Chemical Reaction Extraction from Scientific Literature

This repository contains code/data for the JCIM paper: Automated Chemical Reaction Extraction from Scientific Literature.

Installation

Pre-requirements

1. pytorch (>=1.5.0)
2. transformers (tested on v3.0.2)
3. tqdm (>=4.36.0)
4. numpy (>=1.18.0)
5. seqeval

Install from source

1. git clone https://github.com/jiangfeng1124/ChemRxnExtractor
2. cd ChemRxnExtractor
3. pip install -r requirements.txt
4. pip install -e .

Download Trained Models

Download the trained models: cre_models_v0.1.tgz, and extract to the current directory:

tar zxvf cre_models_v0.1.tgz

Usage

Using RxnExtractor in your code:

from chemrxnextractor import RxnExtractor

model_dir="models" # directory saving both prod and role models
rxn_extractor = RxnExtractor(model_dir)

# test_file contains texts line by line
with open(test_file, "r") as f:
    sents = f.read().splitlines()
rxns = rxn_extractor.get_reactions(sents)

model_dir points to the directory of the trained models (e.g., cre_models_v0.1). test_file has an independent paragraph/sentence each line (e.g., tests/data/raw.txt). See pipeline.py for more details. GPU is used as the default device, please ensure that you have at least >5G allocatable GPU memory.

Preprocessing: We recommend using the ChemDataExtractor toolkit for the preprocessing of chemical documents in PDF format, such as PDF parsing, sentence segmentation, and tokenization.

Train and Evaluation

Pre-training: ChemBERT

Our model is greatly benefited from a domain-adaptively pre-trained model named ChemBERT. To train a new model on your own datasets, download ChemBERT v3.0, and extract to a local directory.

Update! ChemBERT is now installable directly within HuggingFace's framework via path jiangg/chembert_cased.

from transformers import *
tokenizer = AutoTokenizer.from_pretrained('jiangg/chembert_cased')
model = AutoModel.from_pretrained('jiangg/chembert_cased')

Fine-tuning

We provide scripts to train new models (product/role extraction) using our annotated data (located in tests/data/{task}/{train|dev|test}.txt) or your own data following the same format.

Data format

The training data should contain texts (sequences of tokens) and known target labels. We follow conventional BIO-tagging scheme, where B-{type} indicates the Beginning of a specific entity type (e.g., Prod, Reactants, Solvent), and I-{type} means the Inside of an entity.

Product Extraction

The train/dev/test files have the same CoNLL-style format:

#	passage=10.1021/ja00020a078-5	sentence=1
Reaction	O
of	O
diphenylacetylene	O
with	O
complex	O
19A	O
led	O
to	O
only	O
cycloheptadienone	B-Prod
23A	B-Prod
in	O
30	O
%	O
yield	O

It is assumed that the tokens are in the first column, and the targets are in the second column. The comment line (optional) can contain any meta information of the current text sequence, such as the DOI of a paper.

Reaction Role Extraction

Data files for role extraction can have multiple label columns, each corresponding to one product. For example:

#	passage=10.1021/ja00020a078-5	segment=1
Reaction	O	O	O
of	O	O	O
diphenylacetylene	B-Reactants	B-Reactants	B-Reactants
with	O	O	O
complex	O	O	O
19A	B-Reactants	B-Reactants	O
led	O	O	O
to	O	O	O
only	O	O	O
cycloheptadienone	B-Prod	O	O
23A	O	B-Prod	O
in	O	O	O
30	B-Yield	B-Yield	O
%	I-Yield	I-Yield	O
yield	O	O	O
;	O	O	O
with	O	O	O
(phenylcyclopropy1)-	O	O	O
carbene	O	O	O
complex	O	O	O
19B	O	O	B-Reactants
,	O	O	O
cycloheptadienone	O	O	O
25	O	O	B-Prod
was	O	O	O
produced	O	O	O
in	O	O	O
53	O	O	B-Yield
%	O	O	I-Yield
yield	O	O	O

The tokens are in the first column, and the target labels are in the remaining columns.

Run

To train or evaluate a product extraction model, run:

python train.py <task> <config_path>|<options>

where <task> is either "prod" or "role" depending on the task of interest, <config_path> is a json file containing required hyper-parameters such as the paths to the pre-trained model and the data; <options> are instead explicitly-specified hyper-parameters.

For example:

python train.py prod configs/prod_train_hf.json

Configure configs/prod_train.json to turn on/off the train/eval modes.

Performance

Performance of the provided trained models on our test set (tests/data/<task>/test.txt):

Task	Precision	Recall	F1
Product Extraction	84.62	69.37	76.24
Role Extraction	80.12	77.25	78.66

Predict

To generate predictions for unlabeled inputs (see tests/data/<task>/inputs.txt for the format of unlabeled inputs), run:

python predict.py <task> <config_json>

For example:

python predict.py prod configs/prod_predict.json

Contact

Please create an issue or email to [email protected] should you have any questions, comments or suggestions.