SyntaLinker (Automatic Fragment Linking with Deep Conditional Transformer Neural Networks)

This is the code for the "SyntaLinker: Automatic Fragment Linking with Deep Conditional Transformer Neural Networks" paper found on ChemRxiv.

To implement our models we were based on OpenNMT-py (v0.4.1).

Install requirements

Create a new conda environment:

conda env create -f environment.yml
conda activate SyntaLinker

Pre-processing

The tokenized datasets can be found on the data/ folder.

For Input file generation, we the preprocess.sh script:

We use a shared vocabulary. The vocab_size and seq_length are chosen to include the whole datasets.

Training

The data has already been preprocessed for training the SyntaLinker

Model training can be started by running the training.sh script

To achieve the best results with single models, we average the last 10 checkpoints by running average_models.sh script.

Testing (beam search)

To generate the predictions use the translate.py script:

Model testing of beam search can be started by running the testing_beam_search.sh script.

Metrics

Run the the recovery.sh script to get the top-10 recovery. The other metrics, such as RMSD, 3D smiliarity in case study can be found in Utils/ folder.

Citation

If you find this work useful in your research, please consider citing the paper: "SyntaLinker: Automatic Fragment Linking with Deep Conditional Transformer Neural Networks".