ChEBai

ChEBai is a deep learning library designed for the integration of deep learning methods with chemical ontologies, particularly ChEBI. The library emphasizes the incorporation of the semantic qualities of the ontology into the learning process.

Note for developers

If you have used ChEBai before PR #39, the file structure in which your ChEBI-data is saved has changed. This means that datasets will be freshly generated. The data however is the same. If you want to keep the old data (including the old splits), you can use a migration script. It copies the old data to the new location for a specific ChEBI class (including chebi version and other parameters). The script can be called by specifying the data module from a config

python chebai/preprocessing/migration/chebi_data_migration.py migrate --datamodule=[path-to-data-config]

or by specifying the class name (e.g. ChEBIOver50) and arguments separately

python chebai/preprocessing/migration/chebi_data_migration.py migrate --class_name=[data-class] [--chebi_version=[version]]

The new dataset will by default generate random data splits (with a given seed). To reuse a fixed data split, you have to provide the path of the csv file generated during the migration: --data.init_args.splits_file_path=[path-to-processed_data]/splits.csv

Installation

To install ChEBai, follow these steps:

1. Clone the repository:

   git clone https://github.com/ChEB-AI/python-chebai.git

2. Install the package:

cd python-chebai
pip install .

Usage

The training and inference is abstracted using the Pytorch Lightning modules. Here are some CLI commands for the standard functionalities of pretraining, ontology extension, fine-tuning for toxicity and prediction. For further details, see the wiki. If you face any problems, please open a new issue.

Pretraining

python -m chebai fit --data.class_path=chebai.preprocessing.datasets.pubchem.PubchemChem --model=configs/model/electra-for-pretraining.yml --trainer=configs/training/pretraining_trainer.yml

Structure-based ontology extension

python -m chebai fit --trainer=configs/training/default_trainer.yml --model=configs/model/electra.yml  --model.pretrained_checkpoint=[path-to-pretrained-model] --model.load_prefix=generator. --data=[path-to-dataset-config] --model.out_dim=[number-of-labels]

A command with additional options may look like this:

python3 -m chebai fit --trainer=configs/training/default_trainer.yml --model=configs/model/electra.yml --model.train_metrics=configs/metrics/micro-macro-f1.yml --model.test_metrics=configs/metrics/micro-macro-f1.yml --model.val_metrics=configs/metrics/micro-macro-f1.yml --model.pretrained_checkpoint=electra_pretrained.ckpt --model.load_prefix=generator. --data=configs/data/chebi50.yml --model.out_dim=1446 --model.criterion=configs/loss/bce.yml --data.init_args.batch_size=10 --trainer.logger.init_args.name=chebi50_bce_unweighted --data.init_args.num_workers=9 --model.pass_loss_kwargs=false --data.init_args.chebi_version=231 --data.init_args.data_limit=1000

Fine-tuning for Toxicity prediction

python -m chebai fit --config=[path-to-your-tox21-config] --trainer.callbacks=configs/training/default_callbacks.yml  --model.pretrained_checkpoint=[path-to-pretrained-model]

Predicting classes given SMILES strings

python3 -m chebai predict_from_file --model=[path-to-model-config] --checkpoint_path=[path-to-model] --input_path={path-to-file-containing-smiles] [--classes_path=[path-to-classes-file]] [--save_to=[path-to-output]]

The input files should contain a list of line-separated SMILES strings. This generates a CSV file that contains the one row for each SMILES string and one column for each class. The classes_path is the path to the dataset's raw/classes.txt file that contains the relationship between model output and ChEBI-IDs.

Evaluation

An example for evaluating a model trained on the ontology extension task is given in tutorials/eval_model_basic.ipynb. It takes in the finetuned model as input for performing the evaluation.

Cross-validation

You can do inner k-fold cross-validation, i.e., train models on k train-validation splits that all use the same test set. For that, you need to specify the total_number of folds as

--data.init_args.inner_k_folds=K

and the fold to be used in the current optimisation run as

--data.init_args.fold_index=I

To train K models, you need to do K such calls, each with a different fold_index. On the first call with a given inner_k_folds, all folds will be created and stored in the data directory