This repository includes codes to run the model in paper

CLAIRE: A Contrastive Learning-based Predictor for EC number of chemical reactions

to predict EC numbers for chemical reactions.

1.Environment setup

In terminal

cd CLAIRE/
conda create -n claire python==3.10
conda activate claire
pip install -r requirements.txt

Install torch：You may install GPU or CPU version of torch.

conda install pytorch==1.11.0 cpuonly -c pytorch (CPU)
conda install pytorch==1.11.0 cudatoolkit=11.3 -c pytorch (GPU)

Run the following to install rxnfp:

bash rxnfp_env.sh

2.How to use

(1). Run DRFP embeddings

Suppose you have three query reactions to be predicted (shown below), saved in a txt file ("my_rxn_smiles.txt"). Note that multiple reactants and products are seaparated by "."; reactants and products are separated by ">>".

NC(=O)c1ccc[n+]([C@@H]2O[C@H](COP(=O)(O)OP(=O)(O)OC[C@H]3O[C@@H](n4cnc5c(N)ncnc54)[C@H](O)[C@@H]3O)[C@@H](O)[C@H]2O)c1.NCCC=O.O>>NCCC(=O)O
C=C(C)CCOP(=O)([O-])OP(=O)([O-])[O-].CC(C)=CCOP(=O)(O)OP(=O)(O)O>>CC(C)=CCCC(C)=CCCC(C)=CCCC(C)=CCCC(C)=CCCC(C)=CCCC(C)=CCCC(C)=CCCC(C)=CCOP(=O)(O)OP(=O)(O)O
N.NC(=O)C1=CN([C@@H]2O[C@H](COP(=O)(O)OP(=O)(O)OC[C@H]3O[C@@H](n4cnc5c(N)ncnc54)[C@H](OP(=O)(O)O)[C@@H]3O)[C@@H](O)[C@H]2O)C=CC1.O=C([O-])CCC(=O)C(=O)[O-].[H+]>>N[C@@H](CCC(=O)[O-])C(=O)[O-]

Activate the claire environment:

cd CLAIRE/
conda activate claire

Run the following command to obtain DRFP embeddings and save it in "my_rxn_fps.pkl"

drfp my_rxn_smiles.txt my_rxn_fps.pkl -d 256

where -d is the dimension of the embeddings

(2). Run rxnfp embeddings

In Python, import the relevant packages

from dev.prediction.inference_EC import infer_maxsep
import pickle
import numpy as np
import pandas as pd
from rxnfp.transformer_fingerprints import (
    RXNBERTFingerprintGenerator, get_default_model_and_tokenizer, generate_fingerprints
)

compute for the rxnfp embeddings

model, tokenizer = get_default_model_and_tokenizer()
rxnfp_generator = RXNBERTFingerprintGenerator(model, tokenizer)
example_rxns = ["NC(=O)c1ccc[n+]([C@@H]2O[C@H](COP(=O)(O)OP(=O)(O)OC[C@H]3O[C@@H](n4cnc5c(N)ncnc54)[C@H](O)[C@@H]3O)[C@@H](O)[C@H]2O)c1.NCCC=O.O>>NCCC(=O)O", "C=C(C)CCOP(=O)([O-])OP(=O)([O-])[O-].CC(C)=CCOP(=O)(O)OP(=O)(O)O>>CC(C)=CCCC(C)=CCCC(C)=CCCC(C)=CCCC(C)=CCCC(C)=CCCC(C)=CCCC(C)=CCCC(C)=CCOP(=O)(O)OP(=O)(O)O", "N.NC(=O)C1=CN([C@@H]2O[C@H](COP(=O)(O)OP(=O)(O)OC[C@H]3O[C@@H](n4cnc5c(N)ncnc54)[C@H](OP(=O)(O)O)[C@@H]3O)[C@@H](O)[C@H]2O)C=CC1.O=C([O-])CCC(=O)C(=O)[O-].[H+]>>N[C@@H](CCC(=O)[O-])C(=O)[O-]"]
rxnfp = rxnfp_generator.convert_batch(example_rxns)

(3). Concatenate the rxnfp and drfp embeddings

my_rxn_fps = pickle.load(open('my_rxn_fps.pkl', 'rb'))
test_data = []

for ind, item in enumerate(rxnfp):
    rxn_emb = np.concatenate((np.reshape(item, (1,256)), np.reshape(drfp[ind], (1,256))), axis=1)
    test_data.append(rxn_emb)

test_data = np.concatenate(test_data,axis=0)

(4). Make predictions on the concatenated embeddings

# EC calling results using maximum separation
result = infer_maxsep(train_data, test_data, train_labels, test_tags, test_labels, pretrained_model,out_filename='./dev/results/demo', gmm = './dev/GMM/gmm_ensumble.pkl')