#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).
Create a new conda environment:
conda env create -f environment.yml
conda activate DRlinkerThe 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 can be started by running the training.sh script using ChEMBL dataset
The pre-trained models can be found on the checkpoints/ folder.
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 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.
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.
bash fine-tuning-test.sh