rxn-aa-mapper
Reactions SMILES-AA sequence mapping
setup
conda env create -f conda.yml
conda activate rxn_aa_mapper
In the following we consider on examples provided to show how to use RXNAAMapper.
generate a vocabulary to be used with the EnzymaticReactionBertTokenizer
Create a vocabulary compatible with the enzymatic reaction tokenizer:
create-enzymatic-reaction-vocabulary ./examples/data-samples/biochemical ./examples/token_75K_min_600_max_750_500K.json /tmp/vocabulary.txt "*.csv"
use the tokenizer
Using the examples vocabulary and AA tokenizer provided, we can observe the enzymatic reaction tokenizer in action:
from rxn_aa_mapper.tokenization import EnzymaticReactionBertTokenizer
tokenizer = EnzymaticReactionBertTokenizer(
vocabulary_file="./examples/vocabulary_token_75K_min_600_max_750_500K.txt",
aa_sequence_tokenizer_filepath="./examples/token_75K_min_600_max_750_500K.json"
)
tokenizer.tokenize("NC(=O)c1ccc[n+]([C@@H]2O[[email protected]](COP(=O)(O)OP(=O)(O)OC[[email protected]]3O[C@@H](n4cnc5c(N)ncnc54)[[email protected]](O)[C@@H]3O)[C@@H](O)[[email protected]]2O)c1.O=C([O-])CC(C(=O)[O-])C(O)C(=O)[O-]|AGGVKTVTLIPGDGIGPEISAAVMKIFDAAKAPIQANVRPCVSIEGYKFNEMYLDTVCLNIETACFATIKCSDFTEEICREVAENCKDIK>>O=C([O-])CCC(=O)C(=O)[O-]")
train the model
The mlm-trainer script can be used to train a model via MTL:
mlm-trainer \
./examples/data-samples/biochemical ./examples/data-samples/biochemical \ # just a sample, simply split data in a train and a validation folder
./examples/vocabulary_token_75K_min_600_max_750_500K.txt /tmp/mlm-trainer-log \
./examples/sample-config.json "*.csv" 1 \ # for a more realistic config see ./examples/config.json
./examples/data-samples/organic ./examples/data-samples/organic \ # just a sample, simply split data in a train and a validation folder
./examples/token_75K_min_600_max_750_500K.json
Checkpoints will be stored in the /tmp/mlm-trainer-log for later usage in identification of active sites.
Those can be turned into an HuggingFace model by simply running:
checkpoint-to-hf-model /path/to/model.ckpt /tmp/rxnaamapper-pretrained-model ./examples/vocabulary_token_75K_min_600_max_750_500K.txt ./examples/sample-config.json ./examples/token_75K_min_600_max_750_500K.json
predict active site
The trained model can used to map reactant atoms to AA sequence locations that potentially represent the active site.
from rxn_aa_mapper.aa_mapper import RXNAAMapper
config_mapper = {
"vocabulary_file": "./examples/vocabulary_token_75K_min_600_max_750_500K.txt",
"aa_sequence_tokenizer_filepath": "./examples/token_75K_min_600_max_750_500K.json",
"model_path": "/tmp/rxnaamapper-pretrained-model",
"head": 3,
"layers": [11],
"top_k": 1,
}
mapper = RXNAAMapper(config=config_mapper)
mapper.get_reactant_aa_sequence_attention_guided_maps(["NC(=O)c1ccc[n+]([C@@H]2O[[email protected]](COP(=O)(O)OP(=O)(O)OC[[email protected]]3O[C@@H](n4cnc5c(N)ncnc54)[[email protected]](O)[C@@H]3O)[C@@H](O)[[email protected]]2O)c1.O=C([O-])CC(C(=O)[O-])C(O)C(=O)[O-]|AGGVKTVTLIPGDGIGPEISAAVMKIFDAAKAPIQANVRPCVSIEGYKFNEMYLDTVCLNIETACFATIKCSDFTEEICREVAENCKDIK>>O=C([O-])CCC(=O)C(=O)[O-]"])
citation
@article{dassi2021identification,
title={Identification of Enzymatic Active Sites with Unsupervised Language Modeling},
author={Dassi, Lo{\"\i}c Kwate and Manica, Matteo and Probst, Daniel and Schwaller, Philippe and Teukam, Yves Gaetan Nana and Laino, Teodoro},
year={2021}
conference={AI for Science: Mind the Gaps at NeurIPS 2021, ELLIS Machine Learning for Molecule Discovery Workshop 2021}
}
24 Nov 03, 2022
13 Nov 17, 2022
123 Dec 30, 2022
54 Nov 21, 2022
791 Jan 06, 2023
4 Oct 08, 2022
124 Dec 28, 2022
42 Dec 27, 2022
1.4k Dec 22, 2022
93 Dec 19, 2022
391 Dec 29, 2022
78 Jan 07, 2023
19 Oct 19, 2022
19 Nov 05, 2022
23 Jan 25, 2022
140 Sep 26, 2022
228 Dec 25, 2022
58 Dec 19, 2022
172 Dec 14, 2022
254 Dec 27, 2022