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#DRlinker

This is the code for the "DRlinker: Deep Reinforcement Learning for optimization in fragment linking Design".

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 DRlinker

Pre-processing

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

We use the same datasets as SyntaLinker, the data was originated from the ChEMBL database.

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

Pre-training

Pre-training can be started by running the training.sh script using ChEMBL dataset

The pre-trained models can be found on the checkpoints/ folder.

Notice

Because the file is too large to upload, we will publish all the data and pre-trained models on MultCloud.

Please click the link to download and replace the original data/ and checkpoints/ folders.

Fine-tuning and Testing

fine-tuning script fine-tuning-test.sh can be run after pre-training using ChEMBL testing dataset or real drug case.

The final predictions can be found on the case/ folder.

RL (beam search & Multinomial sampling)

There are two strategies to sample for the fine-tuning and testing stages, training in beam search can use train_type as B and in multinomial sampling can use train_type as M.

In most of cases, multinomial sampling performs better because of its ability to explore larger chemical space.

The input of cases can refer to case/

To train the RL model use the train_agent_ms.py script:

Model training of beam search can be started by running the train_agent_ms.sh script.

Example

bash fine-tuning-test.sh