[nH]1>>O=C1Cc2ccccc2N1Cn1cnc2ccccc12"
# Example #2: Map a chemical reaction SMILES dataset using the Chytorch RxnMap approach.
python scripts/map_reaction_smiles_using_chytorch_rxnmap.py \
--input_csv_file_path "data/uspto_50k_by_20171116_coley_c_w_et_al.csv" \
--reaction_smiles_column_name "rxn_smiles" \
--output_csv_file_path "data/uspto_50k_by_20171116_coley_c_w_et_al_chytorch_rxnmap.csv"Please keep in mind that the execution device of the model at the center of the approach is set to the CPU by default, and the number of utilized CPU cores is not easily controllable.
The map_reaction_smiles_using_local_mapper script can be utilized as follows:
# Example #1: Map a chemical reaction SMILES string using the LocalMapper approach.
python scripts/map_reaction_smiles_using_local_mapper.py \
--reaction_smiles "CS(Cl)(=O)=O.OCCN1CCCc2cc(ccc12)C#N>>CS(=O)(=O)OCCN1CCCc2cc(ccc12)C#N"
# Example #2: Map a chemical reaction SMILES dataset using the LocalMapper approach.
python scripts/map_reaction_smiles_using_local_mapper.py \
--input_csv_file_path "data/uspto_50k_by_20171116_coley_c_w_et_al.csv" \
--reaction_smiles_column_name "rxn_smiles" \
--output_csv_file_path "data/uspto_50k_by_20171116_coley_c_w_et_al_local_mapper.csv"The contents of this repository are published under the MIT license. Please refer to individual references for more details regarding the license information of external resources utilized within this repository.
If you are interested in contributing to this repository by reporting bugs, suggesting improvements, or submitting feedback, feel free to use GitHub Issues.
[1] EPAM Indigo: https://lifescience.opensource.epam.com/indigo/index.html. Accessed on: July 26th, 2024.
[2] Schwaller, P., Hoover, B., Reymond, J., Strobelt, H., and Laino, T. Extraction of Organic Chemistry Grammar from Unsupervised Learning of Chemical Reactions. Sci. Adv., 7, eabe4166, 2021.
[3] Nugmanov, R., Dyubankova, N., Gedich, A., and Wegner, J.K. Bidirectional Graphormer for Reactivity Understanding: Neural Network Trained to Reaction Atom-to-atom Mapping Task. J. Chem. Inf. Model., 2022, 62, 14, 3307–3315.
[4] Chen, S., An, S., Babazade, R., and Jung, Y. Precise Atom-to-atom Mapping for Organic Reactions via Human-in-the-loop Machine Learning. Nat. Commun., 15, 2250, 2024.