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BARTSmiles: Generative Masked Language Models for Molecular Representations

BARTSmiles is a chemical language model based on BART, trained on 1.7 billion SMILES strings from ZINC20 dataset.

BARTSmiles can be fine-tuned on chemical property prediction and generative tasks, including chemical reaction prediction and retrosynthesis. BARTSmiles allows to get multiple state-of-the-art results.

Hugging Face

You can use huggingface model from here.

Setup

Clone BARTSmiles repo in the root directory:

git clone https://github.com/YerevaNN/BARTSmiles.git

Setup a conda environment:

conda env create --file=./BARTSmiles/environment.yml
conda activate bartsmiles

Clone and install Fairseq in the root directory:

cd ./
git clone https://github.com/facebookresearch/fairseq.git
cd ./fairseq
pip install --editable ./

You need to add add_if_not_exist=False in this row:

tokens = self.task.source_dictionary.encode_line(bpe_sentence, append_eos=False, add_if_not_exist=False)

of this file: ./fairseq/fairseq/models/bart/hub_interface.py

NOTE! If you don't add this fairseq will be added new tokens in vocab of every unknown token instead of <unk>.

Download BARTSmiles pre-trained model and the vocabulary:

cd ./
mkdir -p ./chemical/tokenizer
cd ./chemical/tokenizer
wget http://public.storage.yerevann.com/BARTSmiles/chem.model
wget http://public.storage.yerevann.com/BARTSmiles/chem.vocab.fs

cd ./
mkdir ./chemical/checkpoints/evaluation_data
cd ./chemical/checkpoints
wget http://public.storage.yerevann.com/BARTSmiles/pretrained.pt
mv ./BARTSmiles/data_name ./chemical/checkpoints/evaluation_data
cd ./BARTSmiles/

Load the pretrained model

dict.txt is the vocab file without special tokens. You need to provide the structure of data_name directories.

from fairseq.models.bart import BARTModel

model = f"./chemical/checkpoints/evaluation_data/data_name/processed/input0"
bart = BARTModel.from_pretrained(model, checkpoint_file = f'./chemical/checkpoints/pretrained.pt',
                                 bpe="sentencepiece",
                                 sentencepiece_model=f"./chemical/tokenizer/chem.model")

Extract the features

Extract the last layer's features:

last_layer_features = bart.extract_features(bart.encode(smiles))

or you can use this file for batches:

python ./BARTSmiles/utils/extract_features.py --path [ the path where your BARTSmiles folder is located] --dataset-name esol --batch-size 32 --output-path [ where you want to locate the outputs]

Fine-tuning on MoleculeNet tasks

  1. Download and preprocess MoleculeNet datasets: Use the following command from the BARTSmiles folder:
python preprocess/process_datasets.py --dataset-name esol --is-MoleculeNet True --root [the path where your BARTSmiles folder is located]

This will create folders in ./chemical/checkpoints/evaluation_data/esol directory:

    esol
    │
    ├───esol
    │      train_esol.csv
    │      valid_esol.csv
    │      test_esol.csv
    │
    │
    ├───processed
    │   │
    │   ├───input0
    │   │       dict.txt
    │   │       preprocess.log
    │   │       test.bin
    │   │       train.bin
    │   │       valid.bin
    │   │       test.idx
    │   │       valid.idx
    │   │       train.idx
    │   │
    │   └───label
    │          dict.txt
    │          preprocess.log
    │          test.bin
    │          valid.bin
    │          train.bin
    │          test.idx
    │          valid.idx
    │          train.idx 
    │          test.label
    │          valid.label
    │          train.label
    │
    │
    ├───raw
    |      test.input
    |      test.target
    |      valid.input
    |      valid.target
    |      train.input
    |      train.target
    |   
    |
    |
    └───tokenized
        test.input
        valid.input
        train.input
  1. Generate the grid of training hyperparameters by running the script ./BARTSmiles/fine-tuning/generate_grid_bartsmiles.py. This will write grid search parameters in ./BARTSmiles/fine-tuning/grid_search.csv file.

Command for the regression tasks:

python fine-tuning/generate_grid_bartsmiles.py --root [the path where your BARTSmiles folder is located] --dataset-name esol --single-task True --dataset-size 1128 --is-Regression True

Command for the classification tasks having a single subtask:

python fine-tuning/generate_grid_bartsmiles.py --root [the path where your BARTSmiles folder is located] --dataset-name BBBP --single-task True --dataset-size 2039

Command for a specific subtask of a multilabel classification task:

python fine-tuning/generate_grid_bartsmiles.py --root [the path where your BARTSmiles folder is located] --dataset-name Tox21 --subtasks 12 --single-task False --dataset-size 7831

All required parameters for training are in grid_search.csv and you can start the training.

  1. Login to your wandb Befor start the training you have to login in wandb for tracking the trainings. For login you can follow: https://docs.wandb.ai/ref/cli/wandb-login

  2. Train the models using the following command:

mkdir ./chemical/log
python fine-tuning/train_grid_bartsmiles.py --root [the path where your BARTSmiles folder is located] --disk [the path where you want to store your checkpoints]  >> ./chemical/log/esol.log

This will produce a checkpoint in disk/clintox_1_bs_16_dropout_0.1_lr_5e-6_totalNum_739_warmup_118/ folder.

  1. You will write wandb urls in ./BARTSmiles/evaluation/wandb_url.csv file example:
url

gayanec/Fine_Tune_clintox_0/6p76cyzr
  1. Perform Stochastic Weight Averaging and evaluate from ./BARTSmiles/evaluation using the following command.
python evaluation/evaluate_swa_bartsmiles.py  --root [the path where your BARTSmiles folder is located] --disk [the path will your checkpoints be located] --dataset-type [dataset type: train, valid or test]

This will produce a log file with output and averaged checkpoints respectively in ./chemical/log/ and disk/clintox_1_bs_16_dropout_0.1_lr_5e-6_totalNum_739_warmup_118/ folders.

Note

If you want to fine-tune another dataset you have to add deatails in datasets.json files and your preprocessing code in ./preprocess/process_datasets.py file in line 103. The key must not contain the '_' symbol unless the following symbols are numbers.

Citation

@article{chilingaryan2022bartsmiles, title={Bartsmiles: Generative masked language models for molecular representations}, author={Chilingaryan, Gayane and Tamoyan, Hovhannes and Tevosyan, Ani and Babayan, Nelly and Khondkaryan, Lusine and Hambardzumyan, Karen and Navoyan, Zaven and Khachatrian, Hrant and Aghajanyan, Armen}, journal={arXiv preprint arXiv:2211.16349}, year={2022} }