MolBART

The MolBART project aims to pre-train a BART transformer language model [1] on molecular SMILES strings [2] by optimising a de-noising objective. We hypothesise that pre-training will lead to improved generalisation, performance, training speed and validity on downstream fine-tuned tasks. We intend to test the pre-trained model on downstream tasks such as reaction prediction, retrosynthetic prediction, molecular optimisation and molecular property prediction.

Installation

The project requires the pysmilesutils library to be installed (see README in pysmilesutils). MolBART also requires RDKit (although this should be installed as part of the installation procedure for pysmilesutils). Finally, the remaining project requirements can be installed with pip using pip install -r requirements.txt.

Code

The codebase is broadly split into the following parts:

• Models
• Data helpers
• Tokenisation
• Decoding
• Scripts

Models

The models.py file contains a Pytorch Lightning implementation of the BART language model, as well as Pytorch Lightning implementations of models for downstream tasks.

Data Helpers

The dataset.py file contains a number of classes used to load, batch and process the data before it is passed to the model. 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, tokenise and tensorise 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.

Tokenisation

Our tokenise.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 tokeniser makes use of the SMILESTokenizer from the pysmilesutils library for tokenising SMILES into their constituent atoms.

Decoding

We include implementations of greedy and beam search decoding in the decode.py file. Both 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.

Scripts

The repository includes the following scripts:

• train.py runs the pre-training
• fine_tune.py runs fine-tuning on a specified task
• evaluate.py evaluates the performance of a fine-tuned model
• build_tokeniser.py creates a tokeniser from a dataset and stores it in a pickle file

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.

References

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

[2] 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.