Chemformer

This repository contains the code used to generate the results in the Chemformer papers [1] [2].

The Chemformer project aimed to pre-train a BART transformer language model [3] on molecular SMILES strings [4] by optimising a de-noising objective. We hypothesized that pre-training would lead to improved generalisation, performance, training speed and validity on downstream fine-tuned tasks. The pre-trained model was tested on downstream tasks such as reaction prediction, retrosynthetic prediction, molecular optimisation and molecular property prediction in our original manuscript [1]. Our synthesis-prediction (seq2seq) Chemformer was evaluated for the purpose of single- and multi-step retrosynthesis [2].

The public models and datasets available here. To run these models with the new version, you first need to update the checkpoint, e.g.:

model = torch.load("model.ckpt")
model["hyper_parameters"]["vocabulary_size"] = model["hyper_parameters"].pop("vocab_size")
torch.save(model, "model_v2.ckpt")

Prerequisites

Before you begin, ensure you have met the following requirements:

• Linux, Windows or macOS platforms are supported - as long as the dependencies are supported on these platforms.

• You have installed anaconda or miniconda with python 3.7

Installation

First clone the repository using Git.

The project dependencies can be installed by executing the following commands in the root of the repository:

conda env create -f env.yaml
conda activate chemformer

If there is an error "ImportError: /lib64/libstdc++.so.6: version `GLIBCXX_3.4.21' not found" it can be mitigated by adding the 'lib' directory from the Conda environment to LD_LIBRARY_PATH

As example: export LD_LIBRARY_PATH=/path/to/your/conda/envs/chemformer/lib

Basic usage

The following is an example of how to fine tune Chemformer using the pre-trained models and datasets available here.

1. Create a Chemformer conda environment, as above.
2. Download the dataset of interest and store it locally (e.g. ../data/uspto_50.pickle).
3. Download a pre-trained Chemformer model and store it locally (e.g ../models/pre-trained/combined.ckpt).
4. Update the fine_tune.sh shell script in the example_scripts directory (or create your own) with the paths to your model and dataset, as well as the values of hyperparameters you wish to pass to the script.
5. Run the fine_tune.sh script.

You can of course run other scripts in the repository following a similar approach. The Scripts section below provides more details on what each script does.

Scripts

The molbart includes the following scripts:

• molbart/train.py runs the pre-training
• molbart/fine_tune.py runs fine-tuning on a specified task
• molbart/inference_score.py predicts SMILES and evaluates the performance of a fine-tuned model
• molbart/predict.py predict products given input reactants
• molbart/build_tokenizer.py creates a tokenizer from a dataset and stores it in a pickle file
• molbart/modules/retrosynthesis/round_trip_inference.py runs round-trip inference and scoring using the predicted SMILES from molbart/inference_score.py

Each script can be run using python -m molbart.<scipt_name> <args>.

See the ArgumentParser args in each file for more details on each argument.

Notes on running retrosynthesis predictions and round-trip validation

Example of running inference and calulcating (1) top-N accuracy (stored in metrics.csv) and (2) round-trip accuracy(stored in round_trip_metrics.csv):

1. python -m molbart.inference_score --data_path data.csv -o metrics.csv -os sampled_smiles.json --dataset_type synthesis <additional_args>
2. python -m molbart.modules.retrosynthesis.round_trip_inference -f data.csv -p sampled_smiles.json -o round_trip_metrics.csv -os round_trip_sampled_smiles.json <additional_args>

When running analysis using the --dataset_type synthesis option (SynthesisDataModule), the input file given by --data_path is assumed to be a tab-separated .csv file containing the columns products (SMILES), reactants (SMILES) and set (labels of each sample according to which dataset split it belongs to, i.e. "train", "val" or "test").

See the ArgumentParser args in each file for more details on each argument.

Notes on FastAPI service

Chemformer predictions and log-likelihood calculations can be executed with FastAPI.

Install FastAPI libraries

python -m pip install fastapi
python -m pip install "uvicorn[standard]"

Then

cd service
export CHEMFORMER_MODEL={PATH TO MODEL}
export CHEMFORMER_VOCAB={PATH TO VOCABULARY FILE}
export CHEMFORMER_TASK=backward_prediction
python chemformer_service.py

The model URL can for example be used to run multi-step retrosynthesis with AiZynthFinder

Code structure

The codebase is broadly split into the following parts:

• Models
• Modules, including utils, data helpers, decoders, etc.
• Scripts for running e.g. fine-tuning, prediction, etc.

Models

The models/transformer_models.py file contains a Pytorch Lightning implementation of the BART language model, as well as Pytorch Lightning implementations of models for downstream tasks. models/chemformer.py contains the synthesis prediction Chemformer model used for both forward and backward (seq2seq) predictions.

Modules

The modules/data folder contains DataModules for different tasks and datasets. The classes which inherit from _AbsDataset are subclasses of Pytorch's nn.utils.Dataset and are simply used to store and split data (molecules, reactions, etc) into its relevant subset (train, val, test). Our _AbsDataModule class inherits from Pytorch Lightning's LightningDataModule class, and its subclasses are used to augment, tokenize and tensorize the data before it passed to the model.

Finally, we include a TokenSampler class which categorises sequences into buckets based on their length, and is able to sample a different batch size of sequences from each bucket. This helps to ensure that the model sees approximately the same number of tokens on each batch, as well as dramatically improving training speed.

Tokenization

Our modules/tokenizer.py file includes the MolEncTokeniser class which is capable of random 'BERT-style' masking of tokens, as well as padding each batch of sequences to be the same length. The ChemformerTokenizer, which is used in the synthesis Chemformer makes use of the SMILESTokenizer from the pysmilesutils library for tokenising SMILES into their constituent atoms.

Decoding / sampling

We include implementations of greedy and beam search, as well as a GPU-optimized beam search decoding (BeamSearchSampler) in the modules/decoder.py file. All implementations make use of batch decoding for improved evaluation speeds. They do not, however, cache results from previous decodes, rather, they simply pass the entire sequence of tokens produced so far through the transformer decoder. The BeamSearchSampler is used by the synthesis Chemformer model in molbart.models.chemformer.

Contributing

We welcome contributions, in the form of issues or pull requests.

If you have a question or want to report a bug, please submit an issue.

To contribute with code to the project, follow these steps:

1. Fork this repository.
2. Create a branch: git checkout -b <branch_name>.
3. Make your changes and commit them: git commit -m '<commit_message>'
4. Push to the remote branch: git push
5. Create the pull request.

Please use black package for formatting.

The contributors have limited time for support questions, but please do not hesitate to submit an issue.

License

The software is licensed under the MIT license (see LICENSE file), and is free and provided as-is.

Cite our work

If you find our work useful for your research, please cite our paper(s):

[1] Irwin, R., Dimitriadis, S., He, J., Bjerrum, E.J., 2021. Chemformer: A Pre-Trained Transformer for Computational Chemistry. Mach. Learn. Sci. Technol. https://doi.org/10.1088/2632-2153/ac3ffb

[2] Westerlund, A.M., Manohar Koki, S., Kancharla, S., Tibo, A., Saigiridharan, L., Kabeshov, M., Mercado, R., Genheden, S., 2024. Do Chemformers Dream of Organic Matter? Evaluating a Transformer Model for Multistep Retrosynthesis, J. Chem. Inf. Model. [https://pubs.acs.org/doi/10.1021/acs.jcim.3c01685)

References

[3] Lewis, Mike, et al. "Bart: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension." arXiv preprint arXiv:1910.13461 (2019).

[4] Weininger, David. "SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules." Journal of chemical information and computer sciences 28.1 (1988): 31-36.